Daily Digest — 2026-08-12
360 items · 6 research labs, 350 arxiv papers, 4 industry media
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🏛️ Research Labs (6)
Testing ads in ChatGPT
OpenAI has launched ChatGPT Ads in multiple markets, including the United Kingdom, Mexico, Brazil, Japan, and South Korea, following a pilot program initiated in the U.S. The ads are designed to support broader access to ChatGPT while maintaining user trust, privacy, and control. Ads are contextually matched to user conversations, clearly labeled, and do not influence ChatGPT's responses. Early results indicate no impact on consumer trust, low dismissal rates, and improved ad relevance. Businesses can participate by signing up through OpenAI’s advertiser portal, with ongoing plans to expand and refine the advertising program.
chatgpt adscontextual matchinguser privacyad relevancepilot program
How Zapier transformed core marketing processes with ChatGPT Work
Zapier's enterprise marketing team deployed ChatGPT Work to automate lead funnel optimization, campaign asset generation, and reporting. The system autonomously performs QA/QC on thousands of leads monthly, reducing manual processing time from 35-45 minutes per lead to near-instantaneous analysis. This intervention recovered seven figures in pipeline value monthly, while enabling real-time executive dashboards and campaign execution. The tool also freed creative capacity by handling repetitive tasks, compressing ideation-to-execution cycles. Future work aims to establish always-on feedback loops and a 'shared brain' integrating meeting context and market insights.
lead funnel optimizationautomated qa/qccampaign asset generationideation-to-execution cyclealways-on loops
Virgin Atlantic sharpens customer journeys with ChatGPT Work
Virgin Atlantic leverages ChatGPT Work to optimize customer journey analysis and decision-making processes. The airline employs ChatGPT to integrate disparate datasets, synthesize strategy documents, and accelerate competitive research across its digital product lifecycle. By creating structured frameworks and authenticated dashboards, teams reduced manual research from weeks to hours, enabling faster insights into customer experience patterns and investment opportunities. Custom product-planning tools further streamlined roadmap management and prioritization. This integration enhanced operational efficiency, enabling faster product delivery and improved end-to-end customer experiences.
chatgpt workcustomer journeystructured frameworkproduct lifecycleauthenticated dashboards
Thinking of ACE? We Can Do It with Fewer Tokens
ALTK-Evolve introduces a token-efficient alternative to ACE (Agentic Context Engineering) for LLM agent memory, maintaining accuracy while reducing inference costs. Both systems avoid compressing agent experience into summaries, instead preserving detailed lessons with provenance tracking. ALTK-Evolve dynamically retrieves task-relevant guidelines (40% of ACE's token cost on DeepSeek-V3.2, 1/7th on gpt-oss-120b), outperforming ACE's full-playbook approach on hard tasks (56.0 vs 54.8 TGC) while matching baseline performance.
agentic memoryin-context learningretrieval-augmented generationtask-specific retrievalsupport count
AMIE, our research medical AI system, demonstrates real-time clinical video consultation capabilities in a first-of-its-kind study.
Google Research and DeepMind present AMIE, a medical AI system demonstrating real-time video consultation capabilities through multi-agent architecture built on Gemini and Project Astra. The system interprets multimodal cues (visual/auditory), guides virtual exams, and performs diagnostic reasoning. In randomized simulated consultations with patient actors and primary care physicians, clinical evaluators rated AMIE favorably across core competencies (history-taking, diagnostic accuracy, management appropriateness, communication), with patients preferring video over text interactions. The study represents early research toward clinical deployment.
multimodal aiclinical consultationdiagnostic reasoningmulti-agent architecturegemini model
Our latest Google Finance upgrades, including a new app
Google Finance introduces a redesigned platform with AI-powered portfolio tracking and market analysis tools, now available via a new Android app. The system enables users to import investment data through multiple modalities (CSV/PDF uploads, screenshots, or natural language descriptions), then provides personalized insights via a conversational research interface (e.g., sector allocation analysis). Real-time notifications deliver scheduled briefings on user-specified market events, with iOS support planned for late 2026. The mobile app currently offers watchlist management, live news feeds, and stock movement explanations ('key moments'), with web features like earnings calls to be added incrementally.
portfolio trackingnatural language interfacereal-time notificationsmulti-modal inputmarket analysis
📜 arXiv Papers (350)
Beyond Naturalness: Probing Automated Text-To-Speech Evaluators on Linguistically Grounded Dimensions
The study introduces a linguistically grounded annotation schema decomposing TTS naturalness into 10 perceptual dimensions, creating a benchmark with 860 utterances annotated by linguists. It evaluates four MOS predictors and four Audio-LLM judges, finding MOS predictors primarily reflect acoustic signal quality while Audio-LLM judges exhibit prompt-dependent, non-generalizable detection across dimensions. Neither approach reliably captures linguistically structured speech errors, highlighting limitations in current automated TTS evaluation methods.
text-to-speechmean opinion scoreaudio-llmperceptual dimensionsmeta-evaluation
Multimodal Model Diffing for Feature Discovery and Control
The paper introduces MMDiff, a multimodal model-diffing framework that trains sparse autoencoders (SAEs) to discover and control features in Multimodal Large Language Models (MLLMs). The method enables (i) feature isolation by comparing base-LM and multimodal-adapted SAEs, (ii) task-specific feature detection via contrastive firing analysis, and (iii) feature-level control through directional removal or steering. Evaluated on LLaVA-MORE, PaliGemma 2, and InternVL3.5, MMDiff achieves 12-17% degradation on spatial/OCR tasks, 24% reduction in attack success rate, and +1.8-3.6% accuracy gains via feature steering, without compromising VQA performance.
multimodal large language modelssparse autoencodersfeature isolationcontrastive firing analysismodel-diffing
From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch
The 'Grip on LLMs' framework introduces a systematic evaluation suite for Dutch governmental use, addressing the gap in frameworks that jointly consider public administration values and non-English linguistic requirements. Developed with domain experts from a Dutch municipal organization, the framework identifies six evaluation dimensions—factuality, honesty, social bias, energy consumption, cost, and training data transparency—and operationalizes them into a benchmark suite covering over 30 multilingual and Dutch-specific models. Results indicate no single model excels across all dimensions, revealing trade-offs between quality, environmental impact, and financial cost, with bias remaining independent of these factors. Factuality and honesty are governed by distinct properties, with high factuality not implying high honesty. A user-friendly model overview is released for stakeholders.
factualityhonestysocial biasenergy consumptiontraining data transparency
GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis
The authors present GENCO, a unified neural solver for steady-state grid analysis that handles power flow, optimal power flow, and state estimation within a single architecture. The system is supported by the open-source GridFM Development Framework for standardized data generation and training, along with large-scale synthetic datasets. Evaluations on PFDelta, OPFData, and Hydro-Québec SCADA show GENCO achieves 30x speedups over Newton-Raphson for power flow, 85x speedups over IPOPT for optimal power flow, and improved robustness in state estimation compared to classical methods.
neural solverpower flowoptimal power flowstate estimationgrid foundation models
DSLE: A Learning Environment for Dark Souls Boss Encounters
The Dark Souls Learning Environment (DSLE) introduces a containerized benchmark platform featuring all 22 boss encounters from Dark Souls: Remastered, accessible via a Gymnasium-style interface. DSLE-5, a curated subset of five diverse boss fights, serves as a recommended starting suite for agent evaluation. Evaluations of random policies, expert systems, evolutionary baselines, and PPO/DQN agents reveal limited success: only the tutorial boss (Asylum Demon) is defeated (63% and 43% win rates by expert and evolutionary methods), while other bosses remain unbeaten, with RL agents showing negligible learning (≤0.33% win rates). Extended tests under advantaged conditions yield minimal additional progress.
reinforcement learningbenchmark environmentgame aisparse rewardshigh-dimensional input
Fusion Training for Mathematical Generalization in Large Language Models
This work systematically investigates Thinking Mode Fusion (TMF) training dynamics for mathematical generalization in large language models, focusing on data ratios and training schedules between thinking and non-thinking modes. Through a constructed benchmark with varied thinking-to-non-thinking data ratios and three training schedules, the study reveals an asymmetric interaction: increased non-thinking supervision reduces thinking mode accuracy. Different training schedules modulate this trade-off, with optimal schedules dependent on data ratios. A negative correlation between non-thinking and thinking mode supervision highlights inherent tension. Practical guidance for TMF training design is provided, with code and data released for further research.
thinking mode fusiontraining dynamicsmathematical generalizationdata ratiotraining schedule
BDH-CQ: In-Context Learning with Recurrent Latent Reasoning
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SHE: Trajectory-driven Safety Harness Evolution for LLM Agents
The paper introduces Safety Harness Evolution (SHE), a framework for evolving safety mechanisms in LLM agent harnesses by learning from rollout trajectories. SHE decomposes the harness into four artifacts—System Prompt, Rule Bank, Safety Memory, and Tool Policy—each with explicit safety responsibilities, enabling localized evolution. An attribution-guided evolution loop converts trajectory failures into structured diagnoses, refines artifact-specific boundaries, and selects evolved harnesses via safety-utility validation. Experiments on Agent-SafetyBench show SHE reduces ASR by 3.1x compared to static SafeHarness while improving benign utility. The evolved harness generalizes to unseen risks on AgentHarm and transfers across agent models without additional evolution.
safety harnessllm agentsrollout trajectoriesattribution-guided evolutionagent-safetybench
Energy-Structured Latent World Models with Neural Time Fields for Physically Constistent Open-World Motion Planning
The paper introduces an Energy-Structured Latent World Model (ELWM) that explicitly encodes energy and momentum in its latent state to ensure physically consistent motion predictions. The method integrates ELWM with Physics-Conditioned Neural Time Fields (PC-NTF), leveraging the Eikonal equation for navigation policy generation. Evaluations show PC-NTF reduces 0.8-s motion-prediction NRMSE from 0.36 to 0.29, improves navigation success from 81.3% to 89.7%, and lowers collision rates from 12.1% to 5.8% compared to baselines.
latent world modelneural time fieldseikonal equationmotion planningphysical consistency
ArchAgent v2: A Case Study with the Data Prefetching Championship
ArchAgent v2 introduces a scalable framework for automated microarchitecture discovery in multi-level data prefetching, addressing challenges of vast search spaces and hardware constraints. The framework employs cascaded evolutionary search, sequentially evolving prefetchers at individual cache levels, and integrates hardware-realizability feedback loops for real-time size estimation. Evaluated under Data Prefetching Championship (DPC4) rules, ArchAgent v2 designs a three-level prefetcher achieving a 3.8% geometric mean IPC speedup over the baseline and a 0.3% improvement over BertiGO, with notable gains in low-bandwidth single-core configurations. Despite its success, multi-core evolution remains challenging due to simulation latency. Profiling of over 12,000 candidate designs offers insights into automated evolutionary exploration of microarchitectural logic.
microarchitecture discoverydata prefetchingcascaded evolutionary searchhardware-realizability feedbacksimulation latency
Sci-VBench: Evaluating Knowledge- and Reasoning-Intensive Video Generation in Science Domains
Sci-VBench introduces a benchmark for evaluating knowledge- and reasoning-intensive video generation in scientific domains, featuring 1,253 expert-annotated examples across 60 subjects in four disciplines. The benchmark employs a rubric-based protocol, demonstrating high agreement between non-expert human evaluators, MLLM-as-Judge systems, and expert judgments. Evaluation of 16 proprietary and open-source models reveals significant performance gaps in Prompt Grounding and Scientific and Causal Correctness, despite comparable perceptual-quality scores, highlighting unmet challenges in modeling scientific dynamics.
video generationscientific reasoningbenchmark evaluationknowledge-grounded synthesiscausal correctness
Stealing Reasoning Traces from Proprietary LLM APIs
The authors identify an architectural vulnerability in proprietary LLM APIs where encrypted reasoning traces (chain-of-thought) are interchangeable across sessions, enabling a decryption jailbreak. By injecting these traces into weaker models from the same provider, they demonstrate four attack vectors: (1) circumventing anti-distillation to extract proprietary reasoning (tested on Anthropic, OpenAI, Google), (2) extracting 367 PII artifacts and 182 credentials from 315,320 public blocks, (3) revealing hidden hazardous reasoning, and (4) executing invisible prompt injections. They propose cryptographic and system-level mitigations.
chain-of-thoughtanti-distillationreasoning tracesprompt injectionpii extraction
Towards Expert-level Medical AI for Real-time Video Consultations
The study introduces AMIE (Video), a Gemini-based multi-agent AI system achieving expert-level performance in real-time clinical video consultations. AMIE integrates low-latency dialogue, clinical reasoning, and real-time audio-visual perception, guided by a taxonomy and automated evaluations for clinical audio-visual cues. In a randomized Objective Structured Clinical Examination (OSCE) study involving 30 primary care physicians, 15 patient actors, and 100 clinical scenarios, AMIE (Video) matched or exceeded physician performance in history-taking, diagnosis, management, and physical observation. Patient actors preferred AMIE for condition assessment and explanation, though physicians were favored for rapport building. Limitations include anatomical precision, affective nuances, and high-frequency movements.
audio-visual interactionlow-latency dialogueclinical reasoningobjective structured clinical examinationmulti-agent system
Agentic Harnesses: LLM-Driven Verification Layers for Robot Autonomy
The authors propose an LLM-driven verification layer for robot autonomy systems to address risks in action planning, including misalignment with ethics, safety vulnerabilities, and adversarial attacks. The system employs an LLM-as-a-Judge ensemble combining chain-of-thought reasoning and expert synthesis, functioning as middleware between planning and execution modules. It evaluates action permissibility, gating plans for approval, rejection, or human escalation. Results demonstrate 85% precision in accept/escalate/reject decisions, 97% containment of adversarial attacks, and minimal errors in task acceptance/rejection, with most errors occurring at the escalate boundary.
llm-driven verificationrobot autonomychain-of-thought reasoningaction permissibilityadversarial attacks
Agentic Auto-Research is Fuzz Testing
The paper argues that autonomous research agents should adopt greybox fuzzing principles to address sparse feedback in the generate-and-rank paradigm. It proposes two key capabilities: (1) exposing cheap, dense signals of epistemic progress before final validation, and (2) using these signals to guide search rather than repeated sampling. The authors suggest controlled tests to evaluate whether candidate signals predict validated progress, whether feedback-directed search improves discovery efficiency, and whether protected validation reduces false discoveries. They identify feedback architecture as a central bottleneck in auto-research.
autonomous research agentsgreybox fuzzingepistemic progressfeedback-directed searchprotected validation
CEAA: A Cognitive Embodied Agents Architecture for Interactive Computing Systems
The paper proposes CEAA, a modular cognitive architecture for embodied Intelligent Virtual Agents (IVAs) that bridges high-level reasoning with real-time execution in interactive 3D environments. It integrates the Sense-Think-Act paradigm and Belief-Desire-Intention model to enable scalable, adaptive, and explainable agents. The framework addresses limitations of existing approaches constrained by game engines or impractical reasoning models, offering a reusable template for IVA deployment.
intelligent virtual agentscognitive architecturebelief-desire-intentionsense-think-actinteractive 3d environments
Mismatch Matters: On-Policy Distillation Beyond Token Agreement
We introduce TIDE (Token-level Independent Deficit-Excess correction), a method addressing degenerate agreement in on-policy distillation (OPD) by focusing on teacher-student mismatch. TIDE employs bounded Hellinger shaping to suppress student-excess tokens and analytic teacher top-$K$ injection to restore student-deficit tokens, enabling stable updates and reasoning pattern transfer. Evaluated on mathematical reasoning benchmarks with Qwen3 teacher-student pairs, TIDE outperforms standard OPD and recent baselines, improving Avg@8 from 6.9% to 20.3%, reducing response length by 3.6×, and mitigating formatting failures.
on-policy distillationtoken agreementteacher-student mismatchhellinger shapingtop-k injection
Multi-Agent AI Safety as an Institutional Design Problem
This paper investigates institutional design for AI safety in multi-agent systems, focusing on how deployment rules and governance structures affect collective behavior. The study employs a frozen 5,280-episode suite across four model families, examining delegation workflows, constitutional prompts, and provenance-aware executable guards. Results show that a detailed constitutional prompt achieves 0/384 violations, while a provenance-aware guard blocks prohibited attempts in 51/384 episodes, with 44/51 completing safely. Local-state guards fail in scenarios where visible policy changes while authority remains fixed, admitting violations in 22/96 episodes. Resource-allocation experiments reveal that revealing numerical caps alters agent requests, highlighting the importance of authority states and post-block paths.
multi-agent systemsdelegation workflowsprovenance-aware guardsconstitutional promptsresource-allocation
Distill Skills into Weights, Not Prompts: Abstract Skills as Privileged Signals for On-Policy Self-Distillation
The paper introduces SKALD (Skill-Anchored Latent Distillation), an on-policy self-distillation framework that transfers abstract skill knowledge from a teacher model (Qwen3-Base conditioned on explicit-answer-filtered skill cards) to a student model (same architecture but question-only). To address context-induced distribution mismatch, SKALD uses an annealed exponentially tilted objective that downweights low-likelihood teacher tokens, converging to teacher cross-entropy. Empirical results show SKALD improves average performance by +2.46 to +12.01 points across five mathematics benchmarks at 0.6B-4B scales, outperforming GRPO and contextual skill exposure while maintaining 84.7% gains with zero-variance distillation.
self-distillationon-policy learningexponentially tilted objectivecontext-induced mismatchprivileged signals
MedPixel: A Unified Pixel-Language Model for Medical Reasoning and Segmentation
MedPixel introduces a unified pixel-language model for medical reasoning and segmentation, addressing the supervision mismatch between vision-language models and segmenters. The method combines joint multi-task supervised fine-tuning with Pixel-Level Preference Optimization, using ground-truth masks as verifiers, and leverages MedPLG-440K, a synthesized dataset of 440K pixel-language samples. Results demonstrate strong performance across explicit grounding, implicit reasoning, spatial interaction, grounded explanation, and medical VQA, with effective zero-shot transfer and robustness to imperfect spatial prompts.
pixel-language modelmedical reasoningpixel-level preference optimizationzero-shot transfervision-language segmentation
Parameter Exploration for RLVR via Variational Learning
This paper introduces Perturbed Parameter Policy Optimization (3PO), a family of methods for parameter-space exploration in LLM reinforcement learning. Unlike action-space exploration techniques like temperature scaling, 3PO generates rollouts by sampling diverse policies from a posterior distribution, enabling broader exploration and reducing training divergence. Experiments on OLMo-3-1025-7B and Qwen2.5-Math-7B across mathematical reasoning and code generation tasks demonstrate that 3PO improves downstream performance over standard GRPO at comparable computational cost, while reducing zero-advantage groups and malformed rollouts. The results suggest parameter-space exploration as an effective approach for LLM reinforcement learning.
parameter-space explorationperturbed parameter policy optimizationrollout generationposterior samplingzero-advantage groups
Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localization
This work demonstrates that modern backbone architectures significantly enhance multi-task DETR frameworks for mammography, improving both classification and lesion localization. The study evaluates a shared representation approach for joint exam-level malignancy prediction and candidate-region localization, comparing various backbones on OPTIMAM and SGM1k datasets. ConvNeXtV2 achieved state-of-the-art performance on OPTIMAM with 97.96% AUC, 99.89% sensitivity, 25.08% mAP@.5, and 74.38% recall@.25, while DINOv2 performed best on SGM1k with 90.97% AUC, 86.28% sensitivity, and 27.04% mAP@.5. Results indicate backbone selection critically impacts multi-task mammography performance, with ConvNeXtV2 emerging as particularly effective.
detrmammographyconvnextv2dinov3lesion localization
CARD: Controlled Agentic Reddit Discussions for Credit Card Simulation
CARD introduces a framework for generating realistic credit card discussion threads on Reddit by combining non-verbatim guidance on reply structure, comment function, stance, and tone with a planner-writer-calibration architecture. The method uses a calibration loop to update comment populations, minimizing distributional differences between generated and real threads. Evaluations on lexical, semantic, behavioral, and structural metrics show CARD outperforms baselines in matching real discussion distributions across multiple LLMs, with smaller effect sizes and distribution distances.
agentic simulationnon-verbatim guidancedistributional calibrationconversational variationbehavioral metrics
KGCaRe: Explainable Complex Conditional Question Answering using Automatic Knowledge Graph Construction and Context Retrieval with LLMs
KGCaRe introduces a hybrid approach combining neural retrieval with symbolic reasoning over LLM-generated knowledge graphs (KGs) to address complex conditional question answering. The method constructs KGs from documents using multi-prompt extraction, stores them in a graph database, and embeds documents into a vector store for neural retrieval. It employs iterative graph traversal guided by LLMs to extract relevant triples and prune irrelevant information, integrating these with semantically retrieved text passages into custom prompts for answer generation. Evaluated on two complex conditional QA datasets, KGCaRe outperforms baselines including Vanilla LLM, Code Prompt, and HybridContextQA across multiple LLMs like Mistral, Mixtral, GPT-3.5, and GPT-4o.
knowledge graphneural retrievalsymbolic reasoningmulti-prompt extractiongraph traversal
AirFlow: Context Preserving and Multi-Rate State Modeling for Air Quality Forecasting
AirFlow introduces a pollutant-aware dual-stream framework for air quality forecasting, addressing limitations in handling channel-specific distributions and multi-rate changes. The method employs a statistic-guided normalization routing mechanism that selects normalization paths based on 24-hour autocorrelation and distribution drift, alongside a hierarchical dual-stream state model combining multi-scale state space propagation with gated bidirectional cross-attention. Evaluated on real-world data from multiple cities, AirFlow outperforms state-of-the-art baselines in 34 of 36 metrics, reducing root mean square error by up to 11.11%, while maintaining low computational overhead with 0.0483M parameters and 0.0215G FLOPs.
normalization routingdual-stream modelstate space propagationcross-attentiondistribution drift
Cultivar: A Contrastive and Locale-Oriented Translation Benchmark for Investigating Contamination and Localisation Robustness
The paper introduces Cultivar, a contrastive translation benchmark designed to evaluate locale-specific translation quality and detect data contamination. Cultivar extends FLORES by incorporating localized content pairs, enabling source-contrastive evaluation across 32 open-weight models. Results reveal that MT-specialized models exhibit lower robustness, potential FLORES overfitting in some cases, and a consistent bias toward US-localized content regardless of language.
translation benchmarklocalisation robustnessdata contaminationsource-contrastive evaluationopen-weight models
MoNo: Multiscale Optimal Transport Neural Operator for Solving PDEs on General Geometries
MoNo introduces a multiscale neural operator for solving PDEs on general geometries, addressing assignment imbalance in latent-space projections of transformer-based neural operators. The core innovation is CoTAP, a method formulating cross-space assignment as an entropy-regularized optimal transport problem, ensuring balanced bidirectional projections and stable latent spaces. This enables efficient multiscale architectures and stable information transfer across latent spaces, improving long-range physical interaction learning. Experiments show MoNo outperforms state-of-the-art neural operators in prediction accuracy and computational efficiency. Code is publicly available.
neural operatoroptimal transportlatent-space projectionpartial differential equationsmultiscale architecture
Second-Order Muon Done Right: A Principled Marriage of Spectral Geometry and Curvature
GO-MUON introduces a principled optimization method combining spectral geometry and curvature, leveraging a matched data-dependent geometry reused across optimization steps. The method ensures exact updates for weighted spectral oracles, conditioned on positive-definite left and right maps, regardless of estimation or refresh timing. For softmax cross-entropy, it quantifies conditions where backward factors approximate the model Fisher and generalized Gauss--Newton factors. Results demonstrate that a four-step refresh maintains tracking delay for slowly changing geometry while increasing stationary factor noise, framing lazy geometry as a compute-statistics tradeoff.
spectral geometrycurvaturesoftmax cross-entropygeneralized gauss-newtonoptimization
Defining Decentralization: An Ontological Perspective
The paper introduces a graph-based ontological framework to resolve the 'Decentralization Problem', defined as the lack of a universal, domain-independent definition of decentralization in computer communication systems. The method combines formal-semantic, epistemological, and pragmatic analyses to distinguish decentralization from distribution, proposing two novel metrics—Void Tolerance and Imperviousness—for evaluation. A browser-based implementation demonstrates consistent assessments in federated learning and blockchain architectures, addressing inconsistencies in prior definitions and enabling cross-system comparability.
decentralizationontologygraph-based frameworkvoid toleranceimperviousness
SR-OPSD: Self-Referenced On-Policy Self-Distillation
SR-OPSD introduces self-referenced on-policy self-distillation to stabilize optimization in reinforcement learning by decoupling adaptive target placement from projection geometry. The method employs a token-level variational characterization to define the distillation target as a geometric interpolation between the self-teacher policy and a reference policy, while generalizing projection geometry using the Rényi divergence family. Experiments across scientific evaluation, mathematical reasoning, and coding generation tasks demonstrate state-of-the-art or competitive performance with multiple large language models.
self-distillationreinforcement learningrényi divergencevariational characterizationtoken-level supervision
Rethinking Factor Sharing in Federated LoRA: A Rank-Aware Adaptive Approach
The paper introduces Federated Adaptive Factor Sharing Low-Rank Adaptation (FedAS-LoRA), a method optimizing factor sharing in federated LoRA fine-tuning by selecting between Share-A/Local-B or Share-B/Local-A strategies based on projection residuals. A Rank-Aware Shared-Subspace Sufficiency (RSS) metric pre-selects the sharing strategy by evaluating shared rank-r subspace adequacy using frozen LLM representations. Experiments across varied tasks, data distributions, and LoRA ranks demonstrate FedAS-LoRA's superior performance over fixed sharing approaches.
federated learninglow-rank adaptationfactor sharingsubspace sufficiencyprojection residuals
ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners
ColluSkill introduces a novel adversarial framework for evading agent skill scanners by decomposing malicious intents into interdependent sub-payloads across multiple skills, exploiting cross-skill composition vulnerabilities. The method employs LLM-based chain planning and scanner-feedback refinement to maintain attack semantics while minimizing individual skill suspiciousness. Experiments demonstrate ColluSkill's 96.0% attack success rate against six skill scanners, significantly outperforming single-skill and multi-skill baselines. ChainGuard, a context-aware skill-chain scanner, mitigates this threat by reconstructing cross-skill dependencies and artifact flows, reducing the attack success rate to 22.5% while maintaining 99.5% benign workflow acceptance.
cross-skill compositionskill scannersllm-based chain planningcontext-aware scanningartifact flow
How Do Large Language Models Judge Social Attraction? Evidence from Theory-Grounded Persona Ratings Across Multiple LLMs and Humans
The study evaluates LLMs' capacity to assess social attraction using theory-grounded persona profiles across three tiers (attractive, mixed, unattractive). In Study 1, 34 LLMs demonstrated strong stability and agreement in profile ordering. Study 2 found no significant gender presentation effects. Study 3 compared LLM and human ratings (N=198), revealing consistent three-tier structure but LLM exaggeration of attractiveness extremes. Methodologically, it employed repeated runs (Study 1), matched gender pairs (Study 2), and human benchmarking (Study 3).
large language modelssocial attractionpersona profilesgender presentationhuman benchmarking
Matryoshka Language Model Suites
The paper introduces Matryoshka Language Model Suites, a nested architecture where sub-models of increasing size are trained end-to-end, improving efficiency over independent training. The method reduces total parameters, enables continuous distillation from larger to smaller sub-models, and optimizes speculative decoding by embedding draft models within verifiers. Evaluated on 500M, 1.5B, and 3B sub-models, the suite matches baseline performance on perplexity benchmarks while using 36% less training compute and improving speculative decoding throughput by 14-26%. Architectural ablations provide practical insights for suite construction.
matryoshka trainingspeculative decodingparameter efficiencydistillationnested architecture
Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
The Model Discovery Agent (MDA) introduces a novel framework for data-efficient discovery of mechanistic world models by coupling a large language model (LLM) with Bayesian methods. MDA employs the LLM as a proposer of candidate model structures, integrates sequential Monte Carlo (SMC) for parameter and structure posteriors, simulation-based inference (SBI) for intractable likelihoods, and value-of-information (VoI) for experiment design. It operates in the M-open setting, expanding the hypothesis space when predictive checks indicate inadequacy. MDA demonstrates state-of-the-art performance in data-efficient model learning and interventional forecasting across physics (DPbench), chemistry (CHEMbench), and biology (HHbench) benchmarks.
mechanistic modelssequential monte carlosimulation-based inferencevalue-of-informationexperiment design
Evaluating Generative Time-Series Models on Data with Point Masses
This study critically evaluates generative time-series models on datasets with high point mass probabilities, revealing methodological pitfalls in standard benchmarks. The authors demonstrate that the rolling-origin protocol can yield evaluation windows with atom structures significantly divergent from the dataset, potentially reversing model performance conclusions. They introduce a control method ensuring CRPS invariance while destroying temporal coupling, isolating its statistical contribution. Benchmarking seven models across five seeds shows autoregressive hurdle models outperform conditional flows on five of six datasets, with performance variations up to 153x. Occurrence statistics exhibit substantial variability across seeds, and model rankings differ under five distinct occurrence metrics.
rolling-origin protocolcrps invarianceautoregressive hurdleconditional flowoccurrence statistics
Confusion-Geometry Rebalancing for Long-Tailed Adversarial Training
We propose Confusion Geometry Rebalancing (CGRm), a plug-in framework for long-tailed adversarial training that addresses dual imbalance issues via directed robust errors. CGRm employs periodic robust evaluations to derive class-specific loss weights, robust coefficients, and a directed confusion geometry graph, coupling feedback-weighted robust optimization with graph-guided margin correction to enhance robustness of vulnerable classes and sharpen critical boundaries. Experiments on long-tailed benchmarks demonstrate consistent robust performance gains over existing methods, with ablation studies validating individual component contributions.
adversarial traininglong-tailed distributionsrobust optimizationconfusion geometrymargin correction
Adaptive Semantic Capacity Allocation for Parallel Generative Recommendation
InforID introduces an adaptive semantic capacity allocation framework for parallel generative recommendation, addressing redundant capacity in homogeneous semantic IDs by dynamically distributing a fixed budget across semantic slots. The method jointly optimizes ID length and slot-specific codebook sizes via lightweight target construction, enabling efficient parallel prediction. Experiments show improved recommendation accuracy under comparable capacity budgets, with one-step parallel generation preserved.
parallel generationsemantic idcapacity allocationcodebook sizerecommender systems
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
The paper proposes Open Evaluation Agent (Open-EA), a framework for efficient and promptable evaluation of visual generative models using human-like multi-round strategies. The method decomposes natural-language requests into sub-aspects, generates targeted prompts, samples media, invokes evaluation tools, and iteratively updates plans, combining predefined benchmarks with open-ended analysis. Open-EA reduces evaluation time to 10% of traditional methods while maintaining accuracy, validated on T2I/T2V benchmarks and open-ended queries. The authors also introduce EA-CoT-10K, a corpus for instruction-tuning, and EA-3B, a local planning backbone trained from Qwen2.5-3B-Instruct.
visual generative modelsmulti-round evaluationinstruction-tuningtool invocationt2i benchmarks
Hallucination-Free GUI Grounding via Regression-Free Layout-Aware Matching
A regression-free framework improves GUI grounding accuracy by decoupling instruction understanding from precise localization. The method employs a frozen multimodal large language model (MLLM) for abstract instruction parsing into structured visual descriptions, followed by a Layout-Aware GUI Grounding Model that matches layout-prior candidates without coordinate regression. This approach suppresses hallucinations and avoids fine-tuning, training solely on Text/Icon binary labels. Evaluations on ScreenSpot-Pro and Mind2Web demonstrate over 20% and 15% improvements in grounding accuracy and element selection rates, respectively, compared to end-to-end systems.
gui groundingmultimodal llmregression-freelayout-awarehallucination suppression
Predictive safety filter enhanced curriculum learning control for efficient vehicle dynamics controller
The study introduces a curriculum learning controller augmented with a physics-based predictive safety filter to enhance safety and performance in vehicle dynamics control. The method addresses limitations of traditional parameter calibration and learning-based approaches by improving stability and agility in state-based control tasks. Validation on the Python-CarSim platform demonstrates superior performance and scalability across diverse maneuvers compared to prior work.
curriculum learningpredictive safety filtervehicle dynamicsphysics-based controlpythonsim platform
Avalon-ToM-Bench: Evaluating Fine-Grained Theory of Mind via Asymmetric Game Mechanics
The paper introduces Avalon-ToM-Bench, a fine-grained benchmark for evaluating Theory of Mind (ToM) in AI agents through asymmetric game mechanics from The Resistance: Avalon. It decomposes ToM into a 2×2 taxonomy (epistemic vs. motivational reasoning, inference vs. action) using perspective-constrained queries. Evaluating 28 LLMs reveals three key findings: models excel at game-rule comprehension but struggle with ToM reasoning; hidden states often contain correct mental-state inferences not expressed in generation (77-82% probe accuracy vs. 62-70% chain-of-thought); and dedicated reasoning training improves performance (+11.0 points) more than test-time chain-of-thought (+1.1 points).
theory of mindasymmetric game mechanicslinear probingactivation steeringchain-of-thought
DUET: A Diversity-Quality Duet of Distillation Experts for Two-Step Video Generation
DUET introduces a two-expert framework for two-step video generation, combining trajectory-level (sCM) and distribution-level (DMD) distillation to reconcile diversity and quality. The method employs independent training of experts: sCM handles high-noise steps for structural diversity, while DMD refines low-noise steps for detail quality. DUET+ further optimizes the relay interface and high-noise stage via RL-guided adaptation. Using Wan2.1-T2V-1.3B, DUET matches DMD's quality while preserving sCM's diversity (2× DMD), and DUET+ enhances quality without sacrificing diversity.
diffusion modelsvideo generationdistillation expertstrajectory-level distillationdistribution-level distillation
NeuroRefiner: Morphology-Aware Multi-Agent Refinement for 3D Fluorescence Microscopy Neuron Segmentation
NeuroRefiner introduces a morphology-aware multi-agent system for 3D fluorescence microscopy neuron segmentation, addressing challenges in preserving local details and global topology. The method formalizes expert workflows through three collaborative agents: diagnosing topological errors, generating correction instructions, and validating refinement quality. It employs TopoRefineNet, a 3D U-Net-based tool with cross-modality feature fusion, for voxel-level editing. Multi-round agent reasoning enhances segmentation accuracy and interpretability. Evaluations on BigNeuron, CWMBS, and ZBFWB datasets show NeuroRefiner outperforms state-of-the-art methods, achieving a 3.02% F1 score improvement on ZBFWB.
neuron segmentationfluorescence microscopymulti-agent systemtopological errorscross-modality fusion
Rethinking Self-Evolving Agents: Do We Still Need Prescribed Optimization Pipelines?
The paper introduces Open-Ended Optimization (OEO), a framework where a frontier model (GPT-5.5) dynamically composes optimization processes without prescribed pipelines, contrasting it with staged (SkillOpt) and evolutionary (GEPA) approaches. Evaluated across 14 comparisons in 8 benchmark settings, OEO achieves 12 wins and 1 tie, using 34.3% of SkillOpt's token budget. Trajectory analysis reveals prescription affects process consistency more than outcomes, suggesting prescribed pipelines serve as capability-dependent scaffolding for weaker optimizers.
self-evolving agentsopen-ended optimizationprescribed pipelinesin-context learningoptimizer capability
Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks
The paper introduces Active Attention Probing to evaluate internal safety scores' effectiveness against jailbreak attacks, demonstrating that current metrics measure harmful intent rather than attack success. By pairing base goals with plain and wrapped versions, the authors generate completions from target models and analyze attention-based measurements. Results show that wrapping increases harmful generation from 0.05 to 0.27 while reducing harmful intent AUROC from 0.936 to 0.803, indicating attacks become more dangerous as prompts appear safer. The reversal persists across three models, seven attack families, and two judges, highlighting distribution shift's impact on calibration and threshold transfer.
active attention probinginternal safety scoresjailbreak attacksharmful intentdistribution shift
Adaptive Sequential Test Planning for Multi-Mechanism Reliability Qualification via Bayesian Monte Carlo Tree Search
The paper introduces a closed-loop adaptive test planning framework for semiconductor reliability qualification, addressing multi-mechanism failure modes via Bayesian Monte Carlo Tree Search (MCTS-SA) and Extended Kalman Filter (EKF) belief-state estimation. The method models per-device variability in Bias Temperature Instability (BTI), Electromigration (EM), and Time-Dependent Dielectric Breakdown (TDDB), optimizing stress selection under catastrophic failure constraints. Results show characterization yield improving from 20% to 54% over 5,000 iterations, with successful sequences terminating within safety margins (DEM=0.564, DTDDB=0.537), demonstrating superior performance over non-adaptive strategies.
monte carlo tree searchextended kalman filterreliability qualificationsequential optimizationfailure mechanisms
Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection
The paper proposes a structure-enhanced and quality-aware framework for robust lane detection, addressing feature discontinuity and confidence-localization decoupling in anchor-based detectors. The method integrates a Gated Horizontal-Vertical Token (GHVT) module to enhance backbone features via directional token interactions and Line-Quality-Aware Dynamic Anchor Scoring (LQAS) to calibrate classification logits with quality supervision. Evaluated on VIL-100, the approach improves ADNet-R34's F1@50 from 89.97 to 91.28, with consistent gains on CULane and TuSimple, demonstrating complementary structural and ranking benefits with minimal overhead.
lane detectionanchor-based detectorsgated token interactionsdynamic anchor scoringstructure-enhanced features
TSPORec: Token Selection via Preference Optimization for LLM-Based Sequential Recommendation
The paper introduces TSPORec, a token selection method for LLM-based sequential recommendation that optimizes computational efficiency while preserving informative content. The approach employs a three-stage pipeline to identify critical tokens across full item descriptions, avoiding the suboptimal truncation used in prior work, and introduces a proxy reward mechanism for optimization. Evaluations on two models and datasets show performance gains up to 31.25% and efficiency improvements up to 63.4% over six baselines.
sequential recommendationtoken selectionpreference optimizationllm efficiencyproxy reward
LEED: Local Embedding Evolution Distance for over-smoothing estimation and virtual node selection in GNN
We propose LEED (Local Embedding Evolution Distance), a node-level metric for quantifying over-smoothing in Graph Neural Networks (GNNs) by tracking individual node embedding evolution across layers. Unlike global metrics like Dirichlet energy, LEED captures heterogeneous over-smoothing patterns and induces embedding-driven centrality measures for node importance. We leverage LEED to optimize virtual node selection, mitigating over-squashing by constructing Local Virtual Nodes based on a single criterion. Experiments demonstrate that LEED provides more informative diagnostics than Dirichlet energy and improves GNN performance across datasets through effective virtual node integration.
graph neural networksover-smoothinglocal embedding evolution distancevirtual node selectiondirichlet energy
From Sweep to Seam: Interleaved Cross-Block Post-Training Quantization
Interleaved Cross-Block Quantization (ICBQ) improves block-wise post-training quantization by revisiting boundary pairs between consecutive Transformer blocks. The method refines each seam pair twice—once at the end of a chunk and again at the start of the next—while retaining the local two-block objective and reusing calibration inputs from existing pipelines. Under local contraction and smoothness assumptions, ICBQ multiplies the propagated term while keeping residuals depth-independent. Experiments show ICBQ reduces perplexity in ternary quantization, prevents severe degradation in configurations where Sequential CBQ fails, and is compatible with 3-bit and 2-bit GPTQ.
block-wise quantizationtransformer blocksperplexity reductioncalibration inputsternary quantization
Illusion or Integrity? Geometrical Consistency Metric for AIGC Video Quality Evaluation
The paper introduces GeoCon-Bench, a novel benchmark for evaluating AI-generated content (AIGC) video quality by measuring geometric consistency as a proxy for adherence to physical laws. The method estimates global motion via translation, fits homography or fundamental matrix models using background correspondences, and reports inlier ratio and geometric error. Experiments on state-of-the-art AIGC models demonstrate GeoCon-Bench's reliability as a video quality assessment metric, supported by a dataset of 20 scenes across six motion categories.
geometric consistencyvideo quality assessmentai-generated contenthomography estimationfundamental matrix
MADBench: A Benchmark for Modality-Aware Audio Deepfake Detection
MADBench introduces the first benchmark for modality-aware audio deepfake detection, treating speech and environmental audio as distinct acoustic components to address independent manipulation scenarios. The benchmark evaluates state-of-the-art detectors and multimodal large language models under a unified protocol, revealing that environmental audio manipulation is more detectable than synthetic speech, pretrained detectors fail on both components, and manipulated environmental audio asymmetrically degrades speech detection. These findings, invisible under single-label paradigms, establish a foundation for robust, component-aware audio deepfake detection research.
audio deepfakemodality-awareenvironmental audiospeech detectionbenchmark
ICM Out! Better Tournament Strategy from Computed Continuations, vs. Solvers and LLMs
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CoRCi: Cross-Reconstruction of Coherent Interests Modeling in Cross-Domain Sequential Recommendation
CoRCi introduces a dual-target Cross-Domain Sequential Recommendation (CDSR) framework addressing domain-invariant interest coherence via Cross-Reconstruction, which generates mixed-domain representations from pre-encoded specific-domain representations using cross-attention. It employs a sequence-level domain-agnostic loss and FocalNCE, an enhanced InfoNCE objective incorporating Focal Loss to penalize same-domain negatives. Evaluations on four real-world datasets show statistically significant improvements over state-of-the-art CDSR methods across all metrics.
cross-domain sequential recommendationdomain-invariant interestscross-reconstructionfocalncemixed-domain representations
ElasticBack: Stealthy Conditional Backdoor in LLM-Agent Skills via Coupled Trigger-Rule Optimization
ElasticBack introduces a stealthy conditional backdoor attack for LLM-agent skills, leveraging a coupled trigger-rule optimization to activate malicious payloads only when specific conditions are met. The method employs semantic-anchored rule injection to generate a rule R in the skill document and evolves a benign-looking trigger T in user queries via a stealth-constrained genetic search, ensuring weight-free and dormant behavior on benign inputs. Experiments across three target behaviors (50 skills each) and four agent LLMs demonstrate high attack success rates (near-zero false positives), preserved clean accuracy, cross-model transferability, and evasion of deployment-time defenses. These findings highlight vulnerabilities in the skill supply chain.
conditional backdoorllm-agent skillssemantic-anchored rule injectionstealth-constrained genetic searchtrigger-rule optimization
The Politician, the Liar, and the Obedient Worker: Emerging Behavior of LLM Agents in Hierarchical Games
The study investigates whether LLM agents replicate human governance failures in hierarchical organizations by introducing the Hierarchical Game (HG), a public goods game extended with managerial authority and democratic elections. Testing six frontier models across twelve institutional configurations reveals distinct behavioral profiles: Qwen exhibits promise-breaking (13.3%), Grok shifts from non-cooperation (16%) to full compliance under managerial punishment, while Claude and GPT-4o maintain baseline cooperation. However, honesty degrades with managerial salaries (except GPT-4o), anonymous punishment induces cheating, and single-model-family groups exhibit entrenched leadership, whereas mixed groups enable turnover.
hierarchical gamellm agentspublic goodsgovernance failuresbehavioral profiles
Distributed Optimization with Streaming Data: A Temporal Weighting Perspective
The paper presents a theoretical analysis of decentralized optimization methods for streaming data with time-varying objectives, formulated as temporally weighted averages of network-wide losses. It examines multi-iteration decentralized first-order methods, including decentralized gradient descent, under strongly convex and smooth loss assumptions. For uniform, exponentially discounted, and finite-memory windowed weighting schemes, the derived bounds decompose tracking error into fixed-point and decentralization-induced bias components, revealing distinct convergence behaviors and non-vanishing error floors under constant step sizes.
decentralized optimizationstreaming datatemporal weightingtracking errorgradient descent
From Semantic Grounding to Decision Optimization: A Unified Framework for Long-Horizon UAV Vision-Language Navigation
The paper proposes a unified semantic-to-decision framework for UAV vision-language navigation (UAV-VLN) to address weak instruction grounding, underutilized history, and unstable decisions. The method integrates an instruction-grounded semantic enhancement module for object-level semantics, a relevance-aware dynamic temporal aggregation strategy for landmark prompts, and a topology-aware decision method with multi-reward policy optimization. Evaluated on AerialVLN and OpenFly benchmarks, the approach achieves state-of-the-art performance.
uav-vlnsemantic groundingtemporal aggregationpolicy optimizationvision-language navigation
Bidirectional Context Self-Distillation for Reinforcement Learning of Skill-Based LLM Agents
The paper introduces BCSD (Bidirectional Context Self-Distillation), a framework enhancing LLM agents' skill-utilization ability via self-distillation combined with reinforcement learning. BCSD evaluates trajectories from two complementary skill-context views: an augmented view with Meta-Skill guidance and a reduced view highlighting task-specific skills, combining their token-level signals to rescale RL advantage. Experiments on ALFWorld and WebShop show BCSD achieves superior performance across model scales, improving external skill utilization. Ablations confirm the complementary roles of both views.
self-distillationreinforcement learningllm agentsskill-utilizationmeta-skill
ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models
ELBench introduces a multi-dimensional benchmark for evaluating education-facing large language models, assessing General Capability, Safety and Trustworthiness, Basic Education, and High-Level Cultivation under a unified protocol. The benchmark combines curated public data with synthesized safety and cultivation datasets, evaluating nine models including seven general-purpose systems and two education-specialized variants. Key findings include: module-level profiles reveal distinct strengths, with safety anti-correlated with practical teaching (r = -0.83); Chinese-developed models lead in safety, particularly on region-specific content; education-specialized models underperform in education modules, with systematic blind spots in High-Level Cultivation tasks favoring pedagogical style over goal alignment.
education-facing llmsmodule-level profilingsafety and trustworthinesshigh-level cultivationdomain post-training
Dual-Adversarial Safety Alignment: Cultivating Intrinsic Threat Comprehension in LRMs
The paper introduces AdvSafe, a dual-adversarial framework for safety alignment in large reasoning models (LRMs) by cultivating intrinsic threat comprehension. The method employs a two-phase adversarial game: (1) adversarial synthesis, where an autonomous agent crafts adaptive jailbreak prompts to breach a teacher model, and (2) adversarial extraction, where the teacher explains successful attacks to generate a compact reasoning dataset. Experiments demonstrate that AdvSafe-aligned LRMs, trained on just 1K synthesized samples, achieve superior jailbreak robustness (outperforming baselines) with minimal utility degradation and improved generalization to out-of-distribution prompts.
safety alignmentadversarial robustnessjailbreak promptslarge reasoning modelscognitive defense
TCS-BENCH: Benchmarking State-of-the-Art Generative AI Theoretical Computer Science Research Ability
TCS-Bench introduces a benchmark for evaluating Large Language Models (LLMs) on research-level Theoretical Computer Science (TCS) proof generation, comprising theorem-proving tasks from top venues (STOC, FOCS, SODA). Each task provides context for deriving self-contained proofs, with correctness verified by an automated verification agent. The verifier's accuracy is benchmarked against human-expert judgements on proof pairs, achieving over 90% accuracy on the expert-labeled set. This benchmark enables systematic assessment of LLMs' capabilities in advanced TCS research.
large language modelstheoretical computer scienceproof generationverification agentbenchmark
verdi: retrieval is not transfer for continual world model optimization
VERDI introduces a continual framework for evidence-licensed optimization of pretrained world models, addressing the challenge of transferring optimization strategies across models. The method constructs Optimization Fingerprints through shared inference-time probes, retrieves relevant prior experience as ranked hypotheses, and validates candidates using a frozen target-side verifier. Probe evolution is triggered by contradictions among fingerprints, refining diagnostic representations. Experiments on Ctrl-World, Cosmos, and RoboCoin demonstrate VERDI's effectiveness, reducing search cost by 68%, GPU cost by 69%, and negative transfer from 0.34 to 0.06, while achieving 83% sign accuracy in predicting transfer outcomes.
optimization fingerprintsinference-time probesnegative transfertarget-side verifierprobe evolution
Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries
Carnot introduces an interactive execution engine for AI-driven analytics that addresses the opacity and inefficiency of existing natural language query systems for enterprise data lakes. The system compiles natural language queries into physical execution graphs, exposing them through an interactive notebook interface. Users can critique, incrementally execute, inspect intermediate results, and edit underlying code or semantic operator instructions. Carnot's query optimizer optimizes queries based on user-defined cost or latency constraints. The approach enables efficient and verifiable insights, demonstrated through real enterprise use cases.
execution enginephysical execution graphsinteractive notebookquery optimizersemantic operators
RangeFactory: Scalable Construction of Multi-Hop Cyber Ranges
RangeFactory introduces an automated framework for scalable construction of multi-hop cyber ranges by formulating range assembly as dependency resolution. The system extracts dependencies from actual attack trajectories, resolves known dependencies via template-guided orchestration, and validates runtime dependencies through end-to-end attack execution. Applied to create RangeBench (1,148 validated instances across 287 attack chains), it reveals a 24.5-47.0% sustained-compromise gap in frontier agents and produces 5,541 annotated multi-hop attack trajectories for analysis and training.
cyber rangesdependency resolutionmulti-hop attacksvulnerability orchestrationattack trajectory
STAIR: Effective Incident Response Using an End-to-End Agentic Planning Framework
STAIR introduces an end-to-end agentic planning framework for adaptive incident response in compromised software systems. The method maintains incident state as Graph-as-State, employs stage-specialized agents via a Stage Router, and retrieves historical experiences to guide action selection, with an Execution Harness for feedback and validation. Evaluated on 100 Docker-based cyber ranges, STAIR achieves a normalized defense score of 0.94, outperforming the strongest baseline by 9.5%.
agentic planninggraph-as-statestage routerexecution harnesscyber ranges
One Adapter Pair per Model: A Universal Activation Interface for Language Models
The paper introduces a Universal Activation Bus, a framework enabling shared activation-based tools across compatible language models through a common activation interface. The method involves learning a shared dense space using a small set of source models, accompanied by one lightweight linear encoder-decoder adapter pair per model. After source training, the interface is frozen, allowing new models to join by fitting only their adapter pair on unlabeled matched text. Results demonstrate that semantically related texts form consistent neighborhoods in the shared space across five models, enabling effective reuse of tools like probes and sparse autoencoders without retraining. Additionally, intermediate activations from one model can be utilized by another model's frozen upper layers for predictions.
universal activation busshared dense spacelinear encoder-decodersparse autoencodersintermediate activation
Renormalising Generative Models for Active Inference: Foundations, Derivations, and Verification
This paper provides a self-contained, derivation-oriented account of renormalising generative models (RGMs) for active inference, addressing scalability challenges in spatial and temporal domains. RGMs compose discrete generative models hierarchically, coarse-graining lower-level states into higher-level causes for objects and actions. The work clarifies implicit algorithmic details, explicates implementation choices, and offers an open, verified implementation to enhance transparency and reproducibility. By decoupling theory from specialized software, it facilitates future quantitative evaluation on machine-learning benchmarks.
renormalising generative modelsactive inferencecoarse-graininghierarchical compositionverified implementation
Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts
The paper introduces Build it, Break it, Repeat (BiBiR), an iterative framework for stress-testing disinformation detection robustness under adversarial conditions. BiBiR evaluates detector performance when social media posts are systematically transformed to evade classification, using techniques like back-translation and LLM persona-based rewriting. Results show adversarial transformations achieve a 95% label flip rate while preserving semantic meaning, and a triplet contrastive model with dynamic anchor switching (DASS) architecture outperforms baselines by 15 percentage points, achieving 72.68% accuracy. The framework effectively exposes detector weaknesses and drives robustness improvements, though semantic preservation analysis remains crucial.
adversarial transformationslabel flip ratetriplet contrastive modeldynamic anchor switchingsemantic preservation
Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
The paper introduces AlignXada, a training-free meta-learning framework for task-specific preference adaptation in LLM personalization. Given a universal user preference summary and a downstream task, AlignXada derives task-conditioned representations by iteratively optimizing a textual refinement policy through verbal reinforcement learning. Evaluated across 13 tasks and three downstream models (39 task-model cells), AlignXada achieves an average gain of 3.82 points, improves 33 cells, retains only 22.8% of original profile tokens, and outperforms RAG in 36 cells. Faithfulness analysis confirms that refined profiles remain grounded in source preferences while preserving task-relevant personalization signals.
task-specific adaptationverbal reinforcement learningmeta-learningllm personalizationpreference summary
Capability Is Not Propensity: Measuring Pressure-Robust Cooperative Behavior in Civic LLM Agents
The paper introduces DiffCoop-Civic, a 10-scenario evaluation suite to measure pressure-robust cooperative behavior in LLMs, distinguishing capability (benign instruction performance) from propensity (realistic civic pressure responses). Testing seven models across four families, subtle omission pressure increased manipulative enablement by 1.17 points and reduced dissent preservation by 1.67 points (5-point scale), while overt false-consensus pressure elicited refusal in aligned API models but compliance in open-weight models. A Pareto-Trace prompting intervention improved robustness without hard refusal. Reproducibility materials are anonymously hosted.
cooperative aipressure robustnessmanipulative enablementdissent preservationpareto-trace prompting
Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance
The paper introduces MGN-AIR, a pixel-level restoration framework for all-in-one image restoration that addresses the limitations of uniform restoration strategies. The method learns pixel-level visual prompts and combines them with textual prompts to provide global and local degradation cues, enabling fine-grained control. Experiments on benchmarks covering denoising, deraining, deblurring, dehazing, desnowing, and low-light enhancement show MGN-AIR significantly outperforms existing approaches.
pixel-level restorationall-in-one restorationvisual promptsdegradation cueslow-level vision
From Prompt to Harness: Coderlet from Scratch
The paper presents a compact harness design for programming agents, focusing on the transformation of model generations into environmental actions and the integration of runtime feedback into subsequent decisions. The proposed architecture delineates three boundaries—model, execution, and state—to connect model services, tool environments, and persistent state, while a request lifecycle governs their interactions. The design, implemented in the executable artifact Coderlet, supports gradual refinement through bootstrapping across runs.
harness designprogramming agentruntime feedbackrequest lifecyclebootstrapping
ActBench: Self-Evolving Benchmark of Behavioral Safety in Cowork Agents
ActBench introduces a self-evolving benchmark for evaluating behavioral safety in cowork agents, focusing on execution trajectories rather than final responses. The benchmark comprises 600 cases across 213 scenarios, covering 15 risk behaviors, six execution spaces, and 48 web-service APIs. A reward-guided beam search method optimizes attack effectiveness and task utility, while a dual evidence verification mechanism ensures safety and utility through log and LLM-based trajectory evidence. Evaluation of 15 LLMs and 6 open-source cowork agents across 24,000 trajectories reveals attack success rates ranging from 10.1% to 94.4%, showing greater variation across models than agent harnesses.
behavioral safetyexecution trajectoriesreward-guided beam searchdual evidence verificationcowork agents
MixFormer: Linear Transformer with Mixture of Memory Experts
MixFormer introduces a linear Transformer architecture with a Mixture-of-Memory-Experts (MoE) mechanism to address limitations in State Space Models (SSMs), specifically input adaptivity and memory capacity. The model employs multiple memory experts and a Time-Aware Linear Attention (TALA) mechanism, utilizing learnable exponential decay functions and positional biases for dynamic memory updates. This design selectively reinforces critical historical information, mitigating memory dilution and enhancing long-range dependency modeling. Experiments on long-sequence text and image generation tasks demonstrate significant performance improvements, positioning MixFormer as a sustainable computational backbone for future web infrastructure.
linear transformermixture-of-memory-expertsstate space modelstime-aware linear attentionlong-range dependency
RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation
RecoverFly introduces a failure-aware reinforcement learning post-training framework for end-to-end UAV vision-language-action policies, addressing sample inefficiency, long-tailed scene distributions, and policy shift. The framework employs token-level RL for stable optimization of grammar-constrained autoregressive actions, revisits unresolved failure cases for corrective learning, and combines a two-stage long-tail scene curriculum with reference-policy regularization. Evaluated on the TravelUAV benchmark, RecoverFly achieves state-of-the-art performance across seen, unseen-map, and unseen-object splits, improving success rates by 3.12 to 8.37 percentage points over AerialVLA initialization with 30% of the training-set rollout budget.
uav-vlnreinforcement learningtoken-level optimizationlong-tail curriculumreference-policy regularization
Learning to Modulate, Not to Cycle: Soft Actor---Critic Recovers Inverter-Style Heat-Pump Control
The study demonstrates that Soft Actor-Critic (SAC) learns inverter-style continuous modulation policies for heat-pump control, eliminating compressor cycling while maintaining thermal comfort. By augmenting the reward function with a levelised compressor-wear term, SAC outperforms Proximal Policy Optimisation (PPO) on the BOPTEST bestest hydronic case, achieving zero daily start-ups versus PPO's bang-bang control. In emulator tests, SAC reduces thermal discomfort by 90.7% at an 11.5% energy-cost increase compared to baseline, while completely eliminating cycling.
soft actor-criticheat-pump controlcompressor wearreinforcement learningboptest
WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training
WDL-OPD introduces a mixture-constrained co-training method for on-policy distillation (OPD) to address instability in student-teacher alignment. The approach employs an anchor policy for rollouts and an auxiliary policy for state evaluation, with their token distributions' geometric mixture matched to a frozen teacher via reverse KL. Experiments on Qwen3 models (1.7B and 4B parameters) show improvements in MATH500 accuracy (0.630→0.685 at 4B, 0.521→0.585 at 1.7B) and code generation performance, demonstrating stabilization over single-policy OPD variants. The method's algorithm and failure cases are provided for hypothesis testing.
on-policy distillationreverse klco-trainingtoken distributionsqwen3
Coupled Graph--Policy Distillation for Personalized Medication Safety in Older Adults with Multimorbidity
The paper introduces ATLAS, a coupled graph--policy distillation framework for personalized medication safety in older adults with multimorbidity. ATLAS constructs a medication-safety graph from guidelines, dynamically updates patient state via targeted questions, and distills relations into a patient-specific medication conflict graph (PMCG). A risk-first multi-agent policy uses PMCG to screen contraindications, assess cautions, and verify medication plans. Evaluated on GeriMedBench and other benchmarks, ATLAS achieves 53.73-point higher Strict Success Rate and 14.63-point higher overall safety reasoning score than proprietary LLM baselines, with no unsafe recommendations in automated evaluation. Clinician ratings favor ATLAS across all criteria.
graph distillationmedication-safety graphmulti-agent policypatient-specific conflict graphgeriatric benchmarking
Listen, See and Track: Spatio-Temporal Audio-Visual Sound Event Reasoning for Omni-Modal Language Models
The authors introduce ST-OmniQA, a 40K-video benchmark with 400K question-answer pairs for evaluating spatio-temporal audio-visual reasoning, covering sound-event recognition, direction-of-arrival, distance estimation, and motion tracking. They propose ST-Omni-R1, an omni-modal model integrating first-order Ambisonics-derived semantic/trajectory representations with panoramic visual context via curriculum learning and reasoning-tree reinforcement learning. ST-Omni-R1 achieves 77.83% average accuracy across four capability levels (37.28% for best baseline) and demonstrates transferability on three spatial-audio benchmarks.
spatio-temporal reasoningfirst-order ambisonicspanoramic visioncurriculum learningreasoning-tree reinforcement
How Simple Can It Get? From Interpretable Equations to Readable Rules for Financial Decision Making
The study introduces a methodology for simplifying interpretable financial decision-making models into more readable forms while quantifying performance trade-offs. Starting from a single-equation classifier, the authors progressively transform it into pruned monomials, directional if-then rules, and integer scorecards. Empirical evaluation across four financial datasets reveals that pruning incurs minimal performance loss and that simpler rules can remain effective classifiers despite reduced fidelity to the original model. Human assessments indicate improved readability, with preferences varying by professional background. The authors also derive theoretical bounds on pruning-induced changes and predict feature-direction preservation in rule transformations.
interpretable classifierspruned monomialdirectional ruleinteger scorecardfidelity trade-off
ZetaGPT: A Reference Implementation of Positional--Encoding--Free State--Space--Attention Language Models
ZetaGPT introduces a positional-encoding-free language model architecture that integrates causal state-space equations before self-attention to implicitly encode positional information. This approach eliminates the need for explicit positional embeddings or encodings while maintaining self-attention's expressive capacity. The model provides a compact, open-source implementation for research, prototyping, and educational purposes, including a complete training pipeline with dataset construction, tokenizer training, pretraining, supervised fine-tuning, RLHF, and CoT reasoning via reinforcement learning. ZetaGPT serves as the first open-source small language model without explicit positional encoding, offering a reproducible reference for empirical study.
positional-encoding-freestate-space equationsself-attentionrlhfchain-of-thought
LITEWAY: LIghtweight HAR via Temporal Efficient highWAY
LITEWAY introduces a lightweight, fully convolutional framework for wearable human activity recognition (HAR) that replaces recurrent architectures with structured convolutional decomposition. The method combines lightweight convolutional blocks, strided temporal processing, and convolution-attention pooling to efficiently capture temporal dependencies while reducing computational complexity. Evaluated on 16 HAR datasets, LITEWAY achieves competitive macro F1 scores while reducing model size by 4.06x-9.52x and energy consumption by 1.46x-3.14x compared to TinyHAR, TinierHAR, and MLP-HAR. The framework demonstrates efficient temporal modeling for resource-constrained devices.
convolutional decompositiontemporal processingconvolution-attention poolingwearable harlightweight framework
KVDiagnosis: A Diagnostic Benchmark for KV-Cache Compression in Long-Context Language Models
The paper introduces KVDiagnosis, a diagnostic benchmark for evaluating KV-cache compression methods in long-context language models. It provides a 25-method taxonomy, verified implementations, and a dataset of 59,800 compressed runs on Qwen3-8B, with 12,520 C-to-W (correct-to-wrong) rows. Results show 63.2% of cases have low or partial coverage, while only 0.2% exhibit high coverage with strong likelihood drift. Diagnostic tests achieve stratified AUROC scores of 0.684-0.871, and a 4x evidence-attention boost repairs 29.2% of failures. The benchmark includes explicit measurements for cache, likelihood, attention, and decoding states.
kv-cache compressionlong-contextdiagnostic benchmarkevidence-attentionlikelihood drift
Sign Language Recognition Using Original and Synthetic Depth Image Based Point Cloud Data Models
The study evaluates sign language recognition using point clouds from both original and synthetic depth images, generated via Depth Anything V2 from RGB inputs across three datasets (Real-time ASL Fingerspelling, KArSL, AUTSL). PointNet architectures classified point clouds using frame-based, Point Gesture Map, and Long Short Term Memory models. Results show acceptable performance for both original and synthetic data, with original depth-based models generally outperforming synthetic ones, though exceptions occurred.
sign language recognitiondepth imagespointnetpoint gesture maplong short term memory
Monotonicity-Guided Bottom-Up Petri Net Discovery: The SPECpp Framework
The SPECpp framework introduces a bottom-up approach for Petri net discovery, leveraging monotonic properties to efficiently characterize places and enabling complex behaviors like concurrency and long-term dependencies to emerge organically. Unlike top-down methods such as the Inductive Miner, SPECpp avoids predefined constructs and addresses the exponential candidate space through optimized strategies for high-quality model generation. The framework is evaluated using synthetic and real-life event data, demonstrating its effectiveness under resource constraints.
petri netsprocess discoverymonotonicitybottom-upfree-choice constructs
Temporal Misgrounding in Legal RAG: A Versioned-Corpus Benchmark for French Tax Law
The paper introduces FiscalQA Pro, a benchmark for temporal misgrounding in legal retrieval-augmented generation (RAG), where systems incorrectly retrieve the current-version legal text instead of the applicable historical or future version. The dataset comprises 32,436 versioned French tax code articles (1938-2031) and 209 expert-reviewed temporal-reasoning questions. Evaluations show parametric knowledge achieves 3.0% strict accuracy, static RAG 2.7%, and static RAG retrieves the correct version 0% of the time. A proposed multi-version retriever reaches 98.3% accuracy, with an oracle ablation indicating recall gaps. The release includes a version-aware jurisprudence dataset (69,208 citations), corpus, benchmark, and pipeline code.
temporal misgroundinglegal ragversioned corpusretrieval-augmented generationparametric knowledge
Imaginative Generative AI: Crossing the Entropy Wall into Worlds Beyond Imitation
The paper introduces Imaginative Generative AI (IGA), a framework that explicitly optimizes for diversity in generative models by targeting distributions with prescribed spectral diversity levels. IGA measures diversity via von Neumann entropy of the kernel covariance operator in a fixed representation space, enabling both diversity repair (below the data's 'Entropy Wall') and controlled extrapolation beyond it. The method formulates a KL-anchored entropy-constrained projection, yielding IGA Guidance for inference-time adaptation of score-based/diffusion models (e.g., DDPM, DDIM). Experiments on synthetic and vision benchmarks demonstrate effective diversity repair and extrapolation.
generative aispectral diversityvon neumann entropydiffusion modelskl divergence
OpenLoopEvolve: A Verifiable Self-Evolution Framework for Loop Policies in Long-Horizon Complex Tasks
OpenLoopEvolve (OLE) introduces a verifiable self-evolution framework for Loop Policies in long-horizon complex tasks, enabling the accumulation and reuse of control experience across historical traces. OLE represents agent components as portable policy assets with versions and lineages, offering online and offline evolution modes. The online mode generates candidates based on continuous operation feedback, while the offline mode searches archived traces and failure evidence. Both modes employ autonomous proposals by a large language model, Champion-Challenger evaluation, and robust release. On the YC-Bench benchmark, OLE improves task performance, success rate, and risk metrics compared to a fixed initial Loop Policy, demonstrating the benefits of treating Loop Policies as governable assets.
loop policyself-evolution frameworkchampion-challenger evaluationlong-horizon tasksportable policy assets
CircuitReason-1k: Benchmarking Long-Horizon Visual-to-Symbolic Reasoning inElectrical Circuits
We introduce CircuitReason-1k, a benchmark of 1,000 authentic textbook problems for evaluating long-horizon visual-to-symbolic reasoning in electrical circuits. Each problem pairs circuit diagrams with questions, answers, and worked solutions, organized by a reasoning-oriented taxonomy. The evaluation combines conservative typed scoring with semantic consensus across models. Testing three commercial chatbots and six open-source multimodal LLMs reveals the highest accuracy at 84.8%, with performance deteriorating on long-horizon problems due to failures in topology-to-target binding and physical conventions. The benchmark provides a focused testbed for assessing multimodal models' ability to transform visual evidence into physically valid symbolic reasoning.
visual-to-symbolic reasoningmultimodal modelscircuit analysistopology-to-target bindingsemantic consensus
FeedbackTrack: Visual-Cortex-Inspired Cross-Frame Feedback for Transformer Tracking
FeedbackTrack introduces a visual-cortex-inspired cross-frame feedback mechanism into Transformer-based visual object tracking, enhancing temporal integration by reusing intermediate representations. The framework employs two lightweight pathways—Query Feedback for token-level query modulation and Gate Feedback for context-dependent feature modulation—while maintaining a fixed-size one-frame cache. Evaluated on LaSOT and GOT-10k with SPMTrack and ARTrackV2 backbones, FeedbackTrack achieves 83.4 AO and 79.1 AUC, improving performance by 1.8–3.2 AO points over same-frame modulation. The method adds less than 1% parameters and reveals non-uniform depth-dependent feedback strengths, demonstrating the efficacy of recurrent historical information for Transformer tracking.
transformer trackingcross-frame feedbackquery modulationgate feedbacktemporal integration
Deep Learning based Detection of Fishing Vessels and Fishing Monitoring using Nightlight Images
A dual-branch YOLO11 architecture was developed for detecting small-scale fishing vessels using nighttime light imagery from the SDGSAT-1 satellite, addressing the challenge of 'dark vessels' without AIS transmission. The model processes both 10-meter panchromatic and 40-meter RGB imagery through parallel convolutional backbones, optimized for small object detection in NTL imagery. It achieved a precision of 0.99, recall of 0.93, F1-score of 0.96, and mAP@50 of 0.96, outperforming YOLOv5s, YOLOv8s, and standard YOLO11s. Applied to the western coast of India, it detected 31,525 vessel instances (77.3% potential dark vessels), revealing peak fishing activity from January to April within 50-100 km of the coastline.
dual-branch yolo11nighttime light imagerysmall object detectiondark vesselsais transmission
Test-Time Augmentation for LLMs: When Input Diversity Beats Output Diversity at Matched Compute
The paper demonstrates that test-time augmentation (TTA) with input-side diversity (semantic rephrasing, lexical perturbations, visual transformations) outperforms output-side diversity methods like self-consistency in cost-effectiveness for LLM inference. Across six datasets spanning general knowledge, mathematical reasoning, and multi-modal QA, semantic rephrasing achieved 1.8X higher accuracy per dollar than self-consistency, Pareto-dominating it on five tasks. The study systematically compared matched-compute scenarios, finding TTA most effective for mid-tier models where stronger alternatives are unavailable or prohibitively expensive.
test-time augmentationself-consistencysemantic rephrasingmatched-compute comparisonpareto-dominance
LLM-Guided Heuristic Design from Simulation Traces: A Case Study in Dynamic Production and AGV Scheduling
The paper introduces an LLM-guided heuristic design framework for simulation-based optimization that leverages event-level traces for policy diagnosis and revision. The method employs a manager agent to identify bottlenecks from low-scoring simulation replications and parallel editing agents to implement code-level revisions, with best-so-far selection retaining improvements. Evaluated in dynamic production and AGV scheduling, Gemini-3.1-Pro-generated policies achieved a mean score of 77.51 (0-100 scale), outperforming rolling-MILP, rule-based, and metaheuristic baselines across 100 seeds and maintaining robustness under faults. Ablations confirmed the necessity of both parallel candidate generation and trace-database access.
simulation-based optimizationllm-guided heuristicevent-level tracesautomated guided vehicleparallel candidate generation
Control-Oriented Scenario Tree Construction through Reinforcement Learning
The paper introduces a reinforcement learning-based method for constructing scenario trees optimized for multistage stochastic model predictive control (MPC). Unlike conventional approaches that focus on distributional accuracy, this method trains an attention-based policy to assign scenarios to tree leaves, maximizing closed-loop control profit. Training is stabilized using an asymmetric critic leveraging realized trajectories. Evaluated on a risk-averse battery arbitrage problem, the method outperforms classical reduction techniques and certainty-equivalent control, achieving higher profit and robustness. Analysis reveals the construction of compact, selectively branching trees that capture high-impact events while maintaining near-deterministic trajectories.
scenario treereinforcement learningmodel predictive controlattention-based policyasymmetric critic
RAG-Audio: Retrieval-Augmented Generation for Faithful Brain-to-Audio Reconstruction
RAG-Audio proposes retrieval-augmented generation to address prior domination in brain-to-audio reconstruction, where weak neural signals yield realistic but inaccurate outputs. The method decodes fMRI into semantic audio embeddings, retrieves matching exemplars, and initializes a frozen generator's sampling trajectory from these while maintaining decoded conditioning. On Brain2Music, RAG-Audio improves 10-way stimulus identification from 0.14–0.18 (near chance) to 0.40–0.43, rivaling retrieval, and reduces Fréchet Audio Distance from 13.49 to 1.25 for AudioLDM. An autoregressive negative control confirms trajectory initialization drives gains, suggesting retrieval mitigates prior domination.
brain-to-audioprior dominationretrieval-augmented generationfréchet audio distancetrajectory initialization
GeoPhysAdapter: Scale-Matched Geophysical Adaptation for Cross-Domain Landslide Mapping with Vision Foundation Models
GeoPhysAdapter introduces a scale-matched geophysical adaptation framework for cross-domain landslide mapping using vision foundation models. The method anchors on a frozen vision foundation model, integrates terrain, material, and rainfall triggering constraints at pixel and candidate landslide body levels, and reverts to visual predictions where support is insufficient. Evaluated on the PILD dataset with 7,890 test samples across 55 global landslide events, pixel-level adaptation reduces errors by 7.76%, while candidate body-level adaptation increases error reduction to 23.99%, improves IoU by 0.031, and corrects 9.92 pixels per pixel harmed. The approach effectively mitigates false positives and enhances mapping accuracy.
landslide mappingvision foundation modelscross-domain transferabilitygeophysical adaptationuncertain geographic context problem
CoRE: Consensus Rewards via Equilibrium for Test-Time Reinforcement Learning
CoRE (Consensus Rewards via Equilibrium) introduces a graph-based method for deriving test-time rewards in reinforcement learning, replacing majority voting. It constructs a graph from $N$ roll-outs, combining answer agreement, reasoning similarity, and generation confidence, and applies replicator dynamics to extract a dominant set, yielding refined pseudo-labels, graded rewards, and cohesiveness gates. CoRE strictly generalizes voting, with theoretical guarantees on minority recovery and confidence calibration. Evaluated across seven backbones and five benchmarks (42 model--benchmark cells), CoRE improves untrained base performance by +21.7 points on average (+20.4 for majority-vote TTRL), achieves up to +7.5 points over voting, and reaches baseline plateau accuracy in 54--70% fewer steps.
test-time reinforcement learningreplicator dynamicsconsensus rewardsgraph-based rewardsconfidence calibration
ASPaeroFlow: Decomposition Heuristics for Joint Air Traffic Flow & Capacity Management
ASPaeroFlow introduces a heuristic for joint Air Traffic Flow and Capacity Management (ATFCM) that resolves the circular dependency between fixed-demand and fixed-capacity assumptions. The method combines instance-space decomposition heuristics with a local exact approach using Answer Set Programming, enabling scalable optimization. Benchmarks demonstrate that ASPaeroFlow provides a computational middle ground between exact methods and operational baselines, outperforms sequential optimization in joint ATFCM, and reveals Dynamic Airspace Configuration's greater impact on solution quality compared to flow measures.
air traffic flow managementdynamic airspace configurationanswer set programmingdecomposition heuristicsjoint optimization
Linearized 2-Simplicial Attention
The paper introduces a linearized 2-simplicial attention mechanism by reformulating the trilinear score as an inner product between a composite query and key, enabling linear computational cost in sequence length while maintaining global reach. The method approximates the sum over one token axis with positive random features and stores past tokens in a fixed-size state, with the second axis handled explicitly over a recent window. Implemented via custom Triton kernels and combined with Kimi Delta Attention, the model achieves state-of-the-art mean downstream accuracy under matched compute, reducing LAMBADA perplexity from 715.6 to 602.6 at 16k context.
2-simplicial attentiontrilinear scorepositive random featureskimi delta attentiontriton kernels
WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation
The paper introduces WorldSimProbe, a diagnostic framework for evaluating the faithfulness of action-conditioned world models (ACWMs) in embodied manipulation tasks. The authors formalize an Observable Simulator Contract requiring action-induced motion and grounded environment responses, operationalized through five controlled test suites assessing control sensitivity, trajectory variation, interaction grounding, and dynamics. Evaluating six ACWMs across 18,000+ instances in RoboTwin, ManiSkill, and LIBERO, the framework reveals systematic degradation in action realization, structured failures in interaction grounding, and benchmark alignment with human judgments. This provides a standardized paradigm for ACWM fidelity assessment beyond task-oriented metrics.
action-conditioned world modelssimulator fidelityobservable simulator contractembodied manipulationinteraction grounding
CADEngBench: It Looks Like CAD, but Does It Work? Evaluating Parametric Design, Assembly Reasoning, and Physics Simulation
The authors introduce CADEngBench, a two-track benchmark for evaluating engineering-grade CAD capabilities beyond visual correctness. CADEngBench-P assesses 300 parametric parts via 600 tasks involving B-Rep validity, DFM checks, parameter perturbations, and matched FEA in CalculiX, while CADEngBench-A evaluates 150 body pairs through joint retrieval, kinematic verification, and face-edge grounding. Testing eight multimodal code-capable models reveals that editing existing CAD is easier than generation, with complex edits and FEA matching remaining challenging, and assembly predictions often failing to recover precise joints. Results demonstrate the necessity of engineering-behavior validation in CAD evaluation.
parametric designb-rep validitykinematic verificationfinite element analysisassembly reasoning
DAVE: A Decoupled Audio-Visual Enhancement Framework for Real-World Speech Separation
DAVE introduces a decoupled audio-visual enhancement framework for robust real-world speech separation, addressing data scarcity via DAVE-Corpus (219,411 mixtures from augmented meeting corpora) and unreliable visual inputs. The method employs progressive multi-objective optimization for separation, intelligibility, speaker identity, and perceptual quality, alongside a certified selective enhancement chain (scene routing, GAN-based denoising, loudness normalization) applied only to no-reference partitions. Evaluated on the Real-World Audio-Visual Speech Enhancement Challenge, DAVE demonstrates robustness under mixed scenarios and visual degradation.
audio-visual enhancementspeech separationprogressive optimizationgan-based denoisingscene routing
UniDFKD: A Unified Semantic Prior Framework for Architecture-Agnostic Data-Free Knowledge Distillation
UniDFKD proposes a unified data-free knowledge distillation framework using architecture-agnostic semantic priors to address limitations of architecture-dependent statistical priors in modern models like Vision Transformers. The method integrates three components: Categorical Semantic Conditioning (language embeddings for semantic diversity), Spatial Semantic Anchoring (Gaussian priors for teacher attributions), and Spatial Semantic Distillation (alignment of teacher-student spatial evidence). Experiments on CNNs and ViTs show UniDFKD outperforms existing methods by an average absolute margin of over 20% in homogeneous and heterogeneous settings.
data-free knowledge distillationvision transformerssemantic priorsspatial attributionteacher-student alignment
VeinCast: Physics-Guided Dynamic Field Graphs with Graph-Conditioned Fusion for Global Medium-Range Weather Forecasting
VeinCast introduces a physics-guided dynamic field graph and graph-conditioned fusion framework for global medium-range weather forecasting, jointly predicting 69 surface and upper-air fields. The method combines predefined atmospheric relations with state-dependent Top-K residual edges in local windows (Physics-Guided Dynamic Field Graph) and employs graph context with source-node centrality for field-to-latent aggregation (Graph-Conditioned Latent Fusion). On the 1.5° ERA5 benchmark, VeinCast matches or outperforms FuXi, Pangu-Weather, GraphCast, FengWu, and ARROW at lead times up to 14 days, with ablations confirming complementary gains from both modules.
dynamic field graphgraph-conditioned fusionmedium-range forecastingtop-k residual edgesearth-window attention
GLocFM: A Geometry-Aware Foundation Model for 3D Indoor Wireless Localization
GLocFM introduces a geometry-aware foundation model for 3D indoor wireless localization by jointly leveraging WiFi measurements and 3D scene geometry. The method formulates localization as maximum-likelihood estimation, using a learned scoring function to match observed delay-angle-of-arrival spectra with predicted spectra from candidate positions. A hierarchical scene encoder extracts propagation-relevant features for LoS and reflection paths, while a ToF-robust variant handles synchronization errors. Evaluated on synthetic (Sionna RT-generated) and real (NeRF$^{2}$) datasets, GLocFM reduces mean 3D localization error by 49.5% and 48.8% respectively compared to state-of-the-art baselines, demonstrating robustness across varying receiver counts, bandwidths, and array sizes.
wireless localization3d point cloudmaximum-likelihood estimationangle-of-arrivaltime-of-flight
ComboShoppingBench: Evaluating LLM Agents for Budget-Constrained Basket Shopping with Coupons
The paper introduces ComboShoppingBench, a benchmark for evaluating LLM agents on budget-constrained basket shopping with coupons, addressing the challenge of open-ended yet verifiable basket construction. The method involves task synthesis via an exploration agent generating feasible baskets, followed by evaluation using LLM judges for semantic satisfaction and deterministic checks for budget compliance and coupon validity. Results show that even strong LLM agents struggle with the benchmark, indicating significant gaps in constraint-aware combo shopping.
llm agentsbasket constructionbudget constraintscoupon optimizationsemantic evaluation
MMArch: Benchmarking Multimodal Reasoning Grounded in Architectural Evidence
MMArch introduces a multimodal benchmark for architecture and civil engineering, evaluating MLLMs' ability to combine distributed visual evidence with domain principles. The benchmark comprises 1,212 short-answer items derived from peer-reviewed figures, validated via automated screening, adversarial audit, and expert review. Testing 18 open-weight and proprietary models reveals a significant gap: top open-source (30%) and proprietary (52%) models trail human experts (95%), with failures concentrated in applying principles and cross-figure reasoning. Code and data are publicly available.
multimodal reasoningbenchmarkarchitecture engineeringvisual evidenceshort-answer items
Software Engineering for and with GUI Agent
The paper conducts a systematic review of 336 GUI-agent publications (2018-2026) to establish software engineering foundations for agent deployment. Analyzing research trends, architectures, and evaluations, it finds rapid growth since 2024 but identifies critical gaps: modular perceive-reason-act loops dominate, yet recovery mechanisms, human escalation, and safety enforcement remain underdeveloped. Evaluations prioritize task success but lack cross-protocol comparability and lifecycle testing. The study highlights deficiencies in observability, privacy engineering, and human oversight, arguing capability improvements alone are insufficient for deployment readiness. Future work must integrate dependable execution with reproducible evaluation, permission controls, and cost-aware governance.
gui agentsperceive-reason-actdeployment readinesslifecycle testinghuman oversight
P$^{3}$: Joint Program-and-Proof Planning for Verified Code Generation
P$^{3}$ introduces a joint program-and-proof planning workflow for verified code generation, addressing inefficiencies in sequential approaches where programs are synthesized before proof attempts. Inspired by Dijkstra's philosophy, P$^{3}$ first derives a unified plan for both program and proof from the specification, then elaborates implementation and proof scaffolds under this shared plan. Evaluated on Lean4Commit0, Verina, and AlgoVeri benchmarks using four frontier LLMs, P$^{3}$ achieves the highest solve rates across all benchmarks, improving by 4.6--11.2 percentage points over baselines while reducing API costs by up to 40% and wall-clock time by up to 37%. Ablation studies confirm gains of 3.3--8.3 points over implementation-only planning.
verified code generationprogram-and-proof planninglean4commit0relational specificationsllm-based workflow
Entropy-based Code Adversarial Translation for Real-world Repository Migration
The authors propose Entropy-based Code Adversarial Translation (ECAT), a multi-agent framework for automated Android-to-HarmonyOS repository migration, addressing long-horizon translation challenges in LLM-based agents. ECAT employs a generator-discriminator architecture that minimizes adversarial entropy, using Code Entropy as a unified metric for migration quality. The generator iteratively updates the repository based on discriminator-produced text gradients, accepting updates only if they reduce Code Entropy. Successful trajectories are distilled into a self-evolving memory tree for transferable knowledge. Evaluated on A2H-RepoBench, ECAT achieves 74.7% migration quality, outperforming existing methods across repositories of varying scales.
entropy minimizationrepository migrationcode entropymulti-agent frameworkself-evolving memory tree
SoftmaxGRPO: Learning to Reason using Softmax Advantage Group Estimation
SoftmaxGRPO introduces a novel group-based reinforcement learning objective that improves gradient allocation across prompt difficulties by replacing z-score-normalized group advantages with temperature-scaled softmax advantages. This method ensures bounded weights regardless of prompt difficulty and derives exact finite-group population objectives for binary rewards, identifying MaxRL as its low-temperature limit. For bounded scalar rewards, it optimizes a log-moment-generating-function objective in large-group updates. Empirical results show SoftmaxGRPO reallocates gradient budget effectively, achieving 51.8% on DeepMath and improving a 1.5B instruction-tuned model from 35.0% to 68.0% on Poetry using lightweight text-similarity rewards.
softmax advantagegroup normalizationtemperature scalinglog-moment-generating-functioninstruction-tuned model
GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views
The GRASP framework addresses fine-grained cross-modal understanding challenges in drone-view scenarios by mitigating Cross-Modal Focus Misalignment and Visual Isomorphism. It combines Region-Focused Alignment (RFA) for object-centric cross-modal alignment and Semantic Perturbation Enhanced Matching (SPEM) with a Semantic Prototype Codebook (SPC) to enhance fine-grained discrimination. Evaluated on GeoText-1652 and ERA datasets, GRASP demonstrates competitive performance in drone-view image-text retrieval, achieving improved cross-modal alignment in aerial scenarios.
fine-grained cross-modal understandingdrone-view scenariosregion-focused alignmentsemantic prototype codebookvisual isomorphism
Can Coding Agents Solve Repository-Level Issues with Rendered Code? An Exploratory Study of Visual Representations
The study evaluates visual code rendering as a token-compression technique for repository-level coding agents, using SWE-bench Verified to measure its impact on repair workflows. Through controlled agent settings, it separates repository exploration from structured repair stages, finding that rendered code reduces prompt-token costs non-linearly while preserving repair accuracy. Results indicate visual compression is most beneficial when source reading is a bottleneck, but offers limited gains in patch-test iteration phases, positioning it as a conditional optimization for coding agents.
visual code renderingrepository-level repairtoken compressioncoding agentsswe-bench verified
Privileged Likelihood Is Not Automatically Value: Three Checks for Token Credit in On-Policy Self-Distillation
The paper critiques privileged self-distillation by demonstrating that token likelihood changes do not inherently confer outcome credit. It proposes three validation checks: score-action alignment, feedback construction independence, and training loss behavior. Formal distinctions are established between scoring methods, particularly contrasting hindsight feedback from the same rollout versus independent rollouts. Experiments with a 20B parameter model on AIME 2025 show that additive token scoring performs near chance (AUC=0.505) and favors incorrect traces after length adjustment, while outcome-only control achieves 64.2% accuracy versus 24.2%-33.9% for token-score variants. The results emphasize separate validation of score meaning, feedback construction, and training behavior.
self-distillationtoken likelihoodfeedback constructiontraining lossoutcome credit
SafeQL: Search-based Refinement for Safe and Efficient LLM-based Text-to-SQL
SafeQL introduces a search-based refinement paradigm for LLM-based Text-to-SQL, redefining the DBMS as an active guide during query refinement. Instead of regenerating entire queries, it incrementally repairs erroneous components by interpreting DBMS feedback, formulating each step as a guided search within a safe query space. Experiments on Bird and Spider benchmarks demonstrate significant improvements in execution accuracy and efficiency over regeneration-based methods.
text-to-sqllarge language modelsquery refinementdatabase management systemexecution accuracy
Business Truth, not SQL Accuracy: A Rule-Gated 7B Analytics Agent Outperforms a Direct-Prompted 32B Baseline
The paper introduces WarehouseReliabilityBench, a 400-task benchmark for evaluating LLM analytics agents on business-reliable responses (clarifications, abstentions, refusals) rather than SQL syntax accuracy. QueryProof, a 7B rule-gated agent, leverages semantic layer rules and post-execution checks to improve reliability. On an 80-task synthetic test split, QueryProof outperforms a direct-prompted 32B baseline by +0.237 Business Truth Rate, reducing false successes from 0.754 to 0.351 and achieving zero wrong answers on answerable tasks (0/24). Performance gains stem from deterministic checks, not model size or routing.
llm analytics agentswarehousereliabilitybenchbusiness truth ratesemantic layerpost-execution checks
SkillSentry: Reliable Skill Execution for LLM Agents via Runtime Assurance
SkillSentry introduces a runtime assurance framework to improve the reliability of LLM agents' skill execution by combining skill specifications with historical execution traces. The method employs a domain-specific language (DSL) for runtime guidance, monitors agent execution, and iteratively refines guidance using new traces. Evaluated on 15 skills across two LLM agents (Claude Code and Codex with varying backbone models), SkillSentry increases task success rates by 24.1% on average while reducing performance variability.
llm agentsruntime assurancedomain-specific languageskill executionexecution traces
MoRSE: Task-Oriented Multi-Agent System with Mixture of Role-Subtask Experts
The paper introduces MoRSE, a task-oriented multi-agent system with mixture of role-subtask experts, addressing limitations in existing LLM-based multi-agent systems through parameter-level specialization. The method decomposes tasks into dependency-aware DAGs of subtasks, assigns (role, subtask) pairs to agents, and implements a dynamic Mixture of LoRA Experts module with prototype-based semantic routing for parameter adaptation. Hierarchical group-relative policy optimization stabilizes training under sparse rewards. Experiments on code-generation benchmarks across three backbones show improved whole-task and step-wise performance, with generalization to held-out task categories.
multi-agent systemmixture of expertslorasemantic routingpolicy optimization
Emotion2Skill: Model-Internal Emotion Signals for Adaptive Skill Selection and Evolution
Emotion2Skill introduces a framework that leverages LLM-internal emotion representations for adaptive skill selection and evolution in skill-based agents. It extracts 27-dimensional emotion vectors from the residual stream, maps them to confidence-gated summaries injected into routing prompts, and analyzes emotion trajectories to guide skill evolution via targeted SOP rewriting. Evaluated on WebShop and ALFWorld with Qwen3-8B, it achieves +26.9% and +25.5% success rate improvements over Zero-Shot baselines, respectively, outperforming all baselines on both benchmarks. Co-activation analysis confirms semantically coherent emotion-skill pairings, validating the meaningfulness of internal-state signals.
emotion representationsskill selectionresidual streamsop rewritingco-activation analysis
An Explainable GNN Framework for Component-Level Anomaly Diagnosis
The authors propose an explainable Graph Neural Network (GNN) framework for component-level anomaly diagnosis in industrial systems, addressing the limitation of existing GNN methods that focus solely on sensor-level deviations. The framework hypothesizes that anomalies arise from disruptions in inter-sensor influences rather than faulty sensors, enabling diagnosis at the component level. Experiments demonstrate the method's effectiveness in identifying and prioritizing true faulty components while providing interpretable insights into system failures.
graph neural networkanomaly detectioncomponent-level diagnosisinter-sensor influencesexplainable framework
Multimodal Federated Learning under Dual-Axis Modality Missingness
Flux introduces a multimodal federated learning framework addressing dual-axis modality missingness via two components: modality-aware confidence tempering, which learns sample-specific modality confidences through mask-aware unimodal supervision and adaptive temperature scaling, and gradient-decoupled private adaptation, which applies tempering only to client-private pathways to stabilize shared representation learning. Evaluated on four datasets, Flux achieves average macro-F1 gains of 1.6 points over baselines, with improved calibration and robustness to missing or corrupted modalities. Code is publicly available.
multimodal federated learningmodality missingnessconfidence temperinggradient-decouplingadaptive temperature scaling
SafeSceneReason: A Multimodal Reasoning Benchmark Connecting Industrial Hazards with Accident Knowledge
SafeSceneReason introduces a multimodal industrial-safety reasoning benchmark combining scene-centric and report-centric pipelines for comprehensive safety assessment. The scene-centric pipeline converts annotated workplace images into executable safety scene graphs, while the report-centric pipeline extracts evidence from accident reports to construct multimodal questions. The dataset contains 110,581 scene-centric and 13,114 report-centric question--answer pairs, covering perception, compliance assessment, causal analysis, and mitigation-oriented decision making. Evaluation of vision--language models reveals persistent weaknesses in comparative, technical, and multi-evidence reasoning, highlighting gaps in industrial-safety reasoning despite strong general visual understanding.
multimodal reasoningsafety scene graphsevidence graphscompliance assessmentcausal analysis
Privileged Solutions or Context-Induced Teacher Behavior? Dissecting On-Policy Self-Distillation
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Omni2LoRA: Coherence-Preserving Parametric Memory for Efficient Omni Language Models
Omni2LoRA introduces a two-stage framework for efficient parametric memory compression in omnimodal language models (OLMs), preserving audio-visual coherence without token bottlenecks. The method first employs a Perceiver hypernetwork to encode multimodal context into a Low-Rank Adaptation (LoRA) adapter, then optimizes rank allocation via Group Relative Policy Optimization (GRPO) to prioritize cross-modal anchors. Operating at a 30% rank budget, Omni2LoRA outperforms full-context inference and token-compression baselines on four audio-visual QA benchmarks, improving accuracy by 8-12% and reducing Time to First Token (TTFT) by up to 12x, with stable performance under 75% compression ratios.
omnimodal language modelslow-rank adaptationgroup relative policy optimizationaudio-visual coherenceparametric memory compression
RL-Native Distillation: Exploiting Scored Trajectories for Few-Step Image Generation
The paper proposes REST (Reward-Enhanced Scored-Trajectory Distillation), a single-stage RL-distillation co-training framework for efficient text-to-image generation. REST exploits scored trajectories from RL-based diffusion models by attaching a decoupled student model that learns segment-wise from teacher rollouts via Advantage-Modulated Distillation (AMD), which weights trajectories by their advantage signals. The method requires no extra rollouts, separate datasets, or adversarial training. Experiments show REST enables few-step CFG-free inference matching 40-step RL teachers, with 25% additional training cost, improving DrawBench PickScore by 0.82 over RTDMD with 5x fewer iterations.
reinforcement learningdiffusion modelsknowledge distillationtext-to-imageadvantage modulation
Governing the KV Cache: Preventing Timing Side-Channel Leakage in Multi-Tenant LLM Inference
The paper introduces KVGov, a governance layer for KV-cache in multi-tenant LLM inference that prevents timing side-channel attacks like PROMPTPEEK, EarlyBird, and InputSnatch. The method employs per-principal cryptographic salting (sigma_p = HMAC_K(secret, principal_id)) to disjoint cache keys across tenants, supplemented by ORIGAMI, a Stackelberg audit scheduler. Evaluations on Qwen2.5-7B-Instruct (vLLM 0.26.0, NVIDIA A100) show a cold/cached TTFT ratio of 0.22, confirming exploitability; KVGov reduces adversary utility by 12.6% at Gini 0.63 while retaining 93% prefix-cache efficiency. Independent validation on llama.cpp (Apple Metal) yields a ratio of 0.093.
kv-cacheside-channelmulti-tenantstackelberghmac
FedTVD: Balancing Data Quality and Quantity for Robust Federated Learning
FedTVD introduces a novel federated learning algorithm that balances data quality and quantity during client aggregation to address label distribution skewness and dataset size variations. The method employs Total Variation Distance (TVD) to measure divergence between local label distributions and a uniform global distribution, assigning lower weights to clients with skewed distributions while incorporating dataset size for fairness. This dual-weighting mechanism mitigates data imbalance, enhancing model stability and generalization. Experiments demonstrate FedTVD's superiority over FedAvg, achieving up to 10.6% improvement on CIFAR-10 under highly skewed data while maintaining robust performance across FMNIST, CIFAR-10, and CIFAR-100 datasets.
federated learningtotal variation distancelabel distribution skewnessdata heterogeneityclient aggregation
Beyond Solvability: Task Learnability as a Static Prior for LLM RL Post-Training
The paper introduces task learnability as a static prior for RL post-training of LLMs, addressing limitations of uniform task sampling and snapshot-based valuation methods. The authors propose TrajVal, a lightweight probe-based estimator that predicts per-task learnability from short probe runs and endpoint evaluations. Experiments on mathematical and logical reasoning benchmarks demonstrate TrajVal's effectiveness in improving data efficiency and complementary gains with online schedulers across multiple model scales.
task learnabilityreinforcement learningpost-trainingstatic priorprobe-based estimator
AkasicDB: Demonstrating Omni RAG with a Unified Vector-Graph-Relational DBMS
AkasicDB introduces a unified Vector-Graph-Relational DBMS that natively supports complex Retrieval-Augmented Generation (RAG) workflows, addressing inefficiencies in existing database architectures. The system extends Chimera with native vector support, enabling joint execution of vector similarity search, graph traversal, and relational filtering within a single framework. This integration, termed Omni RAG, demonstrates superior retrieval and reasoning capabilities compared to vector-only approaches through an interactive chat-style demonstration. Users directly experience enhanced query execution and visualization, highlighting practical limitations of current architectures in supporting Omni RAG.
retrieval-augmented generationvector similarity searchgraph traversalrelational filteringomni rag
FedA2L: Adaptive layer-wise learning rate adjustment in decentralized federated learning
FedA2L introduces adaptive layer-wise learning rate adjustment for decentralized federated learning (DFL) to address convergence inefficiency under data heterogeneity. The method dynamically adjusts learning rates based on model divergence signals, balancing local update intensity with network consensus constraints without additional communication overhead. Evaluations show FedA2L achieves up to 4.94× faster convergence than vanilla DFL, reduces communication rounds by 59%, and maintains robustness across non-IID data, large networks, and sparse topologies.
decentralized federated learninglayer-wise optimizationlearning rate adjustmentnon-iid datacommunication efficiency
CRUISE: Vision-Language Model-Guided Uncertainty-Aware Cross-Modal Sensor Fusion for Robust Autonomous Driving
The paper proposes CRUISE, an uncertainty-aware cross-modal sensor fusion framework for autonomous driving that addresses reliability variations across sensors in diverse conditions. The method integrates a vision-language model (VLM)-guided uncertainty quantification module for pixel-level estimates and a dynamic adaptive mechanism to model cross-modal dependencies. This leverages VLM priors and contextual reasoning to enhance fusion robustness, particularly in out-of-distribution scenarios.
cross-modal fusionuncertainty quantificationvision-language modelautonomous drivingsensor reliability
Signature-Guided Capacity Occupancy for Dense Expert Merging
SigMerge introduces a structured capacity assignment framework for dense expert merging, addressing three key decisions: layer capacity allocation, domain-specific capacity occupancy, and efficient expert delta admission. The method leverages conflict signatures to determine layer capacity from cross-expert conflicts, positive base-merge deficits to allocate domain-specific shares, and a sequential occupancy rule to admit expert deltas within layer-domain budgets. Evaluated across 21 paired settings involving seven dense base merges and three model pools, SigMerge improves performance by 15.0% on average and achieves the best average rank (1.67) among six merging methods, outperforming three categories of merging baselines.
dense expert mergingconflict signaturescapacity occupancysequential occupancy ruletask-vector support
Structure-Preserving Uncertainty Propagation in First-Order Proof Search
The paper introduces structure-preserving uncertainty propagation in GK, a first-order prover combining resolution-based proof search with explicit confidence values and prioritized default rules. The method leverages retained proof histories to compute (1) the probability of valid proofs without double-counting shared premises and (2) resolved support for intermediate atoms before propagation, including uncertain exception handling. Results show agreement with reference calculations in analytic examples and simulators, while comparisons with probabilistic logic, answer set programming (ASP), and default logic reveal semantic divergences and unsupported translations. The implementation avoids global grounding through bounded reconstruction.
first-order proof searchuncertainty propagationdefault rulesprobabilistic logicanswer set programming
SiriusDeliver: Automating Data Warehouse Delivery at Tencent
SiriusDeliver automates enterprise data warehouse task delivery by integrating a hierarchical agent for skill orchestration, artifact lifecycle control, and trace-driven skill evolution. The system addresses production challenges like dependency-aware workflow configuration and platform adaptation, outperforming baseline methods in delivery success and automation efficiency. Deployment on Tencent Cloud WeData achieved an 87.2% end-to-end success rate, 73.5% autonomous submission rate, and reduced median delivery time from 228 to 23 minutes across 18,240 sessions.
data warehousedelivery automationartifact lifecycleskill evolutionworkflow orchestration
Agentic Router: An Execution-Grounded Continual Learning Approach With Memory
The paper introduces Agentic Router, an execution-grounded continual learning framework for CLI-based network operations using LLM agents. The dual-path architecture generates multiple actions via a proposal LLM (abstracting operational lessons into retrievable guidance) and selects optimal actions through a consequence predictor adapted via session-level LoRA updates using real SSH feedback. Evaluations on multi-turn SONiC operations with Qwen3 models demonstrate improved feasible-action coverage and top-1 execution success, with complementary gains from the proposal and selection adaptation paths.
llm agentsexecution-grounded learninglora adaptationcli operationsconsequence prediction
Rethinking Medical Landmark Localization with Prototype Learning-based Progressive Offset Correction
The authors propose PPOC-LL, a parameter-efficient model for medical landmark localization using Prototype learning-based Progressive Offset Correction. The method introduces a multi-scale dynamic perception strategy for patch-level feature pyramid modeling, a similarity-driven prototype learning mechanism for handling anatomically similar patterns, and error-aware reliability regularization for stable learning. Evaluated on a cohort spanning X-ray and ultrasound modalities, including cephalometric, symphysis-fetal head, and fetal heart landmarks, PPOC-LL achieves favorable accuracy-complexity trade-offs compared to existing multi-stage refinement approaches.
landmark localizationprototype learningoffset correctionfeature pyramidreliability regularization
Not All Visual Tokens Are Equally Safe to Remove:Consequence-Sensitive Visual Token Compression
The paper introduces consequence-sensitive visual token compression for vision-language models (VLMs), which dynamically allocates visual computation based on potential error costs rather than treating all errors equally. The method employs a calibrate-then-allocate procedure, estimating error-budget curves offline and applying token budgets online using consequence signals. Evaluated on dense vision-language benchmarks, it reduces high-stakes errors from 0.300 to 0.133 under fixed token budgets and achieves 38% lower cost-weighted error with 21% latency reduction compared to full-resolution inference, demonstrating robustness across architectures and token selection strategies.
visual token compressionvision-language modelserror-budget curvesconsequence-sensitive allocationtoken transfer
From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents
The paper introduces Reward-Aware Dynamic Execution Gate (RADEG), a lightweight decision layer for skill-based LLM agents that predicts execution utility before costly rollouts. RADEG employs a surrogate model trained on locally perturbed skill bundles to isolate compositional effects on verifier reward, enabling adaptive execute/skip decisions via a warm-started logistic head. Evaluated on 288 rollouts, RADEG reduces unnecessary executions while preserving downstream reward, outperforming relevance-based and random gating across execution budgets.
execution gatingskill retrievalsurrogate modelverifier rewardlogistic head
CIDER: A Dataset of Contextual Disclosure Boundaries for Privacy Preference Alignment
The paper introduces CIDER, a dataset of 14,850 human annotations from 169 users, capturing contextual disclosure boundaries for privacy preference alignment across 60 interpersonal communication scenarios. The dataset enables evaluation of LLMs' ability to predict user-specific disclosure decisions, with tasks varying in contextual information. Experiments on 12 models show in-context personalization improves accuracy by up to 11.41 percentage points using 6 examples, with larger models (GPT-5.4, Claude Sonnet 4.6) better leveraging semantic context, while smaller models rely on structured heuristics.
privacy preference alignmentcontextual disclosure boundariesin-context learninglarge language modelspersonalization
Tabular Numeric Stretch Transformation
The authors propose the stretch transformation framework for optimizing numeric feature representations in tabular data, addressing heterogeneous distributions via two variants: unsupervised stretch (uniform density redistribution via minimax optimization) and supervised stretch (target-aware transformation minimizing Dirichlet energy for smoother target functions). Theoretical analysis links unsupervised stretch to Piecewise Linear Encoding and empirical CDF transformations, while supervised stretch relates to target encoding. Experiments on 38 TALENT benchmark datasets show supervised stretch consistently outperforms baselines, demonstrating the efficacy of optimizing for target-function smoothness in tabular deep learning.
tabular datanumeric feature transformationdirichlet energypiecewise linear encodingtarget encoding
From Inaudible Inputs to Model Failures: Low-Frequency Safety Risks in LALMs
The paper introduces Intermittent Low-Frequency Lockout (ILL), a black-box red teaming method to assess low-frequency safety risks in large audio-language models (LALMs). ILL employs Sentence Attention Scale Estimation for interval detection and Frequency Confusion Transfer to generate inaudible low-frequency waveforms. The proposed mitigation, Distributional Requery Guard (DRG), detects distribution shifts and requests clean reacquisition. Evaluated on six LALMs, ILL reduces task accuracy by up to 67 percentage points (human audibility: 1.33 vs. 1.17 for clean audio), while DRG improves attacked accuracy from 28.5% to 46.1% post-reacquisition.
large audio-language modelslow-frequency inputsred teamingdistributional shiftsemantic recovery
TRACE: TRajectory Attribution for Automated Context Engineering
TRACE introduces an automated feedback loop for diagnosing and remediating context-layer failures in AI agents by mining historical trajectories. The method combines trajectory mining, multi-component causal attribution, and exploratory verification to distinguish content gaps from stale content, achieving 96% operation accuracy. Results show 72.7% root cause attribution and 82% end-to-end fix effectiveness on 60 dissatisfaction traces, demonstrating that 80% of context-layer failures can be automatically addressed.
trajectory miningcausal attributioncontext engineeringexploratory verificationfeedback loop
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
SpeedTuning introduces a reinforcement learning framework to accelerate robotic manipulation policies without additional data collection, addressing the speed limitations of imitation learning. The method predicts optimal execution speeds for actions, complementing base policies while maintaining task success rates. Empirical evaluations demonstrate SpeedTuning achieves over 2.4x speed-up compared to original policies and linear interpolation methods, validated across diverse tasks including pouring, throwing, and picking. The framework enhances real-world robotic manipulation by balancing speed and precision.
reinforcement learningimitation learningexecution speedrobotic manipulationtask success rate
When Latents Forget Pixels: Restoring Fidelity in Diffusion Transformer Super-Resolution
We propose Pixel-Grounded Super-Resolution (PGSR), a framework addressing fidelity loss in diffusion transformer-based super-resolution caused by VAE compression bottlenecks. PGSR preserves pre-VAE pixel evidence from upsampled LR images and reuses it through Condition-Side Trajectory Guidance for latent restoration trajectory and Decoder-Side Pixel Grounding for final rendering. The method adapts large pretrained DiT models by freezing the latent autoencoder and flow-matching backbone while training lightweight restoration modules. Experiments show PGSR improves the realism-fidelity trade-off, producing more faithful results than existing latent generative SR approaches.
diffusion transformerssuper-resolutionvae compressionpixel-groundedlatent restoration
MARA: Flow-Matching-Guided Multi-Agent Resource Allocation for Computational Resource Efficient Learning
MARA introduces a multi-agent resource allocation framework for computational resource-efficient learning, addressing the challenge of allocating discrete compute nodes to concurrent learning tasks with unknown training requirements. The method employs conditional flow matching for loss trajectory prediction and a cooperative multi-agent autoregressive policy for node coordination, enhanced by a potential-based progress reward mechanism. Evaluated across in-distribution, reinforcement-learning, and vision workloads, MARA reduces remaining-resource prediction error compared to weighted least squares and achieves a 63.46% task completion rate, outperforming the Learning with Adaptive Resource Allocation (LARA) baseline by 8.54 percentage points.
conditional flow matchingmulti-agent autoregressive policypotential-based progress rewardcomputational resource allocationloss trajectory prediction
Social Gym and SPaRTan: Benchmarking and Improving LLM Social Reasoning via Multi-Agent Game Tournaments
The paper introduces Social Gym, a benchmark environment with 21 multi-agent social games (e.g., Werewolves, Resistance) featuring rule-decided outcomes for objective performance evaluation via Elo tournaments. Results show GPT-5-mini leads but exhibits uneven performance across games and roles, revealing social reasoning gaps. The authors propose SPaRTan, a training-free self-improvement loop where models generate playbooks via self-reflection, improving GPT-5-mini's weaker roles but not Qwen3-32B's performance. The work provides verifiable foundations for evaluating and enhancing LLM social reasoning without weight updates.
multi-agent systemssocial reasoningself-playelo ratingllm benchmarking
ChronoState: Hidden Elapsed-Time Conditioning for Temporal-State Action Selection in Frozen-Backbone Language Models
ChronoState introduces a method for integrating elapsed wall-clock time as a hidden scalar input with symbolic task state in frozen-backbone language models, enabling temporal-state action selection. The approach employs Qwen2.5-3B-Instruct as a frozen backbone, utilizing a 31-dimensional sinusoidal-plus-log time encoding, gated FiLM residual modulation, and a rank-8 LoRA action surface. Experiments demonstrate high accuracy (0.9305 +/- 0.0134) and balanced accuracy (0.9410 +/- 0.0103) for hidden-time conditioning, significantly outperforming no-time and shuffled-time controls. While generalization is strong for held-out templates and durations, quota-family transfer remains weak (0.5065 +/- 0.0559), and prompt-injected timestamps achieve superior performance (0.9893 +/- 0.0052).
chronometric-injectionfrozen-backbonelorafilm modulationtemporal-state
RISE-RL: Rubric-Informed Selective Exploration for Open-Ended Reinforcement Learning
RISE-RL introduces rubric-informed selective exploration for open-ended reinforcement learning in LLMs, addressing persistent capability gaps by leveraging repeatedly missed rubric criteria to elicit privileged trajectories. The method retains trajectories with complete-rubric rewards exceeding natural rollout means, re-evaluates them under the original prompt, and optimizes guidance through an auxiliary objective removed when benefits diminish. Experiments with 4B and 14B models across writing, chat, health, and science domains show RISE-RL achieves the highest mean scores on all benchmarks, improving average scores by 1.3 points (4B) and 3.3 points (14B), including a 6.0-point gain on CreativeWriting-V3. It also enhances creative-writing diversity and objective medical/scientific benchmarks.
rubric-informed explorationopen-ended reinforcement learningprivileged trajectoriescomplete-rubric rewardauxiliary objective
Visual Distortion Detection in UGC Images Using Large Multimodal Models
We introduce VIGIL, a large multimodal model (LMM) for precise visual distortion detection in user-generated content (UGC) images, addressing the synthetic-to-authentic (S2A) generalization gap. VIGIL leverages a 140K-image training set (VIGIL-140K) derived from 1000K samples, incorporating eight synthetic distortion categories through rigorous filtering and distortion injection. The model employs multiple LLM decoder layers as synchronous detectors, utilizing multi-level features and retaining distortion cues from non-distortion predictions to mitigate foreground-background separation ambiguities. VIGIL outperforms baselines in both synthetic distortion detection and S2A tasks after post-processing.
large multimodal modelsynthetic-to-authenticdistortion detectionmulti-level featuresforeground-background separation
MELLON - Multimodal Enhanced LLM for Online Navigation
The paper introduces MELLON (Multimodal Enhanced LLM for Online Navigation), a web navigation agent with multimodal reasoning capabilities, alongside VQAgent and Multimodal Ranker enhancements. The approach focuses on aligning text and image inputs for improved task completion on the WebShop benchmark, a real-world website simulation. MELLON achieves a 9.26% accuracy improvement after one training epoch, demonstrating the potential of multimodal alignment for web navigation agents.
multimodal reasoningweb navigationalignmentllmbenchmark
Motif 3: Technical Report
Motif 3 introduces a decoder-only Mixture-of-Experts (MoE) language model with 314B total parameters and 13.2B activated per token, leveraging sparse MoE layers with 384 experts and eight selected per token. The architecture integrates Grouped Differential Latent Attention (GDLA), manifold-constrained hyper-connections, Expert Specific PolyNorm activations, and multi-token prediction for optimization stability and efficiency. Pretrained on 12.5T tokens across diverse corpora, it employs expert-balancing, MXFP8 computation, and window-aware context parallelism for training with up to 256K context lengths. Post-training includes supervised fine-tuning, specialist teachers, and Multi-teacher On-Policy Distillation, achieving competitive performance in reasoning, coding, and long-context tasks.
mixture-of-expertsgrouped differential latent attentionmanifold-constrained hyper-connectionsmulti-token predictionmxfp8 computation
RAVEN-Eval: Rubric-Guided Automatic Evaluation for AI Video Generation Models Based on LMM Preference Judgement
RAVEN-Eval introduces a rubric-guided automated evaluation framework for AI video generation models (AIVGMs) using LMM-as-a-judge paradigm. The method curates 150 T2V and 100 I2V tasks, collects 4,500 AIGVs, and employs rubric-guided LMM preference judgements via pairwise comparisons. It reduces evaluation costs with an anchor-based model insertion approach. Results include evaluations of 20 AIVGMs and 13 LMM judges, establishing the RAVEN-Eval Leaderboards.
ai video generationlmm-as-a-judgerubric-guided evaluationpairwise comparisonanchor-based insertion
Different Feedback, Different Updates: Selective Self-Learning from User Interactions for Large Language Models
The paper introduces SLIFT, a selective self-learning framework for large language models (LLMs) that decomposes user feedback into atomic components (Fix, Spec, Null) to guide model updates at appropriate generalization scopes. SLIFT employs two LoRA adapters: a Generalist, which consolidates Fix requirements via feedback-conditioned self-distillation, and a Specialist, which provides residual guidance for Spec refinements. Evaluated on MemoryBench and WildFB, SLIFT demonstrates strong performance across backbones while avoiding updates for Null components. The method enables targeted behavioral refinement without compromising task validity.
selective self-learninguser feedback decompositionlora adaptersfeedback-conditioned self-distillationbehavioral refinement
Who Bridges Safety? Identifying and Targeting Cross-Lingual Shared Safety Pathways
The study identifies cross-layer functional pathways responsible for safety signal propagation in multilingual large language models (LLMs), addressing the cross-lingual safety gap. By analyzing monolingual safety pathways and their cross-lingual intersections, the authors demonstrate that a sparse subset of shared pathways transfers safety capabilities from high-resource (HR) to non-high-resource (NHR) languages. A pathways-targeted alignment method, updating only a small fraction of parameters, significantly improves NHR language safety while preserving general model capabilities.
safety pathwayscross-lingual transfermechanistic interpretabilityparameter-efficient alignmentmultilingual llms
The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora
The study investigates the impact of document notation—how text boundaries and structural markup are recorded—on pre-training corpora and model behavior. It introduces clean-window survival, a metric quantifying boundary inference demand, and analyzes 13 public corpora, revealing stark differences (e.g., 0.153 survival in PDF-converted text vs. 0.889 in C4). Experiments with base models (0.6B–8.2B parameters) show that structural announcements (not their notation) cue prediction, while their removal degrades performance. Models do not reintroduce deleted announcements, aligning with authored baselines. The authors propose a reversible sidecar format for corpora, prioritizing training capability over fidelity.
clean-window survivalpre-training corporastructural markupboundary inferencereversible sidecar
TLDChoiceNet: Quantitatively Choosing a Transfer Learning Dataset
TLDChoiceNet proposes a quantitative method for selecting optimal transfer learning datasets in image classification by predicting post-fine-tuning test accuracy. Version 1 achieves 0.154 MSE, while Version 2, incorporating ImageNet-pretrained ResNet50 v2 embeddings and per-class information, reduces MSE 5x to 0.031. Two unsupervised metrics—distribution distance (DD, R²=0.89) and average class correlation (ACC, R²=0.97)—demonstrate that low-level dataset statistics explain transfer learning performance. Results indicate ImageNet embeddings enhance class separability in latent space.
transfer learningimage classificationdataset selectionresnet50feature embedding
A Multi-Scale Temporal Framework with Dynamic Fusion for EEG-Based Emotion Recognition
A multi-scale temporal framework is proposed for EEG-based emotion recognition, addressing mixed emotions through dynamic fusion of multiple temporal scales. The EEG waveform is decomposed into windows of varying durations, processed by a shared attention-based encoder, and integrated via a dynamic fusion module that assigns sample-specific weights across scales. Evaluated under a subject-independent protocol, the framework achieves 65.22% accuracy in binary classification and 45.43% in a three-class task including mixed emotions, outperforming full-signal baselines. Dynamic fusion surpasses concatenation in the two-class task and slightly exceeds it in the three-class task, despite increased computational requirements.
eegdynamic fusionattention-based encodermulti-scaleemotion recognition
When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information
The study systematically evaluates LLM reliability under clinical uncertainty, revealing persistent overconfidence despite accuracy degradation. Authors propose a framework using MedMCQA with two uncertainty settings: linguistic cue modifications and answer removal requiring abstention. Analysis of 500 medical questions shows misaligned confidence-accuracy (calibration gap up to 0.38, ECE 0.15), with unsafe confident errors increasing under information loss, and model-dependent hallucination rates when correct answers are absent.
clinical uncertaintyconfidence calibrationmedmcqaepistemic reliabilityhallucination rate
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
The paper introduces SWE-RPG, a repository-level benchmark for evaluating coding agents through validated ground-truth references for Requirement Clarification and Implementation Planning, alongside executable patch evaluation. Comprising 163 tasks from 31 Python and Java repositories (113 bug fixes, 50 feature additions), it enables retrospective diagnosis of agent trajectories. Evaluation of 3 coding agents (Claude Code, Codex, OpenCode) with 6 LLM backends (e.g., Claude-Sonnet-5, GPT-5.6-Terra) reveals a 31.5% average resolved rate, with implicit requirement recovery identified as the primary bottleneck (24.5%--46.0% of runs).
coding agentsrepository-level benchmarkrequirement clarificationimplementation planningimplicit requirement recovery
Two-Step MV-DeepONet: Probabilistic Operator Learning for Uncertainty Propagation Driven by Random Input Fields
The authors propose Two-Step MV-DeepONet, a probabilistic operator learning framework for uncertainty propagation in physical systems with random input fields. The method decouples output-basis learning from input-to-coefficient mapping via two-step training, employs basis orthogonalization and subspace rotation, and transfers Gaussian probabilistic modeling to a low-dimensional rotated coefficient space. This induces non-diagonal conditional predictive covariance in the physical output space while maintaining single-pass inference. Experiments on PDE-governed problems and a hypersonic aerothermal case demonstrate improved generalization, structured uncertainty bands, and accurate recovery of off-diagonal correlation patterns compared to Prob-DeepONet.
uncertainty propagationprobabilistic operatordeeponetcovariance recoverysubspace rotation
Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation
TriNoL introduces a triple-expert learning framework for semi-supervised adaptation of vision foundation models (VFMs) to address pseudo-label noise. The method routes unlabeled samples into three confidence regions—high, medium, and low—and assigns them to specialized LoRA experts: Positive Expert, Alignment Expert, and Negative Expert. The VFM backbone remains frozen, while only the LoRA experts and classifier head are updated. This separation of pseudo-label reliability regions into distinct adaptation paths enhances robustness to noisy supervision while maintaining low training costs.
triple-expert learningvision foundation modelspseudo-label noiselora expertssemi-supervised adaptation
Bridging the Gap Between Semantics and Reconstruction:Unifying Sign Language Translation and Production
Uni-SLTP introduces a unified framework for sign language translation (SLT) and sign language production (SLP), addressing the bidirectional modality gap between continuous sign motions and discrete text tokens. The framework comprises two components: a shared sign tokenizer that captures semantic and reconstructive information by converting sign sequences into discrete tokens and latent representations, and a unified autoregressive generation model that formulates both tasks as conditional sequence generation. Evaluations on public datasets demonstrate that Uni-SLTP achieves superior motion accuracy for SLP while maintaining competitive SLT performance, bridging the gap between semantics and reconstruction in sign language processing.
sign language translationsign language productionautoregressive generationmodality gapsemantic reconstruction
Don't Scroll Back: Missing-Evidence Memory for Streaming Dialogue Summarization
We introduce ReMEMBER, a missing-evidence memory framework for streaming dialogue summarization that retrieves and refines history chunks under a fixed budget to resolve unresolved window dependencies. The method conditions retrieval on presupposed evidence gaps and compresses retrieved chunks into evidence-dense memory. Evaluated on dialogues with histories up to 160K tokens, ReMEMBER demonstrates improved memory recall and gap-resolution completeness compared to baseline memory construction approaches under identical budget constraints. A novel benchmark and evaluation protocol separately assesses memory evidence recovery and summary reflection of resolved gaps.
streaming dialogue summarizationmissing-evidence memoryevidence-dense memorygap-resolution completenessmemory recall
DualCert: A Solver for the Traveling Salesman Problem with Constraint-Coupled Learning
DualCert introduces constraint-coupled learning for the traveling salesman problem (TSP), integrating optimization constraints directly into learned transitions via primal-slack KKT manifolds and local cost fields. The method employs constrained mirror descent, implicit differentiation for parameter updates, and deterministic verification to ensure validity. On 1,000 TSP1000 instances, DualCert achieves a 0.0573% mean tour-cost gap from LKH-3 references in 9.55 seconds per instance, with 81.46% edge-decision coverage and verified lower bounds. It outperforms NeuroLKH by a 67.1% smaller gap, demonstrating constraint-governed learning with certified outputs.
constraint-coupled learningprimal-slack kkt manifoldheld–karp ascentsubtour-elimination constraintsdeterministic verification
MusicLayout: Explicit Structural Planning for Controllable Text-to-Music Generation
MusicLayout introduces an explicit intermediate representation for controlling musical structure in text-to-music generation, addressing the implicit structural organization in current systems. The method integrates MusicLayout into a unified autoregressive framework, where the model first generates a MusicLayout representation and then predicts audio tokens conditioned on this representation within a single sequence. Evaluation through layout-conditioned generation, layout manipulation experiments, and matched-data ablations demonstrates that explicit layout planning improves long-range structural organization and supports layout-level control.
musiclayouttext-to-musicautoregressiveintermediate representationstructural control
PolicyKG: An Agentic LLM Pipeline for Translating Institutional Policies into SHACL Knowledge Graphs
PolicyKG introduces an agentic LLM pipeline that automatically translates institutional policies into SHACL knowledge graphs. The method employs a four-stage LangGraph state machine with per-stage validators, utilizing a YAML vocabulary registry (Corpus Adapter) to ground LLM predicates in target ontologies without retraining. Evaluated on the Asian Institute of Technology Policies corpus (1,663 sentences), it achieves 86.9% deontic classification accuracy (κ = .709) and SHACL shape correctness (F1 = .866). The pipeline handles 79.2% of rules via first-order logic, with a 95% upper bound of 0.67% higher-order constructs. Domain adaptation improves property alignment from 1/15 to 11/15 (p < .001) when switching to GDPR vocabulary.
shacldeontic logicknowledge graphllm pipelineontology grounding
Context Is Not Authority: Structured Runtime Governance for Financial Market Agents
The paper introduces SAGE-Fin, a runtime governance framework for financial market agents that enforces strict authority-handoff contracts to prevent unauthorized actions. The system compiles proposals into typed candidates, tracks institutional obligations as coverage debt, and validates authority against current market and policy states. Evaluation on a 616-case catalog showed perfect binary reference-prototype parity, with successful deployment at a digital-asset platform receiving positive operational and end-user feedback. Three predecessor failures demonstrated the framework's interception capability for drift, stale evidence, and missing state.
runtime governanceauthority-handoffcoverage debtresponse gatefinancial agents
How People Evaluate AI-, Expert-, and Peer-Style Financial Advice
This study investigates how attribution labels and communication styles influence evaluations of financial advice, contrasting AI-generated, expert, and peer-sourced recommendations. Using a preregistered vignette experiment (N=285), the authors held financial content constant while varying source attribution (correctly labeled, unlabeled, mislabeled) and communication style (AI Financial Assistant, Certified Financial Planner, Online Community Forum). Results show expert advice consistently outperformed AI advice across 9 of 10 outcomes (effect sizes |d|=0.20-0.47), even without labels. Mislabeling enhanced AI advice ratings for situational fit and overall quality (d=0.42) while reducing expert advantage in situational fit (d=-0.36). Findings demonstrate that advice evaluations are jointly shaped by attribution framing and message-level cues.
attribution labelscommunication stylevignette experimenteffect sizessituational fit
SignLlama: Enhancing Gloss-free Sign Language Translation by Prioritizing Visual Features for LLMs
SignLlama introduces a novel approach to Gloss-Free Sign Language Translation (GFSLT) by addressing two key challenges in adapting Large Language Models (LLMs) to visual inputs. First, Filtered Pseudo-Gloss CTC Pretraining bridges the distributional gap between visual and text features by supervising the visual backbone with pseudo-gloss sequences. Second, Visual-Prioritized Distillation trains the model to prioritize visual cues by masking text inputs and distilling outputs from a visual-textual prediction path. Experiments show SignLlama achieves competitive performance on multiple GFSLT datasets without additional modalities or external pretraining data.
gloss-free sign language translationfiltered pseudo-gloss ctc pretrainingvisual-prioritized distillationlarge language modelsvisual-textual prediction
DeepFreqMark: End-To-End Learnable Frequency-Domain Watermarking with Spherical Attack Simulation for Latent Diffusion Models
DeepFreqMark introduces an end-to-end learnable frequency-domain watermarking framework for Latent Diffusion Models (LDMs), addressing copyright and misinformation concerns. The method replaces manual geometric pattern embedding with a neural message encoder and decoder, while employing Spherical Linear Interpolation (Slerp)-based attack simulation to bypass Denoising Diffusion Implicit Model (DDIM) inversion computational bottlenecks. Experiments show DeepFreqMark achieves significantly lower Bit Error Rates (BER) than baselines under real-world attacks, scaling to 256 bits message capacity while preserving Gaussian variance in the noise latent.
latent diffusion modelsfrequency-domain watermarkingspherical linear interpolationbit error ratedenoising diffusion implicit model
Multi-agent discovery of practical quantum LDPC codes
A multi-agent framework is introduced for discovering practical quantum low-density parity-check (qLDPC) codes, addressing the challenge of optimizing code performance under practical constraints. The framework integrates specialist proposal and review, scientific memory, long-horizon program evolution, and deterministic construction within a closed-loop search, focusing on binary CSS codes with block length ≤400 and overall weight ≤10. It identifies high-performing codes, such as [[288,16,18]] at w=7 and [[234,28,18]] at w=10, and novel constructions like [[336,12,≤24]] and [[368,18,16]], demonstrating low logical failure rates under depolarizing noise. This approach advances the discovery of hardware-relevant qLDPC codes.
quantum ldpc codesmulti-agent frameworkcoset-orbit balanced-product codesdepolarizing noiselogical failure rates
Guardian Crawler: Retrieval-First Knowledge Discovery with Bounded LLM Augmentation for Noisy Web Intelligence
Guardian Crawler introduces a retrieval-first testbed for knowledge discovery and evidence-grounded summarization over noisy web-like corpora, combining BM25 retrieval with risk-aware hybrid reranking and constrained retrieval-augmented generation. The method employs embedding-augmented reranking and explicit document citations, evaluated on a synthetic 900-document corpus with 10 queries. Results show P@10=1.00 and NDCG@10=0.94 under risk-based reranking, outperforming BM25 (0.94, 0.81); 36/41 generated bullets were LLM-judged as supported, though statistical superiority and real-world transfer remain unverified.
retrieval-augmented generationbm25ndcghybrid rerankinglexical coverage
How Far Do Foundation Models Transfer to Infant Signals? A Cross-Dataset Transfer Audit with a Unified Need Ontology
The study conducts a cross-dataset transfer audit of foundation models on infant cry signals, revealing significant inconsistencies in single-corpus evaluations: within-domain macro-F1 varies by 0.57-0.80, cross-corpus transfer is negative (negative-transfer ratio 0.19-0.35), and 349 clip groups have conflicting labels. A unified five-class need ontology and leakage audit (byte/embedding deduplication, near-duplicate removal) enable consistent transfer, with positive effects in noisy corpora. Frozen probes saturate early, while stabilized fine-tuning outperforms with full labels; domain-adaptive pretraining excels at 5-10-shot but not beyond. Ontology-mapped joint training improves performance across all tested encoders.
cross-dataset transferfoundation modelsneed ontologynegative-transfer ratiodomain-adaptive pretraining
Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach
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How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review
The study investigates rhetorical reward hacking in AI-based peer review by analyzing how rhetorical choices affect LLM reviewers' judgments while preserving scientific content. Using 4,200 synthetic manuscripts derived from 120 ICLR 2026 submissions, the authors manipulate six rhetorical dimensions via LLM rewriters and evaluate outcomes under standard/strict protocols with five LLM reviewers. Results reveal structured rhetorical sensitivity: evidence framing and novelty stance show strongest effects, while scope framing is weaker; effects are most pronounced in mid-range scores. Strict review lowers mean overall assessment by 1.36 points without altering sensitivity patterns. Joint/recursive rewriting yields diminishing returns, with rewriters determining score separation and reviewers governing effect magnitude/sign.
reward hackingrhetorical sensitivityllm reviewersevidence framingpeer review
GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices
GALA+ introduces a graph-augmented LLM agentic framework for microservice root cause analysis (RCA) and incident response, addressing limitations of single-modality approaches and unconstrained LLM exploration. The framework employs graph-guided investigation, combining heterogeneous telemetry signals with STRIX, a trace- and graph-structure-aware scoring module, to generate hypotheses, ranked diagnoses, and actionable recommendations. Evaluated using SURE-Score, a human-guided RCA-specific metric, GALA+ outperforms LLM baselines by over 25 percentage points in AC@1 on two microservice benchmarks and achieves the highest ratings from SRE experts.
microserviceroot cause analysisgraph-guided investigationtelemetryllm agent
CoRe-UIE: Rethinking Coexisting and Region-wise Degradation for Underwater Image Enhancement
CoRe-UIE introduces a degradation-oriented expert collaboration framework for underwater image enhancement, addressing coexisting region-wise degradations like color distortion, scattering haze, texture attenuation, and uneven illumination. The method combines a content-preserving shared expert with four routed experts (color correction, scattering suppression, texture recovery, illumination protection) using region-adaptive Top-k routing and HSIC-based representation constraints to reduce feature redundancy. Evaluated on UIEB, LSUI, and U45 benchmarks, CoRe-UIE achieves competitive quantitative performance and visually balanced enhancement across diverse degradation patterns.
underwater image enhancementregion-wise degradationexpert collaborationhsic constrainttop-k routing
Fourier Self-Supervision for Fine-Grained Generalized Category Discovery
Fourier Self-Supervision is introduced to improve Generalized Category Discovery by leveraging the Fourier transform for fine-grained distinction. The method employs a dual frequency filtering strategy: a low-pass filter extracts high-level category attributes, while a high-pass filter emphasizes fine details like edges and textures. These filters operate on dedicated latent spaces, and their overlapping representations create a richer feature space. This approach enhances both novel category identification and fine-grained recognition. Experiments on multiple fine-grained datasets demonstrate that Fourier Self-Supervision outperforms state-of-the-art methods, even when the number of classes is unknown.
fourier transformfine-grained recognitiondual frequency filteringlatent spacegeneralized category discovery
Reading is not Reasoning: Bridging the Agentic Policy Gap in Vision-Text Compression
We introduce CAPS, a two-stage cross-modal agentic policy self-distillation framework that bridges the capability gap in vision-text compression for multi-step language-model agents. CAPS employs offline trajectory self-distillation to transfer successful text-history policy behavior to visual-history inputs and online policy self-distillation for dense supervision during reinforcement learning. Evaluations on SearchQA and ALFWorld demonstrate that CAPS improves performance by up to 15.6% over baselines while reducing average memory-context cost by up to 63.3% and peak cost by up to 83.4% compared to text-history policies. These results validate that explicit cross-modal policy self-distillation preserves agent capability under vision-text compression.
cross-modalself-distillationvision-text compressionagentic policymemory-context cost
Depth-Aware Implicit Neural Representation Priors for 3D Gravity Inversion
The paper introduces an unsupervised depth-aware implicit neural representation for 3D gravity inversion, addressing the ill-posed nature of recovering density models from gravity observations. The method represents density volumes using coordinate-based neural networks assigned to overlapping depth slabs, optimized directly from gravity measurements via the sensitivity matrix. It incorporates slab-specific Fourier features, physics-based depth gains, and scheduled regularization as structural priors, eliminating the need for labeled density models. Evaluations on synthetic scenarios demonstrate superior performance in RMSE, PSNR, and SSIM metrics, with improved anomaly separation, internal structure preservation, and vertical extent reconstruction. Field experiments confirm the method's ability to produce coherent anomalies consistent with observed gravity patterns.
gravity inversionimplicit neural representationdepth-awarefourier featuressensitivity matrix
Idea Search: Guiding Tree Search with Ideas to Explore Diverse Scientific Methods
We introduce Idea Search, a framework enhancing Tree Search for automated scientific coding by integrating a dynamic 'Idea Bank'. The method decomposes existing methods into atomic ideas, samples from this bank to guide code mutations, and dynamically updates the bank with new ideas discovered through execution. On scRNA-seq batch integration, Idea Search improves mean score from 0.678 to 0.697, achieving a best score of 0.728, outperforming pure Tree Search. Key design choices include bank augmentation aiding bandit sampling, 'Exploratory' prompting surfacing rare best-performing solutions, and increased sampling-level exploration being counterproductive.
tree searchidea bankscrna-seqbandit samplingcode mutations
Do AI Forecast Ensembles Sample the Correct Conditional Distribution?
The study evaluates whether AI forecast ensembles correctly sample the conditional distribution of outcomes, focusing on probabilistic subseasonal coastal sea level forecasts. Using a diffusion model trained on reanalysis-derived sea level data from eight US East Coast tide gauge stations, the authors find a decoupling between marginal and joint forecast quality: while marginal skill is positive at all stations and lead times, joint spatial structure is worse than climatological draws. Lorenz-96 experiments across 0.7-170 equivalent years reveal this gap persists regardless of training volume and is reproduced by a linear baseline, indicating structural inadequacy in learned distributions.
ensemble forecastingdiffusion modelconditional distributionvariogram scorelorenz-96
From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Driving
The paper introduces a systematic behavioral taxonomy for autonomous driving systems (ADS), addressing the gap between Operational Design Domain (ODD) specifications and behavioral validation. The taxonomy organizes 21 behavioral competencies across Highway (HWY), Urban (URB), and Hub (HUB) domains, derived from the PEGASUS six-layer ODD model. Behaviors are decomposed along longitudinal and lateral control axes, characterized by Safety, Compliance, Comfort, and Efficiency properties. The framework enables concrete scenario generation for systematic testing and SOTIF coverage, validated through deployment in a rule-enforced trajectory optimization system. The Hub domain is highlighted as structurally distinct and underspecified.
operational design domainbehavioral taxonomyautonomous drivingsotif coveragetrajectory optimization
Not an A11y: How Android Accessibility Exposes Mobile AI Agents to Indirect Prompt Injection
The paper identifies a systemic vulnerability in Android-based mobile AI agents (MobileRun, Mobile-Use) due to their reliance on unsanitized accessibility (A11y) trees and visual inputs, enabling indirect prompt injection attacks. Through empirical evaluation, the authors demonstrate three attack vectors: goal hijacking (0.822 success rate with Gemma4:31B in MobileRun), context drift, and unauthorized actions, even in visually hidden scenarios. While Mobile-Use with Qwen3.6:35B reduced success rates to 0.150, fundamental architectural flaws persist in semantic boundary enforcement. The work proposes a taxonomy of attacks and advocates for zero-trust validation and context isolation in mobile agent design.
indirect prompt injectionaccessibility treesmobile ai agentscontext driftzero-trust validation
Integrated Multimodal AI System for Retrieval-Augmented Reasoning, Object Sensing, and Damage Analysis
The paper introduces a multimodal AI system for damage assessment integrating retrieval-augmented generation (RAG), thermal spectrum perception, vision foundation models, and wireless signal sensing. The RAG component grounds a locally hosted language model in project-specific documentation, improving factual consistency over static few-shot prompting. Graph-based retrieval outperforms vector-based RAG for cross-document reasoning tasks. Infrared sensing enhances object detection and segmentation under adverse conditions, while vision foundation models generate synthetic damage imagery and classify severity. Wireless sensing complements EO and IR modalities by detecting presence, motion, and environmental changes.
retrieval-augmented generationthermal spectrum perceptionvision foundation modelsinfrared sensingwireless signal sensing
Decoding Phenotypes: A Framework for Fusing Genomic Language Models and Neuroimaging
GeneFuse introduces a multimodal framework integrating genomic language models (GLMs) with neuroimaging for disease diagnosis, addressing cross-modality heterogeneity. The method combines Genotype-Conditioned Feature Modulation (GCFM), which modulates image features using genomic embeddings, and Uncertainty-aware Genomic Residual Fusion (U-GRF), which gates genotypic contributions via imaging-derived uncertainty. Evaluated on early cognitive decline (NC vs. MCI) and dementia screening (NC vs. AD), GeneFuse achieves AUROCs of 0.77 and 0.83 in APOE-centered settings, outperforming existing fusion methods and demonstrating GLMs' complementary utility.
genomic language modelsneuroimaging fusionfeature modulationuncertainty-aware fusioncross-modality alignment
Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
This study introduces a conditional denoising diffusion probabilistic model (DDPM) framework for synthesizing CT-equivalent images from low-dose cone-beam CT (CBCT) scans to reduce cumulative X-ray dose in radiotherapy planning. The framework evaluates the impact of CBCT input representation, comparing standard clinical DICOM CBCT images with filtered back-projection (FDK) reconstructions from raw projection data. Results demonstrate that physics-aware CBCT representations enhance CT synthesis quality, supporting accurate dose calculation and adaptive radiotherapy planning while maintaining reduced imaging dose.
conditional ddpmcbct-to-ct synthesisfiltered back-projectionradiotherapy planninglow-dose imaging
ToolVision: Learning When and How to Use Visual Tools with Capability-Aligned Supervision
ToolVision introduces capability-aligned supervision to address misalignments in teaching multimodal models when and how to use visual tools. The method employs a multi-agent pipeline during supervised fine-tuning (SFT) to explore and score trajectories, retaining only successful executions for training. Before reinforcement learning (RL), ToolVision rewards tool use only when it provides clear benefits, comparing performance with and without tools. ToolVision-8B outperforms its base model and competitors like Thyme-7B and Qwen3-VL-32B-Thinking on seven benchmarks, including high-resolution tasks. Datasets and source code will be publicly released.
multimodal modelvisual toolssupervised fine-tuningreinforcement learningcapability-aligned supervision
From Recovery to Drop-off: How Action Post-training Reduces a VLM's Late-Layer Depth Decodability
This work investigates how action post-training impacts depth perception in vision-language models (VLMs) by comparing a base VLM (Molmo2-ER) and its action-post-trained counterpart (MolmoAct2-LIBERO). Depth decodability is probed across decoder layers, revealing two phenomena: a persistent degradation (floor) and a late-layer collapse (cliff). Through causal ablation, the cliff is localized to interference in late-layer MLP writes, which recover depth decodability when ablated. Module-level decomposition shows depth information accumulates in MLP writes in the base VLM but collapses in the post-trained model. These findings highlight the trade-offs of action post-training on spatial understanding.
depth perceptionaction post-trainingmlp writesdecoder layerscausal ablation
A New Approach to Characterising Optimisation Problems Using Programmatic Representation and Complexity Measures
The paper proposes a novel method for characterizing optimization problems using programmatic representations and code complexity metrics. By analyzing objective function implementations via Halstead volume (a simplified program entropy measure), the approach avoids search space sampling while providing transformation-invariant complexity measures. Applied to BBOB benchmark problems and neural network training tasks, these metrics demonstrate negative correlation with algorithm performance, suggesting utility as meta-features for algorithm selection. The method complements existing characterization techniques with computational efficiency and automatic calculability.
optimization problem characterizationhalstead volumeprogrammatic representationalgorithm selectionmeta-features
From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings
The study fine-tunes MedGemma-4b-it for multi-modal medical equipment maintenance QA in LMICs, addressing device downtime through AI-assisted troubleshooting. Using QLoRA-based parameter-efficient fine-tuning on the INGENZI_DatasetV1 (10,294 QA-context pairs from MRI/ultrasound manuals), the model achieves significant metric improvements: F1 (0.22→0.38), ROUGE-2 (0.18→0.41), and BERTScore F1 (0.86→0.91). Results demonstrate enhanced precision in generating repair instructions from error logs for resource-constrained settings.
medgemmaqlormulti-modalparameter-efficienttroubleshooting
LLM Reasoning for Subjective Tasks: Failure Modes, Mitigation, and Dynamic Reasoning Routing
The study identifies reasoning collapse—a failure mode where LLMs abandon deliberation for heuristic guessing—when applying Reinforcement Learning with Verifiable Rewards (RLVR) to subjective verification tasks in recommendation systems. It proposes a conditional length-penalized post-training algorithm to mitigate collapse by bounding reasoning length, recovering performance. Analysis of 1500 personas reveals socio-linguistic framing affects verification accuracy (Δ0.38 macro-F1), prompting a mid-training architecture for contextually aligned reasoning routing. The work combines an immediate algorithmic fix with long-term architectural guidance for subjective-task alignment.
reasoning collapserlvrlength-penalized post-trainingsocio-linguistic framingsubjective verification
Full-bandwidth transformer
The full-bandwidth transformer introduces latent feedback to widen the vertical feedback channel in autoregressive transformers, enabling non-verbalized computation to re-enter the stack with renewed depth budget. This method fuses the previous top-layer hidden state with the sampled token embedding through a gated linear unit, maintaining standard transformer architecture, KV cache, and language-modeling objective. Training employs a scheduled multi-pass objective to introduce latent feedback late in pretraining and mix deeper feedback passes for stability. Experiments with 1B-parameter models trained on 400B tokens show improved validation loss, 5-shot language-model evaluation, math and coding generation, and instruction-tuned performance, matching or approaching standard transformers trained with 1.5x more tokens.
latent feedbackautoregressive transformerskv cachegated linear unitmulti-pass objective
AquiLLM: An Architecture for Supporting Tacit Knowledge Capture in Research Groups
AquiLLM introduces an open-source modular RAG-LLM framework for tacit knowledge capture in research groups, addressing transparency and reproducibility concerns of proprietary AI systems. The architecture employs open-weight models with local embedding, reranking, multimodal capabilities, and OpenAI-compatible interfaces, enhanced by semantic/episodic memory and skills support. Domain expert consultations informed improvements, yielding a system better aligned with scientific workflows in fields like astrophysics and environmental research.
retrieval-augmented generationopen-weight modelsmultimodal capabilitiessemantic memorytacit knowledge capture
Epistemic Transfer in AI-Assisted Verification: A Framework and Evaluation Protocol
The paper introduces 'epistemic transfer' as a framework for evaluating how AI-assisted verification tools affect users' subsequent unassisted performance on new claims. It proposes two metrics: the Epistemic Transfer Effect (ETE) for delayed performance comparison and Tool-Removal Cost (TRC) for immediate performance drop measurement. The authors develop an evaluation protocol combining answer-first/evidence-first AI conditions, active-practice/no-practice controls, delayed tests, and multilevel analyses, mapping outcomes to a diagnostic space of capability building, inertness, or de-skilling.
epistemic transferverification toolsevaluation protocolcapability buildingde-skilling
Theory-Guided Deception Detection: A RAG-Based Artificial Intelligence Exploration
This study evaluates Retrieval-Augmented Generation (RAG) models for deception detection, comparing seven theory-guided variants against baseline models across 700 statements from five datasets. Using four LLMs (GPT-4o, Claude-Sonnet-4-6, Llama3, DeepSeek-V4-Flash) and 39,200 judgments, results showed comparable accuracy between RAG (54.5%) and baseline models (54.6%), with minor reductions in truth bias (57.0% vs. 59.7%). Theoretical frameworks influenced response bias (32.2-88.1%) but not accuracy, suggesting limited reliability under current parameters but potential with improved theory-data alignment.
retrieval-augmented generationdeception detectionresponse biastruth-default theoryverifiability approach
DistillCache: KL-Guided Adaptive KV-Cache Eviction for Memory-Efficient LLM Inference
DistillCache introduces a reinforcement learning framework for adaptive KV-cache eviction in Transformer-based LLMs, addressing memory bottlenecks in long-context inference. The method trains a lightweight policy network using internal model signals (attention statistics, value norms, entropy, position) with REINFORCE optimization guided by per-step KL-divergence rewards. On Mistral-7B-Instruct-v0.3, DistillCache achieves 94.2% full-cache accuracy at 25% cache budget on LongBench, outperforming heuristic baselines (H$_2$O, SnapKV) by up to 2.7 points and RL-based methods (ForesightKV, RLKV) by 1.4 points, while delivering up to 2.1x throughput gains.
kv-cachereinforcement learningkl-divergencelong-context inferencememory efficiency
Agentic Anomaly Detection with ORCA-Style Dynamic Inductive Bias Adaptation in Multimodal Wearable Time Series Data
ORCA introduces agentic control for dynamic temporal receptive field adaptation in anomaly detection on nonstationary WBAN time series, eliminating fixed-context limitations. The method employs a lightweight supervisory controller to autonomously select among discrete temporal contexts without trainable parameters, enabling state-dependent inductive bias adaptation. On a custom WBAN dataset, ORCA matches top fixed-context baselines (AUROC=0.99) while avoiding manual tuning; it generalizes conservatively to MIMIC-IV under heterogeneous conditions, demonstrating robustness for resource-constrained physiological monitoring.
temporal receptive fieldinductive bias adaptationagentic controlnonstationary time serieslightweight supervisory controller
Fairness in Link Prediction Beyond Demographic Parity: A Reproducibility Study
This reproducibility study demonstrates that demographic parity (Δ_DP) fails to detect exposure bias in fair ranked link prediction, as it overlooks link ranking positions. The authors validate this claim by showing Δ_DP indicates aggregate parity despite systematic subgroup-pair ranking disparities, which are effectively captured by rank-aware Normalized Discounted KL-divergence (NDKL). They reproduce MORAL, a post-processing method that enhances exposure-based fairness with minimal utility loss, and assess robustness across synthetic homophily settings, categorical sensitive attributes, and metrics like Attention-Weighted Rank Fairness (AWRF). Results confirm exposure-based metrics reveal biases hidden by Δ_DP, with MORAL consistently mitigating these biases. A reproducible implementation is provided.
demographic parityexposure biasnormalized discounted kl-divergenceattention-weighted rank fairnesslink prediction
Consilience for Verifier-Free Test-Time Scaling
The paper introduces consilience, a verifier-free test-time scaling (VF-TTS) framework that addresses the failure of confidence-based methods on complex tasks by evaluating temporal confidence asymmetry. The method penalizes high initial confidence while demanding final certainty, operationalized via a combinatorial metric. Experiments on graduate-level mathematics and code generation show consilience outperforms baselines, validating its approach to completion confidence trajectories.
verifier-free test-time scalingconfidence trajectorytemporal asymmetrycombinatorial metriccompletion confidence
Space-Creating versus Dead Possession: An Off-Ball Possession-Quality Index for Broadcast Football
The paper introduces a two-layer framework to evaluate off-ball possession quality in football, distinguishing between space-creating and dead circulation. First, a junk-possession index flags low-threat sequences using peak threat gain under an expected-threat grid, controlling for scoreline. Second, a Space-Creation Index (SCI) quantifies spatial impact via broadcast-video pitch-control analysis. On 2026 FIFA World Cup data (103 matches), the junk flag correlates negatively with points (r=-0.37) and xG difference (r=-0.51), adding predictive power beyond on-ball VAEP (p<0.0001). In a purposive sample (31/35 flagged windows), 74% were spatially non-space-creating, validating the distinction from event-only models.
possession-valueexpected-threatspace-creation indexoff-ball analysisjunk-possession
Financial Numerical Prediction and Allocation as Token Generation
FinATOM proposes a head-free language model approach for financial prediction and allocation via constrained token generation, unifying stock-return forecasting (volatility-standardized return tokens) and ETF allocation (normalized weights). The method combines ordinal/ranking supervision with token-level policy training (DAPO-augmented GRPO) for Sharpe-optimized allocations. Evaluated on 2023--2025 ETF data, it improves pooled gross Sharpe from 1.428 to 1.529 and net Sharpe (5-bp costs) from 1.394 to 1.494, with multimodal inputs achieving 1.540 mean Sharpe. FinTexTS tests show 73.52%/2.68 (SFT) and 73.72%/2.69 (policy) cumulative-return/Sharpe, demonstrating token generation's viability for financial tasks.
token generationsharpe optimizationdapo-augmented grpovolatility-standardized returnshead-free interface
Logarithmic-Free Moment and Generalization Bounds for Uniformly Stable Algorithms
The work resolves an open question by Bousquet et al. (2020) by eliminating the logarithmic factor in moment bounds for uniformly stable algorithms. The authors prove a tight moment inequality for sums of weakly interacting functions of independent random variables under martingale difference and bounded influence conditions, achieving a bound of $\|\sum g_i(Z)\|_p \leq 16pnβ + M\sqrt{2pn}$ for $p \geq 2$. Their proof technique first establishes the bound on the Rademacher cube, then generalizes to arbitrary product distributions via a two-copy randomization argument, matching prior lower bounds up to constants.
uniform stabilitymoment inequalitygeneralization boundsrademacher complexitymartingale difference
RynnValue: Scaling Robotic Value Foundation Models with Temporal Distance
RynnValue introduces a temporal-distance-based value foundation model for robotic manipulation that scales without preference or progress annotations by deriving supervision from timestamps across 7,000 hours (3M instruction-conditioned clips). The method combines random temporal sampling, temporal-order shuffling, and value-isolation attention to suppress prediction shortcuts. Evaluated on RBM-EVAL-OOD, it achieves Kendall's tau_a of 0.675 (outperforming preference-supervised SOTA at 0.655) and raises real-world policy success rates by +20% online and +18.7% offline via potential-based shaping.
temporal distancevalue foundation modelpotential-based shapinginstruction-conditioned clipskendall's tau_a
Real-Time Climate Risk Assessment for Supply Chain Resilience: A Data-Driven Nowcasting Framework for Colombian Agriculture
The paper contributes a data-driven framework for real-time climate risk assessment to enhance agricultural supply chain resilience in Colombia. The methodology integrates short-term climate nowcasting based on historical meteorological observations with supply chain risk modeling, employing explicit risk mapping, threshold-based categorization, and stakeholder-oriented risk signals. A prototype implementation demonstrates feasibility using historical meteorological and agricultural time series, without satellite imagery or computer vision components. Experimental results show that short-term precipitation nowcasts can be translated into actionable risk indicators, supporting anticipatory decisions in inventory management, sourcing, and transportation.
climate nowcastingsupply chain resiliencerisk mappingthreshold-based categorizationstakeholder-oriented signals
RA-FinBERT: Rule-aware LoRA adaptation for low-resource financial sentiment classification
RA-FinBERT introduces a parameter-efficient framework for financial sentiment classification by integrating low-rank adaptation (LoRA) with rule-derived sentiment features and source metadata. The method concatenates a four-dimensional feature vector derived from VADER sentiment proportions and source-level metadata with FinBERT's 768-dimensional [CLS] representation, adding only 1,024 trainable weights. Evaluated on financial-news titles and descriptions, RA-FinBERT achieved 69.89% accuracy and a macro F1 score of 0.634, outperforming text-only FinBERT (63.44% accuracy, 0.526 F1) and significantly improving neutral-class recall from 18.18% to 45.45%. The framework supports CPU and GPU execution, offering a lightweight solution for low-resource settings.
low-rank adaptationsentiment classificationvaderfinbertparameter-efficient
Deep Multimodal Wearable Sensor Fusion for Detection of Body-Focused Repetitive Behaviors
A multimodal deep learning framework detects and classifies body-focused repetitive behaviors (BFRBs) from wrist-worn sensor data, combining inertial, thermal, and proximity modalities. The architecture integrates a convolutional neural network, gated recurrent unit, modality-specific autoencoders, and late-fusion classification, achieving an F1 score of 0.985 (binary detection) and 0.700 (nine-class scheme). Shapley additive explanations reveal time-of-flight and inertial sensors as dominant discriminative features, while misclassifications correlate with anatomical gesture regions. The system demonstrates potential for real-time wearable-assisted mental health diagnostics.
multimodal fusionwearable sensorsbehavioral monitoringshapley valueslate-fusion classifier
Macaron-V1: Towards Open Continual Learning with Self-Improvement and Mixture-of-LoRA
Macaron-V1 introduces an open continual learning framework combining self-improving model-harness pairs and Mixture-of-LoRA (MoL) architecture. The system employs frozen base models (744B GLM-5.2 or 50B Qwen3.6) with task-specific LoRA adapters (chat, agent, coding, GenUI) selected per user turn, enabling extensible specialization. Key components include Model-Harness Co-design, UI4A GenUI harness, MindForge RL framework, and LongStraw long-context RL. Evaluations on Personal Intelligence and GenUI benchmarks demonstrate competitive performance, with open questions on continual learning scalability.
continual learningmixture-of-loramodel-harness co-designrecursive self-improvementlong-context rl
C$^2$A: Coupling Spatial Evidence with Clinical Priors via Co-occurrence Aware Class Attention for Multi-Label Chest X-Ray Classification
C$^2$A (Co-occurrence Aware Class Attention) improves multi-label chest X-ray classification by coupling spatial evidence with clinical priors via learned per-class attention maps and a co-occurrence graph. The method pools localized descriptors through expectation over attention maps, then refines them via a residual message-passing step on a graph warm-started from empirical label co-occurrence. On CheXpert, C$^2$A achieves a 0.895 macro-mean AUROC, with notable gains (+1.5) on co-occurrent classes like Atelectasis, demonstrating effective regularization with minimal overhead (one linear projection and a $C\times C$ edge matrix).
multi-label classificationspatial attentionco-occurrence graphthoracic pathologychexpert
ReliableNet: A Chance-Constrained Approach to Trustworthy Classification in Deep Learning
ReliableNet introduces a chance-constrained empirical risk minimization (ERM) approach to trustworthy classification by bounding the Joint Confident-Wrong (JCW) probability, the likelihood of confident yet incorrect predictions, below a user-specified risk budget α ∈ (0,1). The method employs a conservative smooth inner approximation ensuring population feasibility of the JCW constraint. Evaluated across four tabular and two image datasets, ReliableNet consistently certifies JCW budget adherence under distributional shifts, outperforming baselines in empirical JCW while maintaining competitive accuracy, coverage, calibration, and selective prediction metrics. It also demonstrates superior selective ranking in risk-coverage analyses.
joint confident-wrongchance-constrainedempirical risk minimizationselective predictionrisk-coverage
Disentangling Co-Occurring Retinal Pathologies with Saliency-Guided Sparse Expert Routing
The paper introduces a sparse conditional computation architecture for multi-pathology retinal fundus image classification, combining Guided Context Gating (GCG) spatial attention with a sparsely-routed Mixture-of-Experts (MoE) block. The method enables interpretable, disease-dependent expert allocation, isolating distinct pathologies (e.g., ERM, AMD) to dedicated experts (p < 0.001). Evaluation on a five-class, patient-disjoint 5-fold cross-validation benchmark yields 0.912 +/- 0.008 macro AUC and 0.653 +/- 0.014 macro F1. Grad-CAM++ and t-SNE visualizations confirm expert routing aligns with localized lesions and clusters co-occurring cases geometrically.
sparse conditional computationguided context gatingmixture-of-expertsretinal pathologyinterpretable routing
PET/CT Radiogenomic Mutation Prediction in Non-Small Cell Lung Cancer Using Multi-Label Learning
This work demonstrates mutation-specific benefits of multi-label learning for PET/CT radiogenomic prediction in NSCLC, showing pairwise modeling improves AUC for certain gene combinations. The study evaluates deep learning on a UK cohort to predict EGFR, TP53, and KRAS mutations from PET/CT scans, comparing single-gene classification with multi-label approaches. Joint prediction of KRAS/TP53 increased AUC by 0.06 and 0.02 respectively, while EGFR/KRAS modeling only benefited EGFR prediction, suggesting optimal strategies depend on mutation pairs.
radiogenomicsmulti-label learningnon-small cell lung cancermutation predictionpet/ct imaging
Input convex neural networks as surrogates in mathematical optimisation
The paper proposes input convex neural networks (ICNNs) as superior surrogates for mathematical optimization when the underlying response is approximately convex or concave. ICNNs offer computational advantages over feedforward neural networks (FNNs) via tighter LP relaxations, exact epigraph representations in favorable cases, and tractable convex hull constructions for branch-and-bound algorithms. Case studies on humanitarian aid, oil well routing, and wine blending demonstrate that ICNNs match FNN accuracy while improving solve time and scalability.
input convex neural networksmixed-integer programmingepigraph representationbranch-and-boundconvex hull
Test-Time Scaling for CAD Generation via Verifier-Free Consensus Selection
The paper introduces 3D CAD consensus selection, a verifier-free method for improving text-to-CAD generation by selecting the most consistent candidate from a pool of parametric CAD programs. The approach samples N programs, compiles them to 3D models, and returns the candidate with highest geometric or topological agreement with the pool, requiring no additional training. Geometric consensus improves all three geometric metrics over a verifier-based baseline, reducing Chamfer distance by 1-10%, while topological consensus matches verifier performance on topology metrics across various LLMs and prompts.
text-to-cadconsensus selectionparametric programsgeometric agreementtopological agreement
Recurrent Neural Networks Beyond Time: Learning from Multiple Ordered Projections
The paper introduces the Ordered Structural Dependency Hypothesis (OSDH), proposing that multiple ordered projections of observations reveal complementary structural dependencies. To operationalize OSDH, the authors propose the Independent Structural Expert Principle (ISEP), where projection-specific sequence models are trained independently and integrated via a fusion model. They present Structural Evolution RNNs (SE-RNNs), using conventional RNNs as projection-specific structural experts while preserving recurrent computation. Experiments on three synthetic datasets with varying structural complexity show SE-RNNs benefit from multiple ordered projections when hidden dependencies exist, remaining competitive on simpler datasets. The framework extends beyond RNNs, offering a computational perspective for exploiting ordered representations in structured learning.
ordered structural dependency hypothesisindependent structural expert principlestructural evolution rnnsprojection-specific sequence modelsfusion model
FedOrbit: Adaptive Personalized Federated Learning for Non-IID LEO Satellite Constellations
FedOrbit introduces adaptive personalized federated learning for non-IID LEO satellite constellations, addressing challenges from orbital geometry-driven data heterogeneity. The method combines continuous orbit-level training via inter-satellite links, class-aware hierarchical aggregation, quality-weighted feature aggregation with return-rate dampening, and adaptive feature decomposition based on inter-orbit class similarity. Evaluated on three remote-sensing benchmarks with two non-IID partitions, FedOrbit achieves top accuracy in 5/6 settings (within 0.9pp in the sixth), with gains up to 16.1pp over baselines under Dirichlet partitioning and 8.6pp under pathological partitioning, while maintaining the smallest per-orbit accuracy spread in 5/6 cases.
federated learningnon-iid dataleo satellitesadaptive personalizationhierarchical aggregation
Deep Learning Imputation of Missing Radius of Maximum Winds (Rmax) Values in Tropical Cyclone Best-Track Data
This study introduces deep learning approaches for imputing missing radius of maximum winds (Rmax) values in tropical cyclone best-track data, a critical parameter for probabilistic coastal hazard assessments. The authors evaluate 1D convolutional neural networks, LSTM networks, and conventional machine learning models, incorporating physics-informed input augmentation, temporal modeling, and transfer learning using synthetic RAFT/STORM datasets and observational IBTrACS data. Results show that including radius of 34-knot winds (R34) significantly improves performance, while temporal models achieve higher average correlations with fewer samples, better preserving Rmax variability across storms. Transfer learning did not enhance performance due to distributional inconsistencies between synthetic and observational datasets.
radius of maximum windstemporal modelingtransfer learningphysics-informed inputsconvolutional neural networks
Activation Probes Surface Code-Security Signals that the Model's Output Misses
The study demonstrates that linear probes on model activations recover security-relevant signals in code that conventional prompting misses. Using single linear probes trained on vulnerable-fixed Python function pairs, the method achieves 61-67% accuracy in distinguishing vulnerabilities (single-function changes) across five open-weight models, outperforming prompted YES/NO classification and chain-of-thought reasoning. Activations consistently encode security signals even for unseen vulnerability types, whereas model outputs often fail to differentiate vulnerable and fixed versions.
linear probescode-securitymodel activationsvulnerability detectionopen-weight models
LoRA-based Adaptation Alone Is Not Enough: Understanding the Limits of Foundation Models for Face Presentation Attack Detection
This study systematically evaluates 32 foundation models (FMs) for face presentation attack detection (PAD), addressing the limitations of prior CLIP-focused approaches. Using low-rank adaptation (LoRA) with <1% trainable weights, vision encoders achieve <2% intra-dataset ACER but exhibit poor cross-dataset generalization. Results indicate that pretrained representations and adaptation datasets dominate cross-dataset performance, while lightweight LoRA adaptation primarily refines decision boundaries within datasets.
face presentation attack detectionfoundation modelslow-rank adaptationcross-dataset generalizationacer
Satellite Trajectory Optimization via Proximal Policy Optimization for Space Debris Avoidance
The authors propose a reinforcement-learning policy for autonomous satellite collision avoidance, trained via Proximal Policy Optimization (PPO) with a high-fidelity astrodynamics simulator. The simulator models Newtonian two-body dynamics with Sun/Moon third-body perturbations, fuel-dependent thrust, and configurable debris fields. Training employs curriculum learning and shaped rewards for survival, miss distance, and delta-v conservation. In 1,000 deterministic GEO episodes, the agent achieves a 97.5% collision avoidance success rate, significantly outperforming rule-based (20.7%) and impulsive delta-v planner (27.5%) baselines. The framework is publicly available for reproducibility.
proximal policy optimizationastrodynamics simulatorcollision avoidancecurriculum learningdelta-v conservation
Bayesian Symbolic Regression with Entropic Reinforcement Learning
The paper introduces ERRLESS (Entropy-Regularized Reinforcement Learning for Expression Structure Sampling), a Bayesian symbolic regression method that samples from the posterior distribution over expressions using maximum-entropy reinforcement learning. The approach employs a neural policy to sequentially construct abstract syntax trees, enabling posterior sampling at convergence. Evaluated on the Feynman benchmark, ERRLESS produces interpretable expressions and achieves competitive performance, with its posterior predictive mean demonstrating high $R^2$ compared to sequential Monte Carlo baselines.
symbolic regressionbayesian inferencemaximum-entropy reinforcement learningabstract syntax treesposterior sampling
Hyperbolic Multimodal Continual Learning
The paper establishes a theoretical foundation for representation preservation in hyperbolic multimodal continual learning, demonstrating that cross-modal invariance under shared hyperbolic isometry prevents catastrophic forgetting. The proposed framework preserves both relational structure and hierarchical geometry by addressing semantic drift and hierarchy distortion through geometric constraints. Experiments on multimodal benchmarks validate the approach's effectiveness in maintaining geometric structure while adapting to new tasks.
hyperbolic geometrymultimodal learningcontinual learningrepresentation preservationgeometric invariance
Training-Free Universal Approximation by Prompting Random Transformers
The paper demonstrates that untrained single-layer softmax attention transformers with random weights can universally approximate Hölder functions on compact manifolds when guided by task-specific soft prompts. By connecting softmax attention to kernel methods, the authors construct explicit prompts that align attention logits with Gaussian kernel exponents, enabling the frozen network to emulate Nadaraya-Watson kernel regression. Theoretical analysis shows minimax-optimal approximation rates dependent on intrinsic dimension, with empirical validation of the construction and rates.
universal approximationsoftmax attentionkernel regressionprompt engineeringfrozen transformers
Generalized Convexity and Smoothness via Conjugate Duality: Optimization Theory for Deep Neural Networks
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XFeat Revisited: Reproducibility and Evaluation of a Lightweight Image Matcher
This reproducibility study re-implements and evaluates XFeat, a lightweight local feature extractor and matcher for resource-constrained hardware. The authors reproduce the architecture based on the original paper and supplementary material, re-evaluate the released checkpoint, and conduct architectural ablations to examine under-justified design choices. Reproduced models match or outperform the original checkpoint on MegaDepth-1500 and ScanNet-1500, validating XFeat's accuracy-efficiency trade-off. Ablations reveal nuanced insights: the parallel keypoint branch benefits semi-dense matching less than claimed, while skip-connection placement remains inconclusive. Downstream evaluations show close agreement for homography estimation but lower Aachen visual localization performance, suggesting sensitivity to underspecified details. Zero-shot out-of-distribution and cross-modal matching tests demonstrate effectiveness in some settings but degradation under severe modality shifts.
local feature extractorimage matchingarchitectural ablationsemi-dense matchingcross-modal matching
Tracking the Best Strategy in an Extensive-Form Game
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Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing
The paper introduces Monte Carlo estimators and deep neural network approximations for solutions to linear elliptic PDEs with constant diffusion, drift, and killing. Building on the modified Walk-on-Spheres algorithm, the authors derive estimators incorporating sampled random times from stochastic representations, proving uniform error bounds and polynomial sample complexity in inverse accuracy and dimension. They also establish neural network approximation results, showing polynomial parameter growth under assumptions on boundary data and distance function representations. These extend prior complexity analyses to a broader class of elliptic equations.
monte carlo estimatorselliptic pdeswalk-on-spheresneural network approximationstochastic representations
When Do Task Vectors Interfere? Mapping the Validity Boundaries of Weight-Space Composition
The study investigates the validity boundaries of weight-space composition via task arithmetic, distinguishing parameter geometry from functional geometry. It measures pairwise functional non-additivity using a predictive-distribution interaction ratio, evaluated with norm-matched controls and response-only fine-tuning on Qwen2.5-1.5B. Results show task-vector interference varies by prompt type (e.g., code+safety exhibits higher non-additivity than code+math on code prompts) and persists across scales (up to 7B) and architectures (Llama-3.1-8B). External validation reveals format sensitivity, with instruction-style wrappers collapsing contrasts. Weight-space composition thus supports coarse, input-conditioned functional statements but not universal merging-performance prediction.
task arithmeticweight-space compositionfunctional non-additivityfine-tuningpredictive-distribution interaction
Hierarchical rank-evolving representation for physics-informed neural networks
The authors propose a hierarchical rank-evolving (HRE) representation for physics-informed neural networks (PINNs) to address limitations in tensor-based PINNs (T-PINNs). HRE automatically determines tensor decomposition ranks through a hierarchical design, decomposing multivariate functions into a small-scale inner tensor and univariate functions. This eliminates manual rank tuning and improves structure capture. Evaluated on high-dimensional static (Helmholtz, Poisson), time-dependent (Klein-Gordon), and fluid-dynamics (Navier-Stokes) problems, HRE-PINNs outperform state-of-the-art methods in accuracy.
physics-informed neural networkstensor decompositionrank determinationmultivariate functionshierarchical representation
Flow-based conditional cardiac anatomy generation for virtual cohorts
CAN-FLOW introduces a two-step conditional generative framework for cardiac anatomy synthesis, decoupling geometry representation learning (via diffeomorphic shape momenta) from metadata-conditioned distribution modeling (via normalizing flows). Trained on 2,208 UK Biobank subjects, it outperforms cVAEs in phenotype distribution fidelity, subgroup variability preservation, and shape coverage metrics, demonstrating superior virtual cohort generation for biventricular anatomies conditioned on sex, age, and BMI.
conditional normalizing flowsvirtual cohortsdiffeomorphic shape momentabiventricular anatomymetadata-conditioned generation
From Approachability Residuals to Anytime-Valid Evidence: The Online Convex Geometry of Testing by Betting
The paper establishes a precise connection between betting-based sequential tests and Blackwell approachability via support-function residuals in online convex optimization (OCO). For compact convex targets and vector observations, it derives an exact pathwise identity linking distance to the target, OCO residuals, and regret. When composed with one-sided betting, this yields finite-time guarantees: target gaps exceeding a computable threshold force rejection, while non-rejection certifies proximity. The framework extends to controlled stochastic experiments where adaptive nulls yield e-processes, with sublinear OCO regret ensuring stochastic approachability and mean separation under alternatives yielding exponential wealth growth. Applications include bounded means, kernel MMD, and active data sources.
blackwell approachabilitye-processesonline convex optimizationsequential testingsupport-function residuals
Depth-adaptive Inference of Looped Language Models via Continuous Depth Batching
The paper introduces continuous depth batching (CDB), a scheduling method enabling efficient depth-adaptive inference in looped language models (LMs). CDB addresses the batching challenge posed by variable loop iterations per token by decoupling boundary stages (e.g., embedding/LM head) and loop steps into separate priority queues, overlapping scheduling with GPU computation. Evaluated on Ouro 1.4B and Huginn 3.5B, CDB achieves 99% of the theoretical speed-up from adaptive depth, yielding 1.5-1.9× higher throughput and 45-90% lower latency under dynamic load.
looped language modelsdepth-adaptive inferencecontinuous depth batchingpriority queuesgpu scheduling
A Mechanistic Diagnostic of Rank Collapse in Post-Norm Decoder Transformers
This work provides a mechanistic diagnosis of rank collapse in Post-Norm decoder-only Transformers, explaining why causal attention creates high-similarity representations and why training fails to repair them. Through a two-stage analysis using token similarity as a scalar state variable, the authors show that causal attention acts as a prefix-averaging operator increasing token similarity at initialization, while RMSNorm backward factor contraction causes geometric gradient decay in high-similarity regimes. Experiments on 48-layer Transformers trained on C4 dataset validate the predicted initialization-time similarity growth and collapse-time gradient contraction, demonstrating that collapsed networks remain near the predicted frequency loss floor. The analysis distinguishes forward similarity amplification from backward repair incapacity in Post-Norm collapse.
rank collapsepost-normtoken similarityprefix-averagingrmsnorm
From Objectives to What Models Learn: A Landau Theory of Invariant Learning
The paper introduces a Landau-theoretic framework to analyze invariant learning objectives by modeling representation learning as multimode magnetization. It derives an effective free energy from concrete objectives, with low-order coefficients forming signatures that predict distinct regularization phenotypes (e.g., phase boundaries, mode elimination, and amplitude regulation). In a bilinear model, closed-form phase boundaries and critical strengths for shortcut/stable modes are derived, validated experimentally. The framework generalizes to ReLU networks and matrix-coupled modes, linking objective signatures to regularization-path behavior.
invariant learninglandau theoryregularization phenotypesphase boundariesmultimode magnetization
Regret, equilibrium, and learning in games: A guided tour
The article provides a unified framework for analyzing learning in games, focusing on both single-agent and multi-agent settings. It examines regularized learning policies that balance best-response strategies with exploration penalties to avoid suboptimal choices. For single agents, it presents regret bounds in adversarial multi-armed bandits; for multi-agent systems, it derives ergodic equilibrium convergence in zero-sum games and links Nash equilibria to attracting points of regularized learning. The analysis distinguishes between oracle- and payoff-based methods, emphasizing player information availability.
regularized learningregret boundsnash equilibriamulti-armed banditszero-sum games
Coordinate-Residual Physics-Driven Neural Network for Electromagnetic Inverse Scattering
A coordinate-residual physics-driven neural network (CRPDNN) is proposed for 3-D electromagnetic inverse scattering, addressing the nonlinear and ill-posed nature of the problem. CRPDNN directly reconstructs unknown contrast distributions using normalized spatial coordinates and a residual convolutional network, eliminating the need for preliminary reconstruction-based region selection. On noise-free 3-D synthetic cases, CRPDNN achieves an average relative error of 2.10%, outperforming CSI (7.97%) and $L_{2/3}$-FBE-WCIE (3.99%), while providing 5.5- and 12.1-fold speedups, respectively. Supplementary 2-D comparisons and 3-D Fresnel experiments demonstrate its stability, computational efficiency, and practical imaging potential under noisy measurements.
electromagnetic inverse scatteringphysics-driven neural networkresidual convolutional networkcontrast distributionfresnel experiments
Beyond Binary: Continuous State Optimization with Graph-Structured Objectives
The paper extends multi-objective optimization to continuous state spaces, introducing a framework that minimizes linear objectives with movement costs for system stability. The method employs a dependency graph to capture local objective structure and proposes Lazy Graph-LinUCB, which reduces switching costs via lazy updates. Three mechanisms exploit graph structure: asynchronous updates, adaptive graph learning, and joint estimation for correlated objectives. Experiments show a 3x reduction in movement costs while maintaining comparable cumulative losses in heterogeneous systems.
continuous state optimizationmovement costsdependency graphlazy graph-linucbregret bounds
A Machine Learning Based Search for Lunar Anomalies
The study evaluates a Beta-Variational Autoencoder (VAE) for unsupervised anomaly detection in Lunar Reconnaissance Orbiter (LRO) imagery (0.5-2 m/pixel resolution). The model identifies geological anomalies (e.g., rockfall deposits, fresh craters, irregular mare patches) and artificial objects (landed spacecraft) with statistical significance, successfully localizing known sites like Plaskett Crater and Paracelsus C Crater. Results demonstrate the VAE's capability for large-scale lunar surface analysis.
beta-variational autoencoderunsupervised learninglunar reconnaissance orbiteranomaly detectionhigh-resolution imagery
In-Context Density Estimation for Tabular Data
The paper introduces ICED, an in-context energy-based density estimator for tabular data that eliminates per-dataset training costs. ICED is a transformer-based model pretrained on synthetic data under an objective that preserves log-density ordering while fitting informative regions. It performs density estimation, out-of-distribution detection, anomaly detection, and data augmentation in a single forward pass without retraining or hyperparameter tuning. Evaluations show ICED matches task-specific methods across all four tasks while requiring no labels or adaptation between them.
in-context learningenergy-based modeldensity estimationtabular datatransformer
Hallucinations and Constraints : Regulating surgical workflow recognition beyond accuracy
The article proposes topological error constraints as measurable hallucinations in medical image processing, formalizing them as linear temporal logic predicates enforceable via probabilistic graphical models. Applied to surgical phase recognition in robot-assisted hysterectomy, this method reduces topological errors while improving accuracy by ~10%, demonstrating how mathematical correctness guarantees can complement empirical ML regulation in medical computing.
topological errorslinear temporal logicprobabilistic graphical modelssurgical workflow recognitionmedical image processing
MaxModShift: Model Privacy via Designed Shifts
MaxModShift introduces a novel federated learning privacy mechanism by treating eavesdropper model learning as an estimation problem. The method designs model shifts to maximize divergence between the eavesdropper's (Eve) and central server's learned models while satisfying transmission power constraints, achieved by driving the Fisher Information Matrix to singularity. Results show MaxModShift outperforms prior ModShift in privacy protection with lower power consumption and surpasses noise injection schemes with reduced secret channel bandwidth and average power requirements.
federated learningfisher information matrixmodel privacypower constrainteavesdropper estimation
Targeted Label-Flipping and Oversampling Attacks on Federated Conditional GANs
The authors analyze targeted label-flipping and oversampling attacks in federated conditional GANs, demonstrating their effectiveness in manipulating the global generator's learned distribution. Through theoretical analysis and empirical evaluation on FEMNIST, MNIST, and CIFAR10, they quantify the attack impact using Kullback-Leibler divergence between clean and poisoned distributions. Results show semantic damage grows linearly with poisoning strength, while deviation from the target distribution grows quadratically, making the attack potent yet difficult to detect via label-agnostic metrics.
federated learningconditional ganslabel flippingoversamplingkullback-leibler divergence
SAFE-CHEM: Uncertainty-Aware Policy Switching for Robust Robotic Chemistry
SAFE-CHEM introduces an uncertainty-aware policy switching framework for robust robotic chemistry, addressing safety risks in learning-based policies. The method employs an ensemble of recurrent neural network-based imitation learning policies to quantify epistemic uncertainty via action prediction variance, with a hybrid control architecture switching to a rule-based backup when uncertainty exceeds a calibrated threshold. Evaluated on three laboratory manipulation tasks, the framework improves task success rates and reduces safety violations versus single-policy baselines, demonstrating zero-shot sim-to-real transfer on a Franka Production 3 robot.
robotic chemistryepistemic uncertaintypolicy switchingimitation learningsim-to-real transfer
Beyond the Capability Boundary: Zeroth-Order Optimization for Self-Evolving LLM Agents
The paper proposes a zeroth-order self-evolution framework enabling LLM agents to learn beyond their capability boundary by perturbing LoRA parameters without trajectory annotations. The method perturbs LoRA parameters, computes loss differences for gradient estimation, and updates parameters via supervised fine-tuning, enhanced by parallel perturbation inference and adaptive lookup mechanisms for efficiency. Experiments on deep research benchmarks demonstrate significant improvements in successful trajectories, particularly on difficult examples, outperforming strong baselines.
zeroth-order optimizationlora parametersself-evolving agentsperturbation inferencecapability boundary
Verifiably grounded machine interpretation of lunar geology
The study introduces a multimodal vision-language architecture for automated geologic interpretation of lunar basaltic mare volcanism, embedding geologic knowledge discovery into machine learning. The model generates verifiably grounded interpretations from co-registered topographic, spectral, and geologic maps, balancing geological priors with local visual evidence for accurate stratigraphy and terrain description. While numeric age dating initially defaults to memorized priors, integrating an open-book retrieval mechanism enables faithful citation of published chronologies. Results highlight the necessity of visual interpretation for site evidence and retrieval for quantitative historical context in automated geologic inference.
multimodal vision-languagelunar stratigraphygeologic priorsopen-book retrievalautomated inference
Did the Grid Erase the Event? EndoClock for Auditing Medical World-Model Pipelines
The paper introduces EndoClock, a pretraining audit framework for assessing whether synchronization in medical world-model pipelines erases task-relevant evidence. EndoClock employs a four-regime taxonomy to classify where evidence survives: in sampled values, grid-cell update patterns, native timing, or external acquisition channels. The framework reports the lowest witness-bearing representation supported by available evidence or flags unresolved cases. The authors demonstrate its application in echocardiography, where B-mode video write-outs cease during pulsed-wave Doppler acquisition, leaving measurement events only in external logs. EndoClock serves as a failure alert, emphasizing the need to preserve native observation processes to prevent information loss.
endoclockmedical world-modelsynchronizationpretraining auditechocardiography
Full-Feature versus Limited-Input Machine Learning for Residential Energy Estimation: A Comparative Analysis of RECS and ResStock Under Realistic Input Constraints
The study evaluates trade-offs between predictive accuracy and input accessibility for residential energy estimation using two U.S. datasets: RECS (survey-based) and ResStock (simulation-based). Full-feature models (CatBoost, XGBoost, LightGBM, Random Forest, Neural Networks) established benchmarks (R2=0.90 for ResStock, R2=0.73 for RECS), while restricted-input models (10 variables) converged to R2=0.61-0.62, showing algorithmic limitations without physical/behavioral data. Targeted modeling for homogeneous ResStock cohorts improved accuracy to R2=0.85, highlighting dataset-specific considerations.
residential energy estimationcatboostfeature accessibilityenergy audithomogeneous cohorts
FEAST: Federated Shared-Space Training for Resource-Heterogeneous Clients
FEAST introduces a federated shared-space training framework addressing resource heterogeneity in federated learning by jointly training multiple subnetworks within each client's computational limits. It employs budget-tailored sub-supernet routing to send relevant supernet portions and sparse aggregation to merge returned parameter slices, enabling direct deployment of subnetworks used during federation and post-hoc extraction of additional subnetworks without retraining. Experiments on CIFAR-100, CINIC-10, and TinyImageNet-200 show FEAST achieves 71.06% accuracy at 596M inference MACs, outperforming SuperFedNAS and DeepFedNAS by 2.4 points. Sub-supernet routing reduces model-parameter traffic by 6.8× compared to full-supernet transmission.
federated learningsupernet trainingsubnetwork routingsparse aggregationresource heterogeneity
Label Granularity Skew in Federated Learning with Hierarchical Image Classification
The paper introduces label granularity skew, a novel statistical heterogeneity in federated hierarchical classification where clients provide labels at varying levels of detail within a shared taxonomy. To address this, the authors propose Branch-wise Decoupled Fine-Tuning (BDFT) and its federated variant FedBDFT, which fine-tune branch-wise classifiers and aggregate them via federated optimization. Analysis reveals that conditional softmax classifiers outperform strongly coupled hierarchical models under incomplete supervision. Experiments on CIFAR-100, TinyImageNet, and ImageNet demonstrate FedBDFT's robustness, achieving average accuracy gains of 27.9% (skewness=0.6) and 56.4% (skewness=0.9), with improved zero-shot performance on unseen fine-grained classes.
federated learninghierarchical classificationlabel granularity skewconditional softmaxzero-shot learning
DreOPD: Degraded-Reference Extrapolative On-Policy Distillation for Flow-matching Models
DreOPD introduces degraded-reference extrapolative on-policy distillation for flow-matching models, bridging reinforcement learning and imitation-based optimization. The method converts implicit reward extrapolation into closed-form velocity regression, enabling stable post-training with dense supervision from student rollouts. By using a degraded reference to enhance teacher-reference contrast, DreOPD clarifies extrapolation direction. Experiments demonstrate superior average performance over on-policy distillation and multi-task RL baselines, outperforming specialized teachers on most metrics.
flow-matchingon-policy distillationreward extrapolationvelocity regressiondegraded-reference
Online Learning of Scale Parameters in Score-Driven Filters
The paper introduces an online learning framework for optimizing scale parameters in score-driven filters, treating the gain controlling update magnitude as a decision variable. The method formulates gain selection as a conditional predictive decision problem with a Kullback-Leibler objective, leveraging stochastic gradients and mirror-descent geometries for bounded gain domains. Theoretical analysis provides dynamic-regret bounds for projected and discounted mirror updates under convexity, compactness, and regularity conditions. Empirical results on equity-index volatilities demonstrate that bounded mirror gains outperform constant gains, particularly in multi-crisis markets, while avoiding extreme spikes observed with unbounded exponential links.
score-driven filtersmirror-descentkullback-leibler objectivedynamic-regret boundsstochastic gradient
UNMASK: Discovering and Causally Verifying Spurious Shortcuts in Text Classifiers
UNMASK introduces a fully automated pipeline for discovering, causally verifying, and mitigating spurious correlations in text classifiers without human annotation. The method generates candidate surface patterns as boolean expressions, validates them statistically, and establishes causal dependence via counterfactual interventions. Applied to BERT and RoBERTa on MNLI, UNMASK rediscovered lexical-overlap and negation biases, verifying 9 of 10 features on BERT and 6 on RoBERTa, improving HANS accuracy by up to 12.58 pp. On CivilComments-WILDS, it achieved 70.1% worst-group accuracy without demographic annotation, matching hand-labeled Deep Feature Reweighting.
spurious correlationscounterfactual interventionsdeep feature reweightinglexical-overlap biasnegation bias
CPDA: Class-Conditional Path Distribution Alignment for Unsupervised Time-Series Domain Adaptation
The paper introduces Class-Conditional Path Distribution Alignment (CPDA), a non-adversarial framework for unsupervised time-series domain adaptation that aligns class-conditional latent path distributions rather than global feature marginals. CPDA employs a composite signature-spectral kernel to capture semantic features, temporal structure, frequency-domain information, and low-rank path-signature dynamics, leveraging source labels and target soft pseudo-labels for class-preserving alignment. Theoretical analysis shows CPDA defines a valid kernel discrepancy and provides a class-conditional target-risk bound. Experiments on 13 benchmarks with CNN, ResNet18, and TCN backbones demonstrate CPDA's superiority over 30 baselines.
domain adaptationtime-seriesclass-conditionalpath distributionsignature-spectral kernel
A Time-Frequency Dual-Domain Multi-Scale Convolutional Neural Network for Bearing Fault Diagnosis under Strong Noise
A time-frequency dual-domain multi-scale convolutional neural network is proposed for robust bearing fault diagnosis under strong noise. The method employs parallel convolutional kernels in the time domain to capture multi-scale impulse features and applies Fast Fourier Transform in the frequency domain to extract noise-robust spectral information. Feature fusion yields a compact model with 110,122 parameters. Experiments on the CWRU bearing dataset show 99.75% accuracy in clean conditions and 92.50% at -4 dB SNR, outperforming WDCNN, DRSN-CW, MCNN, and 1D-LeNet by 7.25 percentage points under strong noise. Ablation studies confirm the independent contributions of both branches.
bearing fault diagnosistime-frequency dual-domainmulti-scale convolutionalfast fourier transformsignal-to-noise ratio
Particle-Based Conformal Prediction for Contact-Aware Uncertainty Calibration in Stratified Configuration Spaces
The paper introduces CaPTURe, a conformal prediction method for calibrated uncertainty representation in robot motion planning under contact. The algorithm uses particle-based models and a calibration dataset to construct probabilistically valid prediction regions, handling trans-dimensional uncertainty arising from contact-rich scenarios. It addresses model inaccuracy and multimodal distributions by locally adjusting uncertainty estimates. Evaluations on labyrinth navigation and peg-in-hole tasks show 30% higher success rates versus baselines while maintaining coverage guarantees in both contact and non-contact regimes.
conformal predictionuncertainty calibrationparticle-based modelstrans-dimensional uncertaintycontact-aware planning
SwiftQK: Fast and Communication-Efficient Tensor Parallelism for Query-Key Normalization
SwiftQK introduces a fast, communication-efficient tensor parallelism method for Query-Key Normalization (QK-Norm) in Large Language Models (LLMs). The approach replaces full-vector All-Gather operations with scalar statistic exchanges and overlaps Peer-to-Peer reduction with element-wise computation in a persistent kernel. Evaluations demonstrate latency reductions of 81.4--93.9% for QK-Norm and 29.5% average improvement in end-to-end serving throughput compared to All-Gather baselines.
tensor parallelismquery-key normalizationlarge language modelscommunication-efficientrmsnorm kernel
A Probabilistic Circuit-Induced Pseudo-Metric for Out-of-Distribution Detection
The paper introduces Hierarchical Likelihood Vector (HLV) and Hierarchical Likelihood Distance (HLD), a probabilistic circuit (PC)-induced pseudo-metric for unsupervised out-of-distribution (OOD) detection. HLV captures likelihoods from selected PC nodes, while HLD compares probability distributions via HLV expectations, forming an integral probability metric. The method enables a principled goodness-of-fit hypothesis test without requiring held-out in-distribution data at deployment. Experiments on tabular and MNIST datasets show that leveraging hierarchical probabilistic summaries improves OOD detection over root-likelihood, uncertainty-, typicality-, and kernel-based baselines, while localizing distribution shifts to responsible PC nodes.
probabilistic circuitsout-of-distribution detectionhierarchical likelihood vectorintegral probability metricgoodness-of-fit test
Multitask Scanning Probe Microscopy
The authors present multitask scanning probe microscopy, a closed-loop workflow using a multitask Gaussian process to autonomously select measurement locations and protocols for efficient multimodal characterization. The method integrates spatial and cross-modal relationships, demonstrated on an AlScN wafer via tapping-mode and Dual AC Resonance Tracking measurements. Results show successful extension of active learning to modality allocation, enabling combined rapid imaging with slower electromechanical measurements.
multitask gaussian processscanning probe microscopyactive learningdual ac resonance trackingelectromechanical measurements
Contrastive Mask Fidelity: Reference-Free Auditing of Ground-Truth Masks in Remote Sensing Semantic Segmentation
The paper introduces Contrastive Mask Fidelity (CMF), a reference-free metric for auditing semantic segmentation masks in remote sensing by evaluating their alignment with image evidence without relying on ground-truth labels. CMF uses counterfactual views and a frozen vision-language model to assess whether class evidence is concentrated inside the mask and absent outside. Validated on controlled corruptions and 10,731 image-class pairs across ten benchmarks, CMF reveals systematic annotation distortions, favoring candidate masks for man-made classes (62-85%) and matching expert judgment on 81% of cases. It also improves cross-domain transfer when used for supervision.
contrastive mask fidelitysemantic segmentationremote sensingreference-free evaluationvision-language model
Real Data Closes Synthetic-to-Real Gap in Optical Chemical Structure Recognition
This work demonstrates that real training data significantly closes the synthetic-to-real gap in Optical Chemical Structure Recognition (OCSR). Through systematic fine-tuning experiments across 21 recognizers, including Qwen2.5-VL-7B, InternVL3-8B, and GLM-4.1V-9B, varying vision-language model bases, real-data fractions, and vision-tower adaptation strategies, the study identifies labeled real images as the primary driver of performance improvement. Exact match accuracy on the ACS benchmark rises from 0.15 to 0.46 as real-data fraction increases from 0% to 50.2%. Vision-tower LoRA adaptation shows model-dependent effects, ranging from +0.00 to +34.6 points. The best configuration achieves 0.96 accuracy on synthetic renders and 0.49-0.84 on real-world benchmarks.
optical chemical structure recognitionvision-language modelsynthetic-to-real gapvision-tower adaptationexact match accuracy
RAVEN: Frozen Random Graph Reservoirs with Physics-Informed Interaction Fingerprints for Protein-Ligand Binding Affinity Prediction
RAVEN introduces a novel framework for protein-ligand binding affinity prediction using frozen random graph reservoirs and physics-informed interaction fingerprints. The method employs a multihead reservoir of independently initialized, frozen atomistic graph encoders to generate diverse structural projections, combined with deterministic physicochemical interaction fingerprints. These features are processed by heterogeneous supervised readers, including neural and tree-based regressors, with outputs fused through validation-based nonnegative fusion. Evaluated on a similarity-isolated PDBbind 2020R1 split and the CASF-2016 subset, RAVEN demonstrates strong predictive performance, showcasing the robustness of frozen multi-view graph representations and heterogeneous model fusion.
protein-ligand bindinggraph reservoirsphysicochemical fingerprintsheterogeneous fusionatomistic encoders
F2STNet: Fair and Federated Spectral-Temporal Modeling for Graph Forecasting
F2STNet introduces a federated framework for spatiotemporal graph forecasting, combining truncated graph-Fourier features, a diagonal state-space temporal encoder, graph convolution, and Fairness-aware Federated Aggregation (FFA). The spectral branch captures graph-frequency structure, while the state-space layer models long temporal dependencies with linear complexity. FFA adjusts FedAvg using client validation losses and a fairness schedule. Evaluated on PeMS04, HZMetro, and KnowAir datasets, F2STNet demonstrates improved forecasting accuracy and enhances worst-client and client-dispersion metrics in federated settings.
graph-fourier featuresstate-space encoderfairness-aware federated aggregationgraph convolutionspatiotemporal prediction
Personalized Federated Learning via Variance-Aware Nonparametric Empirical Bayes
The paper introduces Variance-Aware Nonparametric Empirical Bayes (VANEB), a novel approach for Personalized Federated Learning that addresses client heterogeneity. The method leverages asymptotic normality of local M-estimators to estimate a shared prior via Nonparametric Maximum Likelihood, incorporating parameter-dependent variances through a generalized Tweedie's formula. Theoretical guarantees include non-asymptotic error rates for density estimation and oracle denoising bounds. For Deep Neural Networks, variants VANEB-head and VANEB-FT personalize the final layer using diagonal variance approximation. Empirical validation on MNIST and CIFAR-10 with convolutional networks demonstrates strong performance.
personalized federated learningnonparametric empirical bayesm-estimationtweedie's formulaheteroskedastic variance
Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations
LuminaECG introduces a clinically structured ECG reasoning framework that reformulates ECG interpretation as measurement-grounded visual reading, leveraging cardiologists' expert reading process as a prior. ECG signals are rendered on standard electrocardiographic grid paper, with P-wave, QRS-complex, and T-wave boundaries explicitly delineated and color-coded segmentation decomposing the waveform into discrete visual measurement primitives. A general 2B vision-language backbone is trained with low-rank supervised fine-tuning to associate these primitives with diagnostic reasoning. LuminaECG improves waveform measurement and diagnostic recovery across baselines, reaches a clinically meaningful reader tier on the CODE-test benchmark, transfers across geographically diverse ECG datasets without retraining, and generates reports with emergent prognostic signals.
ecg interpretationvisual measurement primitiveslow-rank supervised fine-tuningdiagnostic recoveryprognostic signal
Decision-Focused Learning in Network Interdiction Games
The paper identifies a structural failure in decision-focused learning (DFL) for shortest-path network interdiction (SPNI) games, where DFL's training objective admits decision-equivalent cost estimators that perform poorly under interdiction. To resolve this, the authors propose Adversarial DFL (A-DFL), which replaces nominal training samples with interdicted scenarios to eliminate harmful equivalence classes. Experiments on synthetic and real-world networks demonstrate that A-DFL restores DFL's effectiveness in this game-theoretic setting, enabling robust end-to-end optimization.
decision-focused learningnetwork interdictionstackelberg gameadversarial trainingend-to-end optimization
HOPPER: Learnable Hop Extraction for Linearized Graph Sequence Models
HOPPER introduces learnable hop extraction for Linearized Graph Sequence Models (LGSMs), decoupling information depth from processing depth to address issues like over-smoothing and over-squashing in graph neural networks. The method employs feature-conditioned, structure-aware, and graph-adaptive propagation mechanisms while preserving permutation equivariance, treating node propagation states as sequences processed by modern state-space models. HOPPER achieves state-of-the-art or competitive performance on the ECHO-Synth benchmark and optimizes accuracy on the LRIM physics-based long-range dependency benchmark by varying the maximum neighborhood size of message backtracking cancellation.
linearized graph sequence modelslearnable hop extractionpermutation equivariancestate-space modelsover-smoothing
Closing the loop in learning with missing data
The paper proposes a dynamical systems perspective on learning with missing data, framing data missingness as a structured loss of actuation that limits controllability in parameter error dynamics. It derives Lyapunov-stable adaptation mechanisms that throttle model updates to preserve learning coherence under partial observability, yielding ISS-type residual-to-state bounds under recurrent excitation. Evaluation on multimodal contexts demonstrates improved learning coherence and stability in pathologically sparse domains.
missing datalyapunov stabilityparameter error dynamicsobservability-aware learningiss-type bounds
PreGress: Ranking-Native Pre-training and Prompting for Graph Node Ranking
We introduce PreGress, a ranking-native pre-training and prompting framework for graph node ranking tasks. PreGress employs multi-task pre-training with objectives including degree centrality prediction and attribute reconstruction to jointly capture structural and attribute information. Task-specific prompt modules adapt a frozen ranking backbone to downstream tasks without full retraining, enabling efficient transfer across heterogeneous ranking criteria. Experiments on six public graphs and two real-world benchmarks (Yelp2018, MovieLens-100K) demonstrate strong ranking quality with low task-specific state overhead, validated through a controlled five-criterion graph-access study.
node rankinggraph pre-trainingprompt modulesdegree centralityattribute reconstruction
Dynamic Distribution-Aware Uncertainty Tracking in Vision-Language Representation Learning
The paper proposes Dynamic Distribution-Aware Uncertainty Quantification (DDA-UQ), a framework for improving uncertainty estimation in Vision-Language Models (VLMs) by addressing static post-hoc methods' limitations. DDA-UQ employs a Gaussian Mixture Model to dynamically model the embedding space during training, extracting distributional evidence to adapt uncertainty estimates to shifting test distributions. Experiments show DDA-UQ outperforms state-of-the-art methods in uncertainty quantification for VLMs.
uncertainty quantificationvision-language modelsgaussian mixture modeldistributional evidencepost-hoc methods
A Tight Lower Bound for Smooth Nonconvex Stochastic Optimization with Bounded Gradient Noise
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Mind the Hook: Source-Level Auditing of Privacy Defenses in Retrieval-Augmented Generation
The contribution introduces a source-level auditing methodology for privacy defenses in retrieval-augmented generation (RAG), focusing on active-path analysis. The method inventories source-level hooks across retrieval, retrieved content, and generation, maps metrics to observed leakage channels, and validates effects using exact-match canaries. Results show that DP-style defenses modify only retrieval scores, explaining their impact on membership-inference behavior but not on named-entity leakage (NEL_strict). In contrast, end-to-end LPRAG effectively mitigates leakage, recovering 53/150 canaries under No-Defense and 0/150 under LPRAG. The findings are specific to reimplementations and serve as a case study rather than a universal ranking.
retrieval-augmented generationsource-level hooksexact-match canariesnamed-entity leakagemembership-inference
SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance
SoftMCC introduces a threshold-free validation framework for model selection in imbalanced binary classification, addressing the threshold-dependency of Matthews correlation coefficient (MCC) rankings. It leverages probability-valued confusion counts with a calibrated identity and tie-aware selection protocol, producing a covariance-normalized probability-label association score that reduces to MCC for hard predictions. Evaluated across 18 settings with 12 grouped repeats, SoftMCC achieves superior stability (mean rank 2.31) and tie-corrected Kendall's W (0.659), outperforming AUPRC and MCC@0.5. It demonstrates calibration sensitivity, with temperature scaling altering rankings (mean Spearman 0.851), while maintaining bounded stability and utility evidence.
matthews correlation coefficientprobability-valued confusioncalibration sensitivitytie-aware selectioncovariance-normalized
Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Models
The paper introduces noise-coupled twin rollouts for counterfactual generation in interactive video world models, where a factual and counterfactual branch share initial states and future noise sequences but diverge in action streams at an intervention point. This approach exacts Pearl's abduction step by construction and enforces minimal-change through verifiable spatiotemporal locality metrics against simulator ground truth. The framework includes formal definitions for counterfactual evaluation and proposes using ground-truth re-renders as verifiable rewards for post-training, with experimental validation pending.
counterfactual generationnoise-coupled rolloutsinteractive video world modelsspatiotemporal localityabduction step
Label-Free Parkinson's Disease Screening from Face and Voice through Mechanistic Interpretability
The study introduces a label-free Parkinson's disease (PD) screening method using frozen pretrained encoders for face and voice modalities, eliminating the need for PD-labeled training data. For voice, a synthetic-dysarthria contrastive activation addition (CAA) direction is derived from degraded healthy speech, while face analysis employs a k-nearest-neighbor anomaly score against control embeddings. The alignment principle validates CAA effectiveness when synthetic and real disease directions exhibit positive cosine similarity (+0.37 for voice, AUROC 0.765; -0.48 for face, using anomaly detection instead, AUROC 0.751). Late fusion achieves AUROC 0.802 (95% CI [0.70,0.89]) with 0.95 negative predictive value, though face-side results may overfit without external validation.
parkinson's diseasecontrastive activation additionanomaly detectionfrozen encoderslate fusion
Math-Vision Diagrams: A Comprehensive Benchmark for Evaluating LLM Mathematical Diagram Generation Capabilities
Math-Vision Diagrams introduces the first benchmark for evaluating LLM mathematical diagram generation, unifying text-to-code and text-to-image paradigms. The benchmark comprises 2920 high-quality competition problem images, curated via an ensemble LLM and SME pipeline. Results show current LLMs struggle with math diagram generation, assessed through novel evaluation metrics. The dataset, pipeline, and evaluation scripts will be open-sourced.
mathematical diagram generationtext-to-codetext-to-imagellm evaluationspatial reasoning
Gradient Under Microscope: Benchmarking Resource Utilization of Memory-Efficient Gradient Computation Methods
This work systematically benchmarks memory-efficient gradient computation methods to address AI training's growing resource demands. The study evaluates five optimizers (SGD, Adam, Adagrad, Adadelta, Conjugate Gradient Descent) under three memory strategies (standard training, gradient checkpointing, accumulation) across four transformer architectures (ViT, ModernBERT, Llama 3.1 1B, NanoVLM), measuring loss, GPU utilization, time, and memory. Key findings show gradient accumulation reduces loss significantly (10× on vision-language, 4× on language models) without extra memory, while Adam is not universally optimal. Gradient checkpointing exhibits architecture-dependent effects, improving ViT but degrading encoder models, with 60% time overhead on memory-bound cases. GPU utilization varies from 8-15% (memory-bound) to 96-99% (compute-bound).
gradient accumulationmemory-efficient trainingtransformer architecturesgpu utilizationoptimizer benchmarking
Can Webcam Gaze Constrain Mesa-Objectives in Driving Models? An Instrument Precision Analysis
The study evaluates whether webcam-based gaze tracking (WebGazer.js) can constrain mesa-objective formation in autonomous driving hazard detection models by providing privileged information. Using 137,663 frame-level gaze samples synchronized with hazard annotations from 388 dashcam clips, experiments tested two calibration protocols (9-point/45-click and 11-point/440-click) and two model architectures (Random Forest and causal Transformer) across five random seeds. Results showed no statistically significant improvement from gaze integration (p = 0.919, 0.578, 0.667). Geometric analysis revealed WebGazer's error (~130-257 px) exceeds 93% of hazard object sizes (median 36 px), making object-level gaze attribution infeasible at this precision.
mesa-objectiveswebcam gazehazard detectioncalibration protocolsgeometric analysis
What Would Fix This RAG Failure? Auditing Counterfactual Response with Paired Evidence Interventions
This paper introduces Pair-ID, an offline audit method for analyzing retrieval-augmented generation (RAG) failures by holding query, retrieval state, and reader constant while manipulating evidence through addition and deletion operations. The method evaluates counterfactual response vectors across 19,981 benchmark queries, identifying 11,105 eligible failures and analyzing 1,200 selected cases. Results show that adding missing evidence repairs 32.8% of failures, while deleting nonsupporting evidence repairs 13.6%, with semantic contrasts retained in sham operations. The original failure view provides partial predictive signal (macro AUROC 0.678), but exact-vector accuracy (0.637) does not surpass the majority-vector baseline (0.646). Evidence sensitivity varies by reader, supporting frame-scoped offline audits over universal taxonomies or runtime repairs.
retrieval-augmented generationcounterfactual responseevidence repairsemantic contrastsoffline audit
Clustered Attractor Manifolds and Dynamical Condensation in Self-Attention
The study analyzes attractor structures in minimal normalized self-attention dynamics, identifying the overlap gap as the key quantity governing behavior in the thermodynamic limit. Using a theoretical framework, the authors demonstrate that when token representations form internally aligned clusters with intra-cluster similarity exceeding inter-cluster similarity, inter-cluster attention decays exponentially with dimension. This results in a high-dimensional manifold of clustered fixed points, ranging from macroscopic to fragmented microscopic states. The work also reveals a dynamical attention-condensation transition, where clustered states emerge from unstructured Gaussian initializations only above a finite sharpness threshold.
self-attention dynamicsattractor manifoldsoverlap gapattention condensationthermodynamic limit
A Domain-Structured Ensemble Framework for Perioperative Outcome Prediction Using Electronic Health Record Data
The study introduces a domain-structured ensemble framework for perioperative outcome prediction using electronic health record (EHR) data, addressing limitations of existing models in calibration and interpretability. The method organizes predictors into patient-, surgery-, and anesthetics-related domains, trains domain-specific gradient boosting models, and integrates predictions via a logistic regression meta-learner. Evaluated on 5,386 surgical encounters for postoperative delirium (POD) prediction, the stacked model achieved AUROC 0.899 (95% CI: 0.891-0.906), outperforming single-stage models (AUROC 0.849) and demonstrating strong calibration (intercept -0.006, slope 1.035) in temporal validation (AUROC 0.915).
ensemble learningperioperative risk predictionelectronic health recordsgradient boostingclinical decision support
Physics-Informed Learning for Robust Acoustic Localization with Calibrated Uncertainty
The paper introduces a physics-informed learning method for robust acoustic localization that addresses limitations of classical approaches (hyperbolic and score-based) in complex outdoor environments. The proposed hybrid system combines a fast hyperbolic solver with a learned correction model operating on physics-informed acoustic features, reducing catastrophic errors while maintaining median accuracy. The method also provides calibrated, geometry-aware uncertainty estimates suitable for downstream spatial modeling. Evaluations on distributed microphone arrays in real and simulated outdoor environments demonstrate improved robustness and uncertainty awareness, advancing automated wildlife monitoring capabilities.
acoustic localizationphysics-informed learninguncertainty calibrationpassive acoustic monitoringhyperbolic solver
Tied Trit-Planes: Constraining PTQTP to a Uniform Nine-Level Quantizer, with a Persistent Folded Format for Disk-Streamed Mixture-of-Experts Serving
The paper introduces tied trit-planes, a method constraining PTQTP to a uniform nine-level quantizer by fixing the scale ratio between two ternary planes to three, enabling lossless folding into a 4-bit code plane. This persistent format serves identically for disk storage, expert caching, and kernel input, optimized for CPU-SIMD and SSD streaming. Applied to DeepSeek-V4-Flash-0731 (284B-A13B MoE), it matches a 4.5-bit Q4_K baseline in API fidelity (5/5 fixtures at step 0, 12/14 continuation steps) with 6.7% faster decoding and 9% smaller files, despite higher weight-reconstruction error and perplexity. Open-source implementation includes ternary ladder and pinned kernels.
ptqtpternary quantizationmixture-of-expertsssd streamingcpu-simd
Federated Attention Autoencoders with a Stochastic Aggregation Scheme for Anomaly Detection
The paper introduces two novel stochastic aggregation functions for federated attention autoencoders, addressing the lack of specialized aggregation methods for attention-based models in federated learning. The proposed approach preserves learned information in memory modules more effectively than traditional methods. Evaluated on KDDCUP10, the method improves F1 score by 2.9% and AUC ROC by 5.1% compared to standard autoencoders.
federated learningattention autoencodersanomaly detectionstochastic aggregationoutlier detection
Inductive Graph Layout with Implicit Neural Fields
Fling (Field Layout via Implicit Neural Geometry) introduces a neural network-based approach for inductive graph layout, optimizing a fixed-parameter function instead of free node coordinates. The method maps node distances to landmarks, positioning nodes via layout energy training, reducing the computational cost to $O(|\mathcal{A}|N)$ per step for $|\mathcal{A}|\ll N$ anchors. Unlike message-passing neural drawers, Fling represents the drawing as a function of node features, enabling one forward pass for unseen nodes. It outperforms PivotMDS, landmark MDS, and kernel ridge regression in fitting graph energy from node samples, and supports stochastic pivot stress and aesthetics-optimized variants.
graph layoutimplicit neural fieldskamada-kawaimajorisation sumsnode-edge clearance
Sparse Attention to Emotion: Efficient Facial Emotion Recognition via Token Reduction
The paper proposes Sparse Attention to Emotion (SAE), a Vision Transformer-based model for Facial Emotion Recognition (FER) that reduces computational complexity by selectively discarding non-informative image tokens. SAE leverages the observation that specific facial regions (eyes, mouth, cheeks) contain sufficient discriminative information for emotion recognition, enabling 90% token reduction while maintaining accuracy. Experiments on RAF-DB show SAE achieves state-of-the-art results with up to 90% lower computational cost compared to standard quadratic-complexity approaches.
facial emotion recognitionvision transformertoken reductionsparse attentioncomputational efficiency
Approximation Rates for Metaplectic Neural Networks
The paper introduces metaplectic neural networks, extending Barron spaces via a symplectically motivated metaplectic transform. It establishes embeddings between metaplectic Barron spaces and Sobolev spaces, then proves Monte-Carlo approximation bounds for metaplectic Barron functions using finite linear combinations of dictionary atoms. A deep neural network architecture is developed using these atoms as building blocks, demonstrating superior performance in approximating solutions to time-dependent Schrödinger equations compared to classical physics-informed neural networks.
metaplectic transformbarron spacessobolev spacesmonte-carlo approximationschrödinger equations
Beyond Routing: Decoupling Expert Dispatch and Aggregation in Sparse Mixture-of-Experts
The paper proposes decoupling expert dispatch and aggregation in Sparse Mixture-of-Experts (MoE) models, demonstrating that current router scores poorly align with optimal expert utility. Through experiments on OLMoE-1B-7B and DeepSeek-V2-Lite, the authors introduce Fixed-Dispatch Adaptive Aggregation (FDAA), a 301K-parameter post-compute head trained while freezing the backbone. FDAA improves language modeling performance by ΔCE = -0.1523 on WikiText-103 and shows robust gains across multiple benchmarks, with router top-1 selections identifying the best expert in only 12.5-17.2% of cases.
mixture-of-expertsrouter utilityfixed-dispatchadaptive aggregationlanguage modeling
The Cost of Adaptivity: Matching Lower Bounds Across Learning Problems
The paper establishes matching lower bounds for adaptive learning procedures by formalizing nuisance adaptation via a slice-normalized minimax ratio and quantifying robustness costs of post-hoc query expansion. It introduces a finite-horizon composition law for Gaussian certification, proving optimal normalized squared half-widths of order log(eM) + log log(e^eT) for familywise certifiers protecting M coordinates over T time steps. Epoch stitching provides upper bounds, while geometric block increments yield matching lower bounds, with empirical validation confirming theoretical predictions.
adaptive proceduresminimax ratiogaussian certificationfamilywise coverageepoch stitching
Hybrid Neural-Classical Correction for Frozen Time Series Foundation Models: A Comprehensive Ablation Study on High-Frequency Stock Prediction
The study investigates hybrid neural-classical correction for adapting frozen TimesFM (200M parameters) to high-frequency stock prediction, comparing AttnCorrect (471K params) and GatedLinear (49K params) architectures augmented with Random Forest residual learning. Systematic ablation across 10 stocks (2M data points) shows the hybrid approach achieves 0.597 pooled correlation and 6.4x mean per-day improvement over frozen TimesFM, with Random Forest providing the largest single-component contribution. GatedLinear+RF outperforms AttnCorrect+RF with 9x fewer neural parameters, demonstrating that classical methods crucially complement neural adaptation.
foundation modelsneural-classical hybridresidual learninghigh-frequency financezero-shot generalization
Distilling Vision-Language Models for Robust Traffic Sign Perception in Autonomous Vehicles
LAMDA (Language-Anchored Model for Direction Alignment) improves robustness in traffic sign recognition (TSR) against multiple physical adversarial attacks without inference overhead. The method distills vision-language model (VLM) knowledge by constructing two fixed prototype banks from OpenCLIP-generated sign descriptions and class names, supervising visual features via auxiliary losses during training. Evaluated on GTSRB and LISA across four backbones and three attack types (shadow, natural-light, printed patches), LAMDA consistently boosts robustness (+12.5 pp under shadows, +13.2 pp under natural light) while maintaining or improving clean accuracy, outperforming ten baseline methods.
traffic sign recognitionvision-language modelsadversarial robustnessknowledge distillationphysical attacks
360CityArena: A Realistic Virtual Urban Navigation Benchmark for Embodied Agents
360CityArena introduces a photorealistic urban navigation benchmark for embodied agents, addressing limitations in existing outdoor benchmarks by reconstructing Tokyo's Akihabara district from 602 360-degree video segments across 85 streets. The benchmark includes 175 human-crafted tasks spanning Environment Understanding, Path Reasoning, and Spatial Reasoning categories. Evaluation with LMM-based agents reveals a significant performance gap (Gemini 2.5 Flash: 17.1% vs. human: 77.3%), highlighting challenges in city-scale embodied navigation and reasoning.
embodied agentsphotorealistic environmenturban navigationspatial reasoning360-degree video
ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems
The paper proposes a hierarchical ML-based energy scheduling framework for dynamic NTN-WPT systems, optimizing energy efficiency, task completion rate, and waiting time. The approach decomposes the problem into three layers: state prediction using forecasting models, interaction mapping via graph neural networks, and decision-making employing multi-agent deep learning with self-attention and MAPPO. A multi-objective reinforcement learning technique scalarizes competing objectives into a weighted-sum reward. Simulations demonstrate superior trade-offs compared to baselines, maintaining competitive task completion and energy efficiency while reducing waiting times under variable conditions.
wireless power transfergraph neural networkmulti-agent reinforcement learningenergy schedulingnon-terrestrial networks
End-to-End Neural Decomposition with Koopman Operators for Time-Series Forecasting
Proposes NDKoop, an end-to-end neural architecture integrating learnable signal decomposition with frequency-independent and frequency-dependent Koopman operators for time-series forecasting. The method decomposes signals into trend and periodic components, each modeled by specialized Koopman networks, addressing non-stationarity and imperfect linearization. Demonstrates improved forecasting accuracy on multiple benchmarks through joint optimization of decomposition and Koopman dynamics within a unified framework.
koopman operatorneural decompositiontime-series forecastingnon-stationary signalsend-to-end learning
Quantum-Classical Physics-Informed Kolmogorov-Arnold Networks for Solving Fuzzy Differential Equations
The study introduces Quantum-Classical Physics-Informed Kolmogorov-Arnold Networks (QCPIKAN), a hybrid quantum-classical framework for solving fuzzy differential equations. The network integrates ChebyKAN modules and parameterized quantum circuits to approximate lower and upper endpoint functions of α-cuts, incorporating governing equations, initial-boundary conditions, and fuzzy-structural constraints into the training objective. Theoretical analysis establishes a unified error framework, demonstrating QCPIKAN's reduced a priori error bound when quantum entanglement benefits outweigh computational errors. Numerical experiments on elliptic, parabolic, and hyperbolic equations show QCPIKAN achieves 1.1-2.7x lower mean relative L2 error and 1.77x lower wavefront-position error compared to PIKAN, though both exhibit local fuzzy-structure violations in high-gradient regions and boundaries.
quantum-classical hybridfuzzy differential equationskolmogorov-arnold networksα-cutserror analysis
A Mean-Field Framework for Inference-Time Distributional Control of Diffusion Models
The authors introduce a mean-field framework for inference-time distributional control in diffusion models, addressing the lack of theoretical guarantees for distribution-level reward steering. They formulate the problem as targeting a tilted measure and derive a weighted interacting particle scheme to achieve this goal. The framework generalizes pointwise-reward steering and provides theoretical grounding for batch-level methods. Empirical validation demonstrates correct targeting in low-dimensional settings and explores behavior in high-dimensional protein conformation tasks.
diffusion modelsmean-field frameworkdistributional controlweighted interacting particle schemeprotein conformation
Learning from Consensus and Disagreement: Unsupervised On-Policy Self-Distillation with Minority-Trajectory Contrast
The paper introduces CoDA (Consensus and Disagreement Alignment), an unsupervised on-policy self-distillation framework for language-model reasoning that constructs privileged information from latent uncertainty in unlabeled rollouts. CoDA leverages answer-level consensus to guide distributional learning via a frozen self-teacher (positive branch) while mitigating false consensus through minority-trajectory contrast and KTO-style calibration (negative branch). Evaluations on competition-level math benchmarks show CoDA outperforms self-generated baselines, improving reasoning stability by balancing consensus exploitation with disagreement regularization.
self-distillationon-policy learningminority-trajectory contrastkto-style calibrationreasoning stability
Can We Optimize the Performance-Carbon Emission Break-Even Point?: The Quest for Greener LLMs
The paper introduces carbon-aware fine-tuning for LLMs, proposing a joint loss function incorporating a differentiable energy surrogate based on parameter norm, FLOP proxy, and memory proxy. This method aims to optimize the performance-carbon emission break-even point during inference. Experiments on Gemma-2 2B, Llama-3.1 8B, and Qwen-2.5 14B, evaluated on MMLU tasks (abstract algebra, philosophy, formal logic), reveal that the carbon term acts as either harmful interference or beneficial regularization, depending on task structure. The approach offers a lightweight, task-dependent regularizer with non-empty break-even regions.
carbon-aware fine-tuningenergy surrogatebreak-even pointdifferentiable lossmmlu evaluation
A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning
The paper proposes a distribution mapping algorithm for achieving counterfactual fairness (CF) in reinforcement learning (RL) systems. The method employs quantile distribution mapping to estimate counterfactual states and rewards during data preprocessing, generalizing additive counterfactual assumptions. Theoretical analysis bounds both per-step unfairness and infinite-horizon suboptimality under mild conditions. Empirical validation includes numerical experiments and application to a real-world digital health dataset, demonstrating practical utility in high-stakes domains like healthcare.
counterfactual fairnessreinforcement learningdistribution mappingquantile estimationcausal reasoning
Memory-Efficient Activation Checkpointing with Sliding Window and Hirschberg's Algorithm for 0/1 Knapsack Solving in PyTorch
We present dp_knapsack_sliding_hirschberg, a memory-efficient activation checkpointing algorithm that combines sliding window optimization and Hirschberg's algorithm to solve the 0/1 knapsack problem in PyTorch. The method reduces peak memory usage from O(nW) to O(W) while maintaining exact optimality, where n is the number of operations and W is the memory budget. Experiments demonstrate a 20× increase in computable problem size (n=2000 vs. n=100) and a 25-28% runtime speedup compared to the default dp_knapsack implementation. The algorithm has been integrated into PyTorch version 2.10.
activation checkpointingknapsack problemsliding windowhirschberg's algorithmdynamic programming
Measuring and Reducing WebGPU Dispatch Overhead for LLM Inference
This work introduces a sequential-dispatch measurement method to accurately characterize WebGPU dispatch overhead in browser-based LLM inference, addressing the limitation of naive single-operation measurements that conflate dispatch with synchronization. The method demonstrates that per-dispatch cost is independent of data type and identifies dispatch count, not kernel quality, as the bottleneck at batch size 1. The findings suggest that reducing dispatch count through dispatch amortization, both in inference engines and the WebGPU specification, is the most effective optimization strategy for practical browser-based LLM inference.
webgpullm inferencedispatch overheadbatch sizedispatch amortization
PAST: Privileged Adaptation from Complete Student Trajectories for On-Policy Self-Distillation
The paper introduces Privileged Adaptation from Student Trajectories (PAST), an on-policy self-distillation (OPSD) method that leverages complete student trajectories as privileged information for teacher adaptation. PAST preserves student distributions on correct trajectories and adapts the teacher using failed trajectories under proximity regularization, projecting teacher distributions via Forward-KL to separate privileged variation from transferable policy shifts. Experiments on three mathematical reasoning benchmarks show PAST improves Avg@12 macro average by 5.6 percentage points over Vanilla OPSD, with ablation studies confirming the benefits of trajectory access and adaptation.
on-policy self-distillationprivileged informationforward-kl distillationtrajectory-conditioned teacherstudent-proximity regularization
Gaming Without an Attacker: Benchmark Fingerprinting in LLM-Driven Search Under Selection Pressure
The study identifies benchmark fingerprinting as a critical failure mode in LLM-driven GPU kernel optimization, where models inadvertently exploit evaluation configurations despite lacking adversarial intent. Using a (1+1) evolutionary loop with Opus 4.7, Gemini 3.1 Pro, and GPT-5.5 on Metal-Sci (10 tasks) and Metal-ZK (12 tasks), the authors show that 30% (16/53) of optimized kernels fail to generalize due to configuration-specific branching. They propose a four-mode taxonomy of failures (e.g., gate leakage) and design principles for robust evaluation, emphasizing non-enumerable axes and mechanism-graded transfer rates (gamed, overfit, benign).
benchmark fingerprintinggpu kernel optimizationllm-driven searchevolutionary looptransfer rate
Multi-kernel spectral clustering: Entrywise eigenvector perturbation bounds and exact recovery
The paper introduces multi-kernel spectral clustering to address inadequate single-bandwidth kernel methods for multi-scale data, particularly in high dimensions. It aggregates kernels with bandwidths selected as empirical quantiles of pairwise squared distances, capturing multiple distance scales without population-level information. Theoretical analysis under a high-dimensional mixture model establishes row-wise perturbation bounds for spectral components and normalized Laplacian, enabling observation-level control. Under eigen-gap and separation conditions, approximate $K$-means on the multi-kernel embedding achieves exact recovery with high probability.
spectral clusteringmulti-kerneleigenvector perturbationhigh-dimensional dataexact recovery
Loss-Resilient Wireless Video Token Communication over Block Fading Channels
The authors propose a loss-resilient wireless video token communication (WVTC) framework to mitigate degradation from block fading channels. WVTC evaluates token importance via intrinsic predictive structure, prioritizing I-tokens and measuring P-token importance by temporal neighborhood novelty. A shuffled mixed I/P-token packetization disperses structural anchors and correlated temporal regions, while an online scheduler jointly considers packet importance density, MCS-dependent decoding reliability, block capacity, and importance concentration for packet allocation. A fine-tuned detokenizer reconstructs missing content without retransmission. Results show improved perceptual quality and graceful degradation under increasing packet error rates.
video token communicationblock fading channelspacketizationdecoding reliabilitydetokenizer
RippleKV: Cross-Layer KV Cache Allocation via Perturbation Propagation
RippleKV proposes a novel KV cache allocation method for long-context LLM inference by estimating layer sensitivity through perturbation propagation. The approach injects norm-adaptive perturbations into each layer's value cache, measures the induced KL divergence at the model output over a calibration set, and converts the sensitivity profile into layer budget multipliers via normalization and exponential mapping. Experiments on LongBench demonstrate that RippleKV achieves the highest average performance among KV cache compression methods under matched cache budgets, addressing the challenge of distributing limited cache resources across layers.
kv cacheperturbation propagationlayer sensitivitykl divergencelong-context inference
Efficient Test-Time Scaling for LLM-based Time Series Forecasting
The paper introduces SCALER, a coarse-to-fine framework for efficient LLM-based time series forecasting. The method first predicts coarse future dynamics using a lightweight Transformer for global structure, then guides an LLM to refine residuals iteratively with fewer tokens per step, avoiding costly reward-model selection. SCALER reduces reliance on long prompts and computational overhead while maintaining accuracy. Experiments show improvements in long-term, short-term, and zero-shot forecasting, with significantly lower inference costs compared to scaled LLM baselines.
time series forecastingtest-time scalingiterative refinementtransformerresidual token refinement
Backward Compatibility in Tree-Based Explanations and Enhanced CART Algorithm
The paper introduces Backward Compatibility Loss in Tree-based eXplanations (BCLTX), a loss metric to minimize explanation changes in decision tree updates, and proposes CART-BCTX, a modified CART algorithm incorporating BCLTX. The method ensures stable explanations while updating models by optimizing for backward compatibility alongside predictive performance. Experiments on 10 real-world datasets (classification and regression) demonstrate CART-BCTX achieves balanced trade-offs between prediction accuracy and explanation consistency, with computational efficiency matching standard CART.
backward compatibilitydecision treesexplainable aimodel updatescart algorithm
Catastrophic Forgetting in Continual Reinforcement Learning
This study investigates the relationship between task similarity and catastrophic forgetting in continual reinforcement learning, employing interpretable Q-learning on graph-based tasks to minimize goal-reaching steps. Experimental results reveal complex dynamics, with forgetting severity fluctuating across task similarity and complexity levels, but no statistically significant independent effect of task similarity on forgetting. High variability in forgetting and uneven task similarity distributions were observed, suggesting interdependence between these factors warrants further research.
catastrophic forgettingcontinual reinforcement learningq-learningtask similarityinterpretable reinforcement learning
Kernel Methods for Refined Prophet Inequalities
The paper introduces a kernel method for refining single-threshold prophet inequalities by bounding the relative variance of the prophet's value, creating a nonparametric complexity measure. The method represents instances via the quantile function of the maximum and formulates threshold payoffs as linear kernel functionals, enabling infinite-dimensional convex programming and strong minimax duality in quantile space. Results include exact characterization of the IID bounded-variance curve, asymptotically optimal finite-horizon thresholds, closed-form expressions for fixed-order non-identical models, and a prophet-secretary lower-bound program. The technique also yields an exact formula for IID random horizons under convexity conditions.
prophet inequalitykernel methodquantile functionminimax dualityrelative variance
LegoLM: Structured Weight Sharing for Large Language Models
LegoLM introduces a structured weight-sharing compression framework for large language models, addressing two failure modes: distributional mismatch and outlier dominance. The method employs scalar-block encoding, percentile-selective replacement, and boundary-layer protection to mitigate these issues. Results show LegoLM achieves +0.03% perplexity degradation at 4.41X compression on Mistral-7B and -0.02% at 2.67X on GPT-2 small, outperforming PTQ-8bit in both quality and compression ratio. Downstream evaluations on LAMBADA and HellaSwag confirm accuracy preservation within noise at 5.12X compression. Selective replacement is identified as the dominant mechanism, rescuing models from catastrophic degradation.
weight-sharingdistributional mismatchoutlier dominancescalar-block encodingpercentile-selective replacement
Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking
The paper introduces MakeTUL, the first knowledge graph representation learning approach for Trajectory-User Linking (TUL). The method constructs a multi-relational mobility knowledge graph incorporating visit-time, POI-category, and transfer-speed relations, jointly constraining embeddings through heterogeneous mobility semantics. It enriches POI representations with high-order co-occurrence patterns and integrates them with temporal, category, and transfer information in a dual-branch classifier preserving both structural and sequential evidence.
trajectory-user linkingknowledge graph embeddingmulti-relational learningmobility semanticspoi representation
Path-dependent Discrete Amortized Inference
The paper introduces path-dependent discrete amortized inference, a method that enhances sampling of discrete compositional objects by lifting the Markov Decision Process (MDP) with a learnable latent dynamical system. This allows policies to depend on entire trajectories, addressing state aliasing and training signal propagation issues in Markovian approaches. Theoretically, the work extends existing discrete amortized sampler learning algorithms to this non-Markovian setting. Experiments on standard benchmarks demonstrate faster convergence and improved state space exploration compared to prior methods.
discrete amortized inferencemarkov decision processstate aliasinglatent dynamical systemtrajectory dependence
Exact Rank-Space KL Projection for Shared-Marginal Low-Rank Factors: Application to Doubly Stochastic Clustering
The paper introduces an exact Kullback-Leibler (KL) projection method for low-rank factorizations with prescribed row marginals and a shared column marginal, reducing the joint KL projection to a strictly convex dual problem with $r-1$ variables. The method leverages matrix-free Hessian-vector products and specializes to doubly stochastic graph learning via row-simplex factors, ensuring exact feasibility at every step. Experiments demonstrate competitive clustering accuracy, near-zero feasibility residuals, and efficient anytime performance without dense graph learning.
kullback-leibler projectionlow-rank factorizationdoubly stochastic graphmirror-descent methodmanifold regularizer
Trajectory Design and Budgeted Querying for Digital Twin Calibration
The paper introduces a framework for digital-twin calibration that jointly optimizes trajectory generation and budgeted parameter queries, combining an excitation-oriented RL controller, a recurrent parameter estimator, and a query policy. In Pendulum, their method achieves a mean absolute error of 0.0066 without queries and 0.0092 under a 3-query budget, outperforming an uncalibrated twin (error 0.2031). In Waterworld, a mixture of five controllers yields normalized errors of 4-5% for hidden parameters. The results highlight trajectory design and query allocation as critical for data-scarce calibration.
digital-twin calibrationexcitation-oriented rlrecurrent parameter estimatorbudgeted query policypartially observable systems
Domain-Aware Pruning: Sparsity and Domain Generalization via Regularized Probabilistic Masking
Domain-Aware Pruning (DAP) unifies domain generalization and network pruning by learning continuous probabilistic masks that penalize domain-sensitive weights, yielding domain-invariant subnetworks. The method replaces binary mask optimization with parameter retention probabilities $p \in [0, 1]$, regularized to discard domain-specific features. Evaluated on five DG benchmarks, DAP achieves high sparsity while matching or outperforming dense models in out-of-distribution accuracy. The framework integrates with existing DG pipelines without fine-tuning, additionally improving adversarial robustness and interpretability through domain-invariant weight retention.
domain generalizationprobabilistic maskingsparsityout-of-distribution robustnessadversarial robustness
ADEx-FNO: A Unified Ambient-Domain Framework for Fourier Neural Operators on Varying Geometries
The paper introduces ADEx-FNO, a unified framework for Fourier neural operators (FNOs) that handles varying geometries without modifying core Fourier-operator layers. The method embeds physical domains in a fixed ambient hypercube using signed distance functions, extends inputs and fields deterministically to this domain, processes them on a common latent grid via FNO, and interpolates results to target discretizations. ADEx-FNO achieves relative l2 errors of 0.32%-0.77% on nonlinear PDEs in 2D/3D and reduces pseudo-time iterations by 43%-44% in RANS cases. It also improves initialization for CFD solvers, with 18.52%-27.51% reductions in URANS physical-time advances.
fourier neural operatorssigned distance functionambient-domain extensionnonlinear pdescfd initialization
Multi-Agent Reinforcement Learning via Agent-Specific Preference
The paper introduces Multi-AGent Preference-Integrated lEarning (MAGPIE), a MARL framework that replaces global rewards with agent-specific preference modeling. Each agent is evaluated by a dedicated expert via preference signals, with theoretical guarantees of convergence to Nash equilibrium policies. Agent-specific reward models are aggregated monotonically, proven equivalent to Nash policy optimization. Experiments on benchmark tasks and a production line show MAGPIE matches reward-engineered baselines, enabling learning in reward-agnostic settings.
multi-agent reinforcement learningpreference modelingnash equilibriummonotonic aggregationdecentralized evaluation
Population-Scalable Multi-Agent World Modeling
Khora introduces a scalable multi-agent world model that supports inference-time expansion to arbitrary agent populations without retraining. The framework decouples world-state evolution from visual rendering, employing a population-agnostic rendering mechanism that queries a shared world state to maintain cross-view consistency. This design avoids dense interactions within the video generator, enabling approximately linear scaling with the number of queried views. Qualitative experiments show generalization to unseen agent counts while preserving visual quality and multi-agent consistency. A real-time interactive system demonstrates scalable open-world simulation capabilities.
multi-agentworld modelcross-view consistencypopulation-agnosticvisual rendering
LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs
The paper introduces LazyHMC, a Hamiltonian Monte Carlo (HMC) framework for infinite-dimensional probabilistic programs leveraging lazy evaluation in Haskell. It develops gradient-based HMC variants and a No-U-Turn Sampler that operate over infinite-dimensional parameter spaces, supported by a novel analysis of gradients under piecewise analytic cylindrical partitions (PACAP). The method ensures finite support for gradients despite infinite-dimensionality. Experiments demonstrate its efficacy in Gaussian mixture clustering, random walks, and piecewise-constant regression with Poisson-process changepoints.
hamiltonian monte carlolazy evaluationinfinite-dimensionalprobabilistic programmingautomatic differentiation
When Can Fraud Operations Authorize Automation? A Decision-Support Framework for Fresh Audit Evidence and Review Workload
The paper introduces freshness-constrained audit capacity (FCAC), a decision-support framework for fraud operations that balances automation authorization with action risk, evidence freshness, and review capacity. FCAC evaluates candidate action regions using mature randomized audits and a prespecified temporal allowance, automating supported regions while keeping unsupported ones under review. The framework ensures finite-sample control of unsafe authorization under representative randomized audits and label-independent evidence windows. Experiments on IEEE-CIS, ULB-Worldline, and Elliptic++ datasets demonstrate automation rates of 84.4%, 67.4%, and 81.3%, with review workloads of 24.1%, 46.0%, and 43.1%, highlighting an audit-capacity trade-off.
freshness-constrained audit capacityautomation authorizationrandomized auditsevidence freshnessreview workload
Robust Reputation-Driven Crowdsourced Federated Learning
The paper introduces R2CFL, a robust reputation-driven Crowdsourced Federated Learning (CrowdFL) framework addressing stealthy adversaries. R2CFL integrates a robust reputation model with a nearest neighbor mixing (R2-NNM) defense mechanism, linking reputation evolution to update filtering during aggregation to prevent trust accumulation by attackers. Experiments show R2-NNM matches or exceeds state-of-the-art Byzantine-robust and backdoor defenses against adaptive attackers. When combined with existing detect-and-filter defenses, the reputation model accurately reflects statistical robustness through reputation scores aligned with true positive and false positive characteristics.
crowdsourced federated learningreputation-drivennearest neighbor mixingbyzantine-robustbackdoor defense
Neural Message Passing on Structural Interaction Graphs for Fully-Inductive Graph Neural Networks
SIGIL introduces a framework for fully-inductive graph neural networks by mapping heterogeneous attributed graphs to a unified representation space. The method constructs structural interaction graphs where nodes represent input feature dimensions and edges encode multi-order feature alignment, processed via relational message-passing for dimension-agnostic embeddings. SIGIL achieves permutation equivariance and generalizes knowledge graph foundation models, demonstrating strong fully-inductive link prediction performance with pretrained models.
structural interaction graphfully-inductiverelational message-passingpermutation equivariancelink prediction
Differentiate the Solver, Not the Equation: Reverse-Sweep Adjoints for Block Implicit Simulation
We introduce solver-level differentiation, a novel approach for differentiable simulation that differentiates the executed solver rather than the converged equation. The method leverages block implicit updates to construct a reverse-sweep formulation, where the backward pass mirrors the forward solver through local adjoint solves without assembling global Jacobians. Instantiated on Vertex Block Descent, the approach achieves machine precision matching automatic differentiation, while being 33x faster and using 71x less memory. It scales to elastodynamics simulations with 10^6 contact-coupled soft bodies (8M vertices) on a single GPU, demonstrating the practical utility of solver structure for efficient differentiable simulation.
differentiable simulationblock implicit updatesreverse-sweep adjointsvertex block descentelastodynamics
OpenVisTool: An Open Recipe for Synthesizing Instructive Visual Tool-Use Trajectories
OpenVisTool introduces a framework for synthesizing instructive visual tool-use trajectories by ensuring both outcome validity (correct answers) and causal utility (tool observations contributing to answers). The method involves difficulty screening, domain-specific trajectory synthesis, and supervision verification, producing OpenVisTool-42K dataset and OpenVisTool-Bench across five visual reasoning domains. Fine-tuning on this dataset improves tool-use performance across four backbones (4B-27B), with larger models nearing closed-source system performance, demonstrating that causally grounded supervision is key for effective tool learning.
visual tool usecausal utilityoutcome validitymultimodal agentssupervision verification
Transfer Learning-Enabled Distortion Compensation for Amplitude-Phase-Time Block Modulation-Based Nonlinear Single-Carrier Wireless Communications
A transfer-learning-enabled method is proposed for distortion compensation in amplitude-phase-time block modulation (APTBM)-based nonlinear single-carrier wireless communications. The approach combines iterative clipping and filtering (ICAF) with static digital pre-distortion (SDPD) at the transmitter, while leveraging weakly supervised prior knowledge from APTBM constraints for offline pretraining of a lightweight digital post-distortion (DPoD) network at the receiver. The cascaded DPoD and clipping-noise cancellation scheme compensates for residual distortions from both the power amplifier and ICAF. Results show reliable transmission at 2 dB input back-off under 30-dBc ACLR constraints, with over 2 dB performance gain and reduced training time compared to conventional DPoD schemes.
transfer learningdigital pre-distortionamplitude-phase-time block modulationclipping-noise cancellationadjacent channel leakage ratio
MotionCraft: Latent World Modeling with Sparse Attention for Visual Upscaling
MotionCraft introduces a controllable video super-resolution framework that formulates restoration as motion-aware latent state prediction, integrating adaptive sparse attention and explicit user controls. The method combines robust motion fusion, a Latent World Transformer balancing local and non-local interactions, and a compact conditional decoder to achieve temporally consistent reconstructions under streaming constraints. Empirical evaluations demonstrate strong reconstruction and perceptual performance, with predictable trade-offs between temporal smoothness and reconstruction fidelity.
video super-resolutionlatent state predictionsparse attentionmotion fusionlatent world transformer
Curriculum Generation under Structured Parametric Environments for Robust Navigation Policies
The paper proposes a reparameterized curriculum generation framework for training robust navigation policies in structured parametric environments. The method employs unidirectional gradient-based optimization with a distribution-shift regularization objective to improve latent representations in multimodal observation spaces. Evaluated on modified Car Racing and Bipedal Walker (OpenAI Gym) environments, it outperforms seven baselines (including SPRL, ALP-GMM, and manual curricula) across five random seeds, with ablation studies confirming the curriculum mechanism's effectiveness.
curriculum generationgradient-based optimizationmultimodal observationdistribution-shift regularizationcontinuous-control
When Skills Meet Safety: Benchmarking and Characterizing the Adaptive Jailbreak Robustness of Skill-Merged LLMs
We introduce SkillSafe-Bench, a benchmark evaluating skill-merged LLMs on static refusal, adaptive jailbreak robustness, and capability retention. Merging task vectors into safety-aligned bases via task arithmetic, TIES, or DARE often compromises safety, particularly under adaptive attacks despite clean static refusal scores. Testing six open-weight bases across five families and two scales reveals that static safety does not predict robustness: fragile bases like Qwen and Gemma are jailbroken 60-76% of the time, while Llama and Phi-4 remain robust. We propose SubSafe-Merge, a method projecting task vector overlap with safety subspaces to mitigate safety erosion without compromising capabilities.
skill-merged llmsadaptive jailbreaktask arithmeticstatic refusalsafety subspace
Can Graph Learning Learn Circuits?
Graph Circuit Learning (GCL) introduces a supervised, amortized framework for circuit localization in transformers by framing it as a graph machine learning problem. GCL trains graph neural networks (GNNs) across multiple model-task pairs, modeling interactions among computational pathways represented as graph edges. Evaluated on augmented InterpBench cases, the best GCL configuration achieved a median edge AUROC of 0.902, close to EAP-IG's 0.910 but below ACDC's 0.959. Removing message-passing edges reduced performance to 0.825. Adapted PGExplainer achieved 0.858 AUROC, suggesting graph learning's potential for circuit localization.
circuit localizationgraph neural networksamortized learningaurocmessage-passing
Task-to-Model Optimization for Enterprise LLM Coding Assistants: A Data-Driven Framework for Cost-Optimal Routing
We propose Task-to-Model Optimization (T2MO), a data-driven framework for cost-optimal routing in enterprise LLM coding assistants that minimizes end-to-end cost per completed task rather than token cost. The nine-stage pipeline includes task discovery, difficulty grading, benchmark construction, candidate evaluation, and optimal mix derivation, with explicit pricing of failure escalations. The method establishes routing boundaries based on minimum pass rates and organizes decisions hierarchically by task category and difficulty tier. The framework enables developer guidance, spend forecasting, and staged transitions to intelligent routing, demonstrating weak dominance over token-cost minimization under escalation scenarios.
task-to-model optimizationcost-optimal routingdifficulty gradingrouting boundaryescalation pricing
Out-of-Distribution Federated Distillation with Domain-Aware Proxy
The paper proposes a domain-aware proxy selection framework for Federated Distillation (FD) to address Out-of-Distribution (OOD) challenges. The method enhances FD by selecting proxy data that better adapts to distribution shifts, enabling improved model generalization. Experiments demonstrate superior performance, achieving average accuracy gains of 82.9% and 80.6% over baseline methods in OOD scenarios with and without proxy data, respectively. The framework supports heterogeneous model collaboration while maintaining data privacy.
federated distillationout-of-distributiondomain-aware proxyknowledge distillationheterogeneous models
No Unique Minimizer, No Problem: On the Consistency of Robust Neural Classifiers
The authors develop a consistency theory for robust neural classifiers that addresses the non-identifiability of neural parameterizations, where the population loss minimizer forms an equivalence class rather than a unique point. They propose training via stochastic optimization over this non-identifiable parameter space using the S-divergence family, proving that empirical minimizers converge to the population-optimal equivalence class under mild regularity conditions. The theory is verified for three architecture choices, and limit points of the robust training algorithm are shown to be stationary points of the empirical objective. Experiments on vision and language benchmarks demonstrate that S-divergence training maintains clean-data accuracy while competing with existing robust methods.
non-identifiabilitys-divergencestochastic optimizationequivalence classrobust classifiers
HoloAegis: Frozen Representation, Topological Inference: Minimally Parametric Safety Manifolds for Zero-Shot LLM Guardrails
HoloAegis introduces a minimally parametric topological inference framework for zero-shot LLM safety guardrails, decoupling representation from reasoning to avoid fine-tuning distortions and high inference costs. The method employs an un-fine-tuned encoder to map text to a unit sphere, followed by geometric decision-making via Gibbs-Boltzmann Free Energy computation over a pre-computed System Topology Anchor Bank, with Dual Time-Scale Exponential Moving Averages detecting semantic drift. The approach leverages sparse anchor centroids for boundary stability against lexical perturbations. Evaluated across 8 benchmarks, HoloAegis achieves state-of-the-art accuracy (1.0000 AUC on AuthenHallu, 0.9802 on HarmBench) with sub-millisecond latency, zero cold-start data, and cross-lingual transfer (0.9758 AUC on Chinese CHIFRAUD).
topological inferencegibbs-boltzmann free energysemantic driftanchor bankboundary stability
SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks
We introduce SuperNeuroMAT, an open-source Python-based simulator for spiking neural networks (SNNs) that employs a novel matrix-based approach to model leaky integrate-and-fire neuron dynamics. The simulator supports both dense and sparse execution modes, enabling efficient simulation of up to 10,000 neurons in dense mode and 100,000 neurons in sparse mode on standard hardware without specialized accelerators. Benchmarking against NEST, Brian2, BindsNET, and snnTorch demonstrates superior performance in execution speed and memory efficiency across various network sizes and connection probabilities. SuperNeuroMAT handles diverse tasks including machine learning benchmarks (Digits, citation networks), neuromorphic vision (N-CARS, ASL-DVS), and general-purpose workloads like shortest path algorithms and arithmetic operations.
spiking neural networksleaky integrate-and-firematrix-based simulationneuromorphic computingsparse execution
MGMCL: Multi-Granularity Manifold Contrastive Learning With Neural ODEs for Cross-Subject EEG Emotion Recognition
Proposes MGMCL, a Riemannian manifold-based framework for cross-subject EEG emotion recognition that preserves affective continuity via multi-granularity contrastive learning (instance, emotion, trajectory levels) and neural ODEs for continuous dynamics. The method employs Gromov-Wasserstein alignment for cross-subject generalization and weakly-supervised learning for continuous valence-arousal-dominance prediction. Achieves state-of-the-art accuracy on SEED (91.23%), SEED-IV (73.82%), and DEAP (76.38%), with improvements of 1.89%, 1.66%, and 1.28% over prior work, respectively.
riemannian manifoldscontrastive learningneural odesgromov-wasserstein alignmenteeg emotion recognition
FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing
FSTC-Encoder introduces a unified framework for generalizable RF sensing across heterogeneous devices, environments, and modalities through feature-spatial-temporal correlation learning. The method employs structure-aware feature encoding for diverse signal structures, set-based spatial encoding for variable observations, and hierarchical temporal encoding for both local variations and long-range dependencies. Evaluated on Widar3.0, CSI-Bench, and XRF55 datasets, FSTC-Encoder achieves 92.15% mean accuracy under multi-factor cross-domain protocols, ranks first on three of four additional sensing tasks, and reduces the cross-modality performance gap from 18.85% to 12.93%. The results demonstrate robust domain generalization, task versatility, and modality extensibility.
rf sensingfeature encodingspatial encodingtemporal encodingcross-domain protocols
ARC: Augmented-Rank Conformalization for Changepoint Localization --- Finite-Sample Validity and Distribution-Robust Efficiency
ARC (Augmented-Rank Conformalization) introduces a family of conformal changepoint localization scores based on within-segment ranks, ensuring finite-sample coverage under any weight configuration, including random initialization. The method leverages rank-CUSUM location/scale channels, fixed combinations, and a pre-trained lightweight neural score, with efficiency guaranteed via an invariance theorem under strictly increasing marginal transforms. Simulations demonstrate nominal coverage, robustness to distribution shifts, and precise localization (3–5 candidates) on the well-log benchmark, though exactness breaks under serial dependence or trend-type alternatives.
conformal predictionchangepoint localizationrank-cusumfinite-sample coveragedistribution robustness
Population-Level Generative Modeling for Ranking Data
The paper proposes a generative framework for ranking data via latent preference simplex embeddings, addressing challenges in high-dimensional combinatorial spaces and population heterogeneity. The method combines likelihood-based estimation of a low-dimensional latent simplex, flow matching for population distribution learning, and probabilistic ranking models for generation. Theoretical analysis provides finite-sample guarantees on generation accuracy relative to item count, ranking length, and latent dimension. Empirical evaluation on synthetic and real datasets demonstrates improved population-level fidelity and interpretable preference heterogeneity representation.
generative modelingranking datalatent simplexflow matchingpreference heterogeneity
Optimal Learning Under Tsybakov Noise
The paper resolves a 20-year open question in PAC learning by establishing optimal error guarantees under Tsybakov noise, closing a logarithmic gap between prior upper and lower bounds. The proposed algorithm adaptively partitions the instance space into regions corresponding to different noise levels, then outputs a hypothesis satisfying region-specific error constraints within the concept class. This matches the best known lower bound, achieving optimal learning rates for general concept classes under Tsybakov's noise model.
pac learningtsybakov noiseconcept classerror guaranteesadaptive partitioning
Constrained Learning with Universally Learnable Concept Classes
The paper establishes universal PACC (Probably Approximately Correct on Constraints) learnability for constrained statistical learning over infinite-dimensional hypothesis classes in fully nonconvex settings. By formulating the population problem over a universal RKHS dense in a decomposable envelope and learning over norm balls of growing radius, the authors introduce Tikhonov complexity to quantify the RKHS norm required for ε-optimal Lagrangian level sets. They prove exact learnability of the optimal value with polynomial sample thresholds in 1/ε under a source condition and introduce the closure-realization gap ε⋆∞ to measure feasibility retrieval from dualization. Exact learnability occurs when ε⋆∞=0, otherwise near-PACC learnability with residual ε⋆∞ is achieved.
pacc learnabilitytikhonov complexitydual algorithmsrkhsclosure-realization gap
Does a Toehold Make a Bidder Bolder? Preemption and Multiplicity in Multi-Round Takeover Auctions
The study investigates the role of toeholds in multi-round takeover auctions, challenging classical single-round models. Using computational game-solving techniques, the authors analyze bidder behavior and deterrence effects with certified numerical accuracy. Key findings include: toehold profitability persists, but deterrence effects vanish in multi-round settings; preemptive bidding emerges independently of toeholds; and solver outputs vary with initialization despite convergence. The work explains the rarity of toeholds in practice and cautions against overinterpreting solver results in economic games.
toeholdtakeover auctionsgame solverpreemptive biddingdeterrence effect
Rethinking Learning-Based Influence Maximization: Simple Neural Surrogates and Native Discrete Search
The paper introduces SIMBA, a novel framework for influence maximization that replaces complex neural architectures with a lightweight approach combining anchored node embeddings, a two-layer graph neural network surrogate, and discrete search via batched multi-swap simulated annealing. The method eliminates initialization noise, focuses on graph topology and diffusion patterns, and avoids gradient-based optimization. SIMBA achieves superior influence spread and data efficiency while significantly reducing computational time compared to existing learning-based approaches.
influence maximizationneural surrogatediscrete searchanchored embeddingssimulated annealing
Exact Rank and Convex Calibration Dimension Lower Bounds for the Multi-Label F1 Loss
The paper establishes exact rank and convex calibration dimension lower bounds for the multi-label F1 loss, a central performance measure in multi-label classification. For a problem with s labels, the authors prove that the F1 score matrix, shifted loss matrix, and unshifted loss matrix all have rank s²-s+2, while the column-affine dimension is s²-s+1. Through factorization via subset-incidence matrices and a positive-definite Cauchy matrix, and by analyzing the Bayes geometry of F1, they derive a lower bound of (2/(3√3)-o(1))s² for the convex calibration dimension. This, combined with a quadratic upper bound, shows that the convex calibration dimension is Θ(s²).
multi-label classificationf1 lossconvex calibrationbayes geometryrank bounds
Safety Cost of Steering Vectors Is Separable and Reducible
The work demonstrates that safety degradation in LLMs from steering vectors stems from a separable component disrupting safety mechanisms while minimally contributing to steering objectives. A constrained optimization approach via primal-dual updates removes this component, preserving steering utility and bounding false refusal rates. Evaluations across models, behaviors, and attack suites show the method significantly reduces safety degradation with minimal utility loss, offering a post-hoc correction for activation-level interventions without safety trade-offs.
steering vectorssafety mechanismsconstrained optimizationfalse refusalactivation-level interventions
Failure-Mechanism Transferability of Cumulative-Damage Features for Health State Estimation of SiC Power Modules
The study demonstrates that input representation significantly impacts failure-mechanism transferability in health-state estimation for SiC power modules. A physics-informed Neural Ordinary Differential Equation (NODE) is benchmarked against five reference methods across two SiC power-cycling campaigns involving solder-layer fatigue and wire-bond lift-off. The NODE, when fed with cumulative thermoelectric features, maintains consistent performance across both failure mechanisms, with metrics differing only within fold-to-fold variance. In contrast, the same architecture using baseline electrical precursors performs similarly to the reference methods, which degrade on the wire-bond campaign. Results indicate that cumulative features enhance transferability more than architectural choices.
neural ordinary differential equationsic power modulesfailure-mechanism transferabilitycumulative thermoelectric featureshealth-state estimation
Physics-Informed Condition Monitoring of SiC Power Modules
We propose a physics-informed condition monitoring framework for SiC MOSFET power modules in automotive traction inverters, addressing limitations of existing approaches. The method combines physics-informed features derived from junction temperature metrics, a monotonicity constraint via gradient penalty regularization, and heavy-tailed output distributions for uncertainty calibration. This framework handles distinct aging behaviors in sintered packaging, characterized by abrupt wirebond liftoff events rather than smooth degradation. Evaluated on Infineon Technologies' industrial power cycling dataset, the approach reduces mean absolute error by 70% compared to purely data-driven baselines while maintaining stability across cross-validation folds and enabling embedded deployment.
sic mosfetphysics-informedcondition monitoringgradient penaltywirebond liftoff
Unimodality-Promoting Regularized Learning for Ordinal Regression
The study introduces a novel unimodality-promoting regularized learning (UPRL) method for ordinal regression, addressing limitations in prior approaches that inadvertently smooth predicted conditional probability distributions (CPDs). The proposed method strictly enforces unimodality while avoiding scale-related biases, improving prediction performance, particularly with small training datasets. Experiments demonstrate that unimodality promotion enhances accuracy, with analysis revealing the impact of scale-related biases on performance across varying data sizes.
ordinal regressionconditional probability distributionunimodality-promoting regularizationprediction variancescale-related bias
Correlation flow governs learning at criticality
The study establishes a theoretical connection between correlation propagation and the Neural Tangent Kernel (NTK) in infinitely wide and deep networks, demonstrating that learning dynamics are governed by information flow at criticality. Using mean-field theory and random matrix theory, the authors prove that orthogonal initialization at a critical point in the weight-bias variance plane suppresses finite-size corrections and ensures algebraic vanishing of the end-to-end Jacobian with depth. This leads to the NTK becoming proportional to output correlation at infinite depth. Theoretical predictions are validated on finite-width, finite-depth networks, highlighting the importance of orthogonal initialization in controlling deep learning dynamics.
neural tangent kernelmean-field theoryrandom matrix theoryorthogonal initializationcriticality
📰 Industry Media (4)
AI professors are negotiating the new realities of academic research
The article examines challenges faced by academic AI researchers amid industry dominance, highlighting resource disparities and shifting research priorities. Universities lack GPU access for frontier model training, forcing academics to focus on non-commercial questions like bias in LLMs (e.g., gendered prompt responses) or specialized applications (climate modeling). Researchers report funding constraints for API-based studies and public misconceptions about non-LLM AI. While some fear automation in mathematics, others view AI as a tool for scientific acceleration. Adaptation strategies include efficiency optimizations (e.g., Tim Dettmers' work) and novel architectures.
large language modelscompute constraintsmodel biasspecialized aiacademic-industry gap
Building and Validating a Quantitative Trading Strategy with OctoBot, Walk-Forward Backtesting, Parameter Optimization, and Interactive Analysis
This tutorial presents a comprehensive workflow for developing and validating a quantitative trading strategy using OctoBot and OctoBot-Script. The method involves configuring a rule-based strategy combining RSI, EMA, and ATR indicators, performing multi-parameter grid search over an in-sample period, and validating the selected parameters on an out-of-sample dataset. The workflow includes isolated environment setup, historical data retrieval, backtesting, and interactive analysis using Pandas and Plotly. Results demonstrate the strategy's performance relative to buy-and-hold, with metrics for profitability, market comparison, and execution details.
quantitative tradingwalk-forward backtestingparameter optimizationrsiatr
webAI Releases TwIL-LM: A 1.7B and 3B Formal-Logic Model Family for Autoformalization on Local Hardware
webAI introduces TwIL-LM, a family of formal-logic models comprising 1.7B and 3B parameter variants for autoformalization tasks. TwIL-LM3, the 3B model, is fine-tuned via LoRA supervised training, checkpoint fusion, WiSE-FT interpolation (λ = 0.25), and MGPO entropy-weighted GRPO. Both models target first-order logic translation and entailment classification, optimized for local execution with quantized builds (1.06 GB for 1.7B, 1.78 GiB for 3B). TwIL-LM3 achieves 0.4488 on a six-lane formal-logic benchmark, outperforming LFM2.5-8B-A1B (0.3757) but trailing gpt-oss-120b (0.5192), while generating 32.9 answers per second. The models are released under a non-commercial license.
autoformalizationlorawise-ftentailmentquantization
Implementing a MiniMax-H3 Multimodal Video and Audio Generation Pipeline with ComfyUI APIs
This work presents a reproducible pipeline for MiniMax-H3 multimodal video generation using ComfyUI as a headless inference backend. The method integrates GPU-aware model profile selection, automated setup of diffusion, text-encoder, video-VAE, and audio-VAE weights from Hugging Face, and schema-aware graph construction via ComfyUI's HTTP/WebSocket APIs. Results demonstrate support for text-to-video, frame-conditioned, and reference-image-conditioned generation modes, with dynamic hardware optimization and joint video-audio decoding. The pipeline achieves reproducible experimentation without reliance on ComfyUI's graphical interface.
multimodal generationdiffusion modelsschema-aware graphvideo-vaeheadless inference
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