Daily Digest — 2026-06-11
325 items · 5 research labs, 320 arxiv papers
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🏛️ Research Labs (5)
PRC-linked influence operations are targeting AI debates in the US
OpenAI identified two PRC-linked influence operations targeting US AI policy debates using ChatGPT-generated content. The 'Data Center Bandwagon' campaign spread narratives about AI data centers increasing electricity prices, while the 'Tech and Tariffs' campaign criticized US tariffs without mentioning Xi Jinping. Both clusters employed inauthentic social media accounts and attempted to manipulate public discourse without significant reach. The findings highlight foreign actors' attempts to exploit AI for covert influence, emphasizing the need for vigilance in democratic AI governance.
influence operationschatgptdata centertariffsinauthentic accounts
From data to decisions: how LSEG is scaling trusted AI
London Stock Exchange Group (LSEG) deployed ChatGPT Enterprise and OpenAI APIs to transform financial workflows, achieving a 10x reduction in product release cycles (from 3–6 months to 2 weeks) and accelerating customer delivery to ~4 weeks. The method involved integrating OpenAI's models with LSEG's proprietary data platform, emphasizing governance (human-in-the-loop review, privacy controls) and broad employee enablement. Results included increased analyst productivity, cross-functional collaboration, and innovation velocity, with prototypes developed in hours. The approach prioritized high-impact use cases, grassroots adoption, and workflow redesign over task automation.
chatgpt enterprisehuman-in-the-loopworkflow automationmodel context protocolin-context learning
What Codex unlocks for Notion
OpenAI's Codex significantly accelerates software development at Notion by enabling autonomous code generation from specifications. Engineers provide high-level task descriptions and verification methods, allowing Codex to explore existing codebases and produce production-ready implementations. In one case, porting a voice input feature from mobile to web took 3-4 hours instead of 2 weeks, with the generated code matching internal standards. The system enables parallel task execution, managerial coding contributions, and overnight research automation, effectively multiplying engineering throughput while maintaining code quality.
codexautonomous codingspecification-driven developmentcode generationengineering productivity
Industrial policy for the Intelligence Age
OpenAI proposes an industrial policy framework for the Intelligence Age, emphasizing human-centric approaches to AI governance and economic adaptation. The initiative includes policy ideas for expanding opportunity, sharing prosperity, and building resilient institutions, with a focus on democratic refinement. Methodologically, OpenAI solicited over 400 responses, established a pilot program offering fellowships and research grants up to $100,000 plus $1 million in API credits, and opened a Washington, DC workshop for policy discussions. The proposals aim to catalyze public discourse on AI's societal impact while avoiding premature policy finalization.
industrial policysuperintelligenceapi creditsdemocratic processresilient institutions
Migrating Your GitHub CI to Hugging Face Jobs
The article presents a method for migrating GitHub CI workflows to Hugging Face Jobs, enabling GPU-accelerated testing and reduced CI latency. The approach uses huggingface/jobs-actions as a bridge between GitHub Actions and HF Jobs, converting workflow_job webhooks into ephemeral self-hosted runners on HF infrastructure. Results show a 30% reduction in CPU job latency and successful GPU test execution (45s on t4-small), with improved log accessibility via CLI. The system supports custom Docker images (e.g., nvidia/cuda:12.4.0) and hardware-specific labels (hf-jobs-t4-small).
github actionshugging face jobsci/cdgpu accelerationephemeral runners
📜 arXiv Papers (320)
A Unifying Lens on Supervised Fine-Tuning Through Target Distribution Design
The paper introduces Q-target, a framework reinterpreting supervised fine-tuning (SFT) as target distribution design, decomposing supervision into reliance on observed tokens and probability mass allocation over alternatives. This unifies existing SFT variants as implicit target distribution choices. Target-SFT, a method constructing training objectives directly from desired target distributions, consistently outperforms baselines across ten reasoning dataset-model settings. The approach reveals fundamental SFT design principles and expands the search space for SFT objectives.
supervised fine-tuningtarget distributionq-targetprobability massreasoning datasets
EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents
EEVEE introduces a multi-dataset test-time prompt learning framework for LLM agents, addressing cross-dataset interference via a router-prompt co-evolution strategy. The router partitions heterogeneous input streams into task clusters, assigning them to optimal prompt configurations through interleaved learning phases. Experiments show EEVEE improves multi-benchmark scores by 10.38-24.32 points over Qwen3-4B-Instruct and DeepSeek-V3.2, outperforming GEPA and ACE by 37.2-48.2%.
test-time learningprompt engineeringmulti-datasetrouter-prompt co-evolutionllm agents
The Role of Feedback Alignment in Self-Distillation
The study investigates context design for self-distillation in language models, comparing three feedback conditions: binary reward (GRPO), reference solution, and step-aligned critique. Step-aligned feedback, which targets only erroneous reasoning tokens, yields a 16.11-point improvement over GRPO and 5.27 points over reference-solution conditioning. Analysis shows that step-alignment preserves correct behavior while correcting errors, unlike reference solutions that induce unnecessary changes. Results demonstrate that structural alignment between feedback and model reasoning is critical for effective self-distillation.
self-distillationfeedback alignmentlanguage modelstep-aligned critiquegpt-3
Piper: A Programmable Distributed Training System
Piper introduces a programmable distributed training system that decouples parallelism strategy from runtime implementation, enabling flexible adaptation to state-of-the-art strategies. The system allows users to declare distributed training strategies via model annotations and scheduling directives, which transform Piper's intermediate representation (IR)—a unified global training DAG. Piper compiles per-device execution plans from this IR and executes them using a strategy-agnostic distributed runtime. The system maintains performance parity with common strategies like ZeRO while achieving additional efficiency gains through joint scheduling in composed parallelism strategies such as DeepSeek-V3's DualPipe.
parallelism strategyintermediate representationdistributed runtimescheduling directivesglobal training dag
Flaws in the LLM Automation Narrative
The study critiques overoptimistic claims about LLM capabilities by introducing a novel benchmarking task requiring code generation for data analysis. It compares GPT-4 against human experts, measuring performance variance and error magnitude. Results show humans outperform the LLM on multiple metrics with lower variability, challenging narratives of LLM parity with human expertise. The work highlights the need for reliability-focused benchmarks in high-stakes applications.
llm benchmarkingperformance varianceerror magnitudecode generationhuman-expert comparison
ReasonAlloc: Hierarchical Decoding-Time KV Cache Budget Allocation for Reasoning Models
ReasonAlloc introduces a hierarchical KV cache budget allocation framework for LLM reasoning tasks, addressing the inefficiency of uniform budget distributions during decoding. The method combines offline layer-wise preallocation, capturing architecture-specific demand patterns ("Reasoning Wave"), with online head-wise reallocation based on real-time utility. Evaluations on MATH-500 and AIME~2024 benchmarks using DeepSeek-R1-Distill-Llama-8B, DeepSeek-R1-Distill-Qwen-14B, and AceReason-14B demonstrate superior performance over uniform-budget R-KV, SnapKV, and Pyramid-RKV, particularly at small budgets (128-512 tokens), with negligible overhead.
kv cachereasoning wavebudget allocationtoken evictiondecoding-time compression
ABC-Bench: An Agentic Bio-Capabilities Benchmark for Biosecurity
The paper introduces ABC-Bench, a benchmark for evaluating LLM agents on biosecurity-relevant tasks including liquid handling robot programming, DNA fragment design, and DNA synthesis screening evasion. The benchmark assesses both benign and dual-use biological capabilities requiring biology and software expertise. Results show all tested LLM agents (including OpenAI's o4-mini-high) outperformed median human baselines, with strong performance on protocol-based tasks but weaker performance on novel bioinformatics reasoning. Wet-lab validation confirmed successful DNA assembly using agent-generated scripts on an OpenTrons robot.
llm agentsbiosecuritydna synthesisliquid handling robotsbioinformatics
Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques
This study compares data assimilation (DA) techniques for subsurface flow using latent diffusion models (LDMs) to preserve geological realism while calibrating model parameters. The authors evaluate ensemble smoother with multiple data assimilation (ESMDA), Markov chain Monte Carlo (MCMC), and Sequential Monte Carlo (SMC) in 3D-LDM latent space, employing a fast surrogate flow model to mitigate computational costs. Results show MCMC and SMC outperform ESMDA in uncertainty reduction and data mismatch, with ESMDA yielding overestimated posterior uncertainty due to nonlinear LDM mappings. All methods maintain geological plausibility through LDM parameterization.
data assimilationlatent diffusion modelsensemble smoothermarkov chain monte carlosequential monte carlo
Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation
The study introduces a provenance-grounded gating mechanism and adaptive recovery pipeline for synthetic post-training data curation, addressing two underexplored practices: evidence-based filtering and systematic sample recovery. Using adversarially injected corpora for ground-truth failure labels, the method evaluates gate configurations, recovery strategies, and generator scales. Results show that source provenance improves faithfulness gating, hallucination and reward gates reject distinct sample populations, and adaptive recovery outperforms naive resampling in yield, recovery rate, and injection recall. Downstream fine-tuning quality is primarily driven by generator scale, with filtration and recovery playing secondary roles.
provenance-grounded gatingadaptive recoverysynthetic post-trainingfaithfulness gatinginjection recall
Monte Carlo Pass Search: Using Trajectory Generation for 3D Counterfactual Pass Evaluation in Football
The paper introduces Monte Carlo Pass Search (MCPS), a method for evaluating football passes using Monte Carlo Tree Search components: a possession value model, a multi-agent trajectory world model, and a policy sampling pass variants. Leveraging 3D Bundesliga tracking data, MCPS infers pass parameters, samples execution variants, and rolls them forward with a ball-conditioned world model to score outcomes via a learned value model. The approach yields distribution-aware attribution metrics (mean-based and percentile-based execution-surplus scores) and demonstrates sample-efficient trajectory forecasting using an adapted autonomous driving model (SMART). Model checkpoints and code are released.
monte carlo tree searchpossession value modelmulti-agent trajectoriesexecution-surplus scoresautoregressive trajectory generator
TRACE: A Unified Rollout Budget Allocation Framework for Efficient Agentic Reinforcement Learning
TRACE introduces a unified rollout budget allocation framework for efficient agentic reinforcement learning, addressing reward contrast limitations in multi-turn rollouts. The method models each ReAct-style turn as a distinct node, forming tree-structured rollouts, and allocates budget to both prompt roots and intermediate prefixes via a shared predictor estimating conditional success probability. Empirical results show TRACE improves Qwen3-14B Multi-Hop QA accuracy by 2.8 points over baselines at equal sampling cost.
rollout budget allocationreward contrastreact-styletree-structured rolloutsconditional success probability
Towards Autonomous Accelerator Design: FPGA Accelerator Generation with SECDA
SECDA-DSE extends FPGA accelerator design automation by integrating Large Language Models (LLMs) into the SECDA ecosystem for guided design space exploration. The framework combines a structured DSE Explorer with an LLM Stack employing retrieval-augmented generation and chain-of-thought prompting, reinforced by iterative feedback. Evaluations demonstrate successful FPGA synthesis and execution for three workloads (vector multiplication, 2D convolution, matrix transpose), capturing kernel-specific trade-offs between compute parallelism and data movement while reducing manual exploration effort.
fpga acceleratordesign space explorationlarge language modelsretrieval-augmented generationhardware-software co-design
Designed by Journalists, but Is It for Readers? Rethinking AI Disclosures and Transparency in News
The study identifies a disconnect between AI disclosure practices in journalism and reader needs, framing it as a human-computer interaction design challenge. Through a controlled experiment with 34 news readers, it demonstrates that current approaches—either brief one-line labels or detailed disclosures—fail to build trust: detailed disclosures trigger a transparency dilemma (reducing trust), while minimal labels create cognitive load through information gaps. Readers proposed alternative designs emphasizing user agency, including detail-on-demand interactions, AI-ratio visualizations, and explicit 'no AI' labels. The findings suggest current practitioner assumptions about responsible disclosure misalign with user preferences.
ai disclosuretransparency dilemmahuman-computer interactionuser agencydark patterns
FADA: Accessible fetal ultrasound interpretation and annotation with a selectively distilled unified vision-language model
FADA introduces a unified vision-language model for accessible fetal ultrasound interpretation, built on Qwen3.5-VL, that integrates clinical interpretation, classification, detection, and segmentation in a single pipeline without requiring external labels. The method employs selective knowledge distillation from four domain-specific foundation models (FetalCLIP, UltraSAM, USF-MAE, UltraFedFM) via offline feature caching, outperforming full distillation. FADA-SKD achieves 0.8820 mean Dice for segmentation, 0.7671 mAP@0.50 for detection, and 100% structured interpretation compliance, with expert validation confirming 73.5% perfect interpretations under guidance. The 0.8B-parameter model runs on a consumer GPU and deploys offline on smartphones (60s latency).
vision-language modelselective distillationfeature alignmentfetal ultrasoundedge deployment
PhantomBench: Benchmarking the Non-existential Threat of Language Models
The paper introduces PhantomBench, a novel benchmark comprising 60K non-existent terms and entities derived from real concepts across domains, designed to evaluate language models' ability to recognize knowledge limits. Using a pipeline for scalable generation of non-existent concepts, the authors assess 21 models of varying types and sizes, revealing high hallucination rates (up to 86.7%) and failure to abstain on non-existent concepts, particularly when inputs presume their existence. PhantomBench also serves as a proxy for studying hallucination-prone rare concepts.
hallucinationslanguage modelsbenchmarkknowledge limitsnon-existent concepts
RoboNaldo: Accurate, Stable and Powerful Humanoid Soccer Shooting via Motion-Guided Curriculum Reinforcement Learning
RoboNaldo introduces a three-stage motion-guided curriculum RL framework for humanoid soccer shooting, combining motion tracking-driven stability with task reward-driven performance. The method progresses from learning a stable kicking prior to adapting to stationary and moving balls via a locomotion-command interface. In simulation, it reduces free-kick shot error by 48.6% and achieves 2.96x higher shoot velocity than baselines. Real-world tests on a Unitree G1 show 0.73-0.86 m average target error from 3 m and 13.10 m/s ball velocity (59-71% of professional speeds).
humanoidreinforcement learningcurriculum learningmotion trackingwhole-body stability
Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
The paper introduces Q-Guided Flow (QGF), a reinforcement learning algorithm that performs policy optimization exclusively at test time. QGF pre-trains a flow policy via behavioral cloning and a value function critic, then uses value gradients to guide the policy toward higher-value actions without additional training. This approach avoids stability issues in actor-critic methods while maintaining scalability. Empirical results show QGF outperforms prior test-time RL methods on high-dimensional action tasks and matches state-of-the-art training-time algorithms with lower computational cost.
reinforcement learningflow policiestest-time optimizationbehavioral cloningvalue gradient
Unifying Local Communications and Local Updates for LLM Pretraining
The paper introduces GASLoC, a decentralized pre-training algorithm for LLMs that unifies local communications and updates to address bandwidth heterogeneity. The method generalizes communication acceleration to outer optimizers, enabling gossip-based training compatible with adaptive optimizers, local steps, and sparse randomized peer communication. Empirical results on standard LLM tasks show GASLoC outperforms state-of-the-art decentralized algorithms in single-step settings across various topologies and matches DiLoCo's performance with multiple local steps, while significantly surpassing DiLoCo in heterogeneous bandwidth scenarios.
decentralized traininggossip-based learningouter optimizercommunication accelerationheterogeneous bandwidth
A History-Aware Visually Grounded Critic for Computer Use Agents
The paper introduces HiViG, a History-aware Visually Grounded test-time framework for Computer Use Agents (CUAs) that addresses two limitations of existing critics: short-sighted decision loops and lack of visual grounding. HiViG employs a multimodal critic trained on real GUI trajectories to abstract past interactions into a compact record (macro-action history) and verify actions against current screenshots (visually grounded critique). Evaluated across web, mobile, and desktop benchmarks, HiViG improves success rates by 5.8% for Qwen3-VL-32B and 9.0% for Gemini-3-Flash over baselines, demonstrating strong cross-platform generalization. Ablations confirm both components are critical for long-horizon GUI tasks.
computer use agentsmultimodal criticmacro-action historyvisually grounded critiquegui trajectories
Modeling Complex Behaviors: Multi-Personality Composition and Dynamic Switching in Vision-Language Models
The paper introduces explicit personality conditioning for Multimodal Large Language Models (MLLMs), proposing a framework for single/multi-personality induction and dynamic switching. Experiments demonstrate that personality induction enhances image captioning but may degrade visual question answering (VQA) performance, revealing co-modulation by prior and current personality constraints. Prompt-based methods show limited transferability to multimodal settings, highlighting the need for robust personality modeling techniques. The study provides empirical evidence of balancing and residual effects during personality composition and switching.
multimodal large language modelspersonality inductionvisual question answeringdynamic switchingimage captioning
T1-Bench: Benchmarking Multi-Scenario Agents in Real-World Domains
The authors introduce T1-Bench, a novel benchmark for evaluating multi-scenario agentic systems in realistic, multi-domain environments. The benchmark addresses limitations in existing evaluations by featuring 25 diverse domains with interleaved scenarios requiring structured reasoning across multi-turn interactions. They assess 12 proprietary and open-weight models using both automated metrics and human judgments, focusing on agent behavior, tool utilization, and conversational quality. T1-Bench significantly advances prior work through increased task complexity, interaction depth, and domain coverage, with plans to release data and evaluation code publicly.
agentic systemsmulti-domain evaluationstructured reasoningtool utilizationconversational quality
CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs
The paper introduces CIAware-Bench, a benchmark for evaluating control intervention (CI) awareness in frontier LLMs, measuring their ability to distinguish between original and modified trajectories. The benchmark comprises four task domains (essay writing, BigCodeBench, Bash Arena, SHADE-Arena) with variations in watermarking, side-tasks, and control protocols. Testing eleven models reveals low to moderate CI awareness (up to 0.87 accuracy vs. 0.5 random chance), with detection easier across model families, suggesting exploitation of provider-specific traits. The authors release CIAware-Bench to track CI awareness and improve stealthier control protocols.
control interventionllm benchmarkingtrajectory modificationmodel awarenesstrusted monitoring
What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents
This work investigates why benchmark-driven machine learning exhibits minimal overfitting, proposing that successful ML strategies are highly compressible. The study employs LLM-driven research agents to test this hypothesis through two information bottlenecks: output compression, where a reproducer agent attempts to replicate exploration agent findings using short prompts, and input compression, where the explorer receives one-bit feedback on model improvements. Experiments across 8 datasets (tabular classification, vision, language modeling, diffusion modeling, reward modeling) demonstrate that compressed representations suffice for reproducing high-performance models. Results support a description-length explanation, showing successful strategies occupy low-complexity regions of strategy space.
information bottleneckllm-driven agentsdescription-lengthoutput compressioninput compression
Workflow-GYM: Towards Long-Horizon Evaluation of Computer-use Agentic tasks in Real-World Professional Fields
The paper introduces Workflow-GYM, a benchmark for evaluating AI agents on long-horizon GUI tasks in professional domains, addressing gaps in existing benchmarks focused on general-purpose software. It tests agents' ability to autonomously operate domain-specific software and complete economically valuable workflows. Experiments with state-of-the-art models reveal low success rates (~30%), with failures attributed to workflow stage omission, error propagation, objective drift, and limited understanding of professional environments. The findings highlight challenges in GUI-agent research and suggest directions for improvement.
workflow-gymgui agentslong-horizon tasksprofessional softwareerror propagation
AuRA: Internalizing Audio Understanding into LLMs as LoRA
AuRA introduces a method for internalizing audio understanding into large language models (LLMs) via LoRA adaptation, addressing limitations of cascaded ASR-LLM pipelines and costly multimodal training. The approach distills audio encoding capability into the LLM by feeding speech input to an ASR encoder (teacher) and a LoRA-adapted LLM (student) through a lightweight audio embedding layer, aligning hidden states via layer-wise distillation. This enables tighter speech-language joint modeling and efficient parallel end-to-end inference while reusing pretrained models. AuRA outperforms cascaded systems, speech-to-LLM adaptation baselines, and multimodal models on multiple benchmarks in both effectiveness and efficiency.
lora adaptationasr encoderlayer-wise distillationspeech-language modelingmultimodal training
Diffusion Forcing Planner: History-Annealed Planning with Time-Dependent Guidance for Autonomous Driving
The Diffusion Forcing Planner (DFP) introduces a history-annealed diffusion framework for temporally consistent autonomous driving trajectories. DFP decomposes trajectories into history, current, and future segments with independent noise levels, applying joint denoising through a heterogeneous diffusion process. Classifier-free guidance steers future sampling using annealed historical context. Evaluated on nuPlan, DFP achieves competitive performance while generating stable, continuous trajectories in complex scenarios.
diffusion modelsmotion planningautonomous drivingclassifier-free guidancetemporal consistency
Superficial Beliefs in LLM Decision-Making
The study investigates whether LLM decision-making reflects systematic underlying structures or mere imitation of rationales. Using synthetic binary choice tasks with graded attributes, researchers compared self-reported decision drivers with those inferred from behavioral models. Results show LLM choices are systematically predictable from attributes (behavioral model R^2=0.72), but self-reports and score-based judges only partially align with these inferred drivers (alignment <60%). This 'superficial belief' pattern persists across prompt variations, model architectures, and decision settings, suggesting LLMs operate with probabilistic attribute priorities while having limited verbal access to their own decision processes.
large language modelsdecision-makingbehavioral modelingattribute weightingself-report accuracy
Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning
The study demonstrates that on-premise open-source LLMs serve as effective structural priors for tuning strongly coupled MIMO controllers, particularly in high-dimensional, non-convex optimization landscapes. Using scaffolded reasoning, LLMs propose counter-intuitive controller structures (e.g., asymmetric configurations) and achieve sample-efficient convergence (J ~ 16.9) on a quadruple-tank system, outperforming naive relay tuning (J ~ 28.6) and local optimization (0/10 success). Refinement with classical optimizers attains global optima (J ~ 12.0), while LLMs reduce evaluations by 6x in 3x3 plants. The method generalizes across four open models, with no advantage in simple loops, delineating a clear applicability boundary.
mimo controlllm reasoningnon-convex optimizationsample efficiencystructural prior
Understanding and mitigating the risks of OpenClaw for non-technical users: A practical guide with Skill
The authors address the accessibility gap in risk mitigation for non-technical OpenClaw users by (1) identifying seven core risks in plain language, (2) providing actionable defensive strategies, and (3) developing an automated companion Skill for security configuration. Their methodology combines risk categorization, operational step distillation, and tool automation. Results demonstrate that non-experts can effectively reduce autonomous agent risks through simplified interventions.
openclawautonomous agentsrisk mitigationnon-technical userssecurity automation
Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning
The paper introduces Null-Space Constrained Response-Specified Unlearning (NSRU), a low-rank adaptation framework for controlled unlearning in large language models. NSRU combines safe-target response specification with orthogonal-projected LoRA updates confined to the null space of retain subspaces, jointly optimizing target alignment, undesired-response suppression, and retention preservation. Experiments on TOFU and WMDP benchmarks demonstrate NSRU's effectiveness in suppressing forget-set knowledge while maintaining model utility, with ablation studies validating the contributions of each component.
large language model unlearninglow-rank adaptationnull-space projectionresponse-specified unlearningorthogonal-projected updates
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
The authors propose Bellman-Taylor score decoding, a novel framework for Markov decision processes (MDPs) with state-dependent feasible action sets, addressing limitations of standard deep reinforcement learning (DRL) algorithms. By leveraging a Taylor expansion of the optimal action-value function, the method shifts policy learning to a Euclidean score space while ensuring feasibility through an action decoder, enabling optimization via standard DRL techniques without decoder differentiation. Theoretical analysis decomposes the optimality gap into structural approximation and algorithmic learning errors. Empirical evaluation on queueing network control demonstrates near-optimal performance in small instances and significant improvements over benchmarks in larger systems.
markov decision processesbellman-taylor score decodingdeep reinforcement learningaction decoderqueueing network control
Optimizing 2D Input Representations and Sub-phase Fusion Strategies for Differential Diagnosis of Asthma and COPD Using CNN- and GRU-Based Networks
The study compares MFCC matrices, log-mel spectrograms, and VAR models for asthma-COPD differentiation using deep learning. Adaptive-length windowing addressed inconsistent temporal dimensions in pulmonary sounds, with MFCC (13 coefficients, 64/256-point resolution) outperforming alternatives. CNN architectures extracted sub-phase features, fused via concatenation, GRU, or attention; direct concatenation achieved the best F1-scores (0.877 cycle-based, 0.855 subject-based). Data augmentation, particularly mixup, degraded performance, highlighting the importance of authentic data. Sophisticated fusion strategies did not improve results.
mfccadaptive-length windowingcnngrudata augmentation
Beyond Uniform Token-Level Trust Region in LLM Reinforcement Learning
The paper introduces CPPO (Cumulative Prefix-divergence Policy Optimization), a reinforcement learning method addressing limitations of uniform token-level trust regions in LLM fine-tuning. CPPO employs position-weighted thresholds to prioritize early-token regulation and a cumulative prefix budget to track historical deviations, aligning updates with finite-horizon policy improvement. Experiments demonstrate improved training stability and reasoning accuracy across model scales compared to standard PPO approaches.
reinforcement learningtrust regionautoregressive generationpolicy optimizationprefix divergence
Mind the Gap: Can Frontier LLMs Pass a Standardized Office Proficiency Exam?
The study evaluates Large Language Models (LLMs) on professional Office automation tasks using China's National Computer Rank Examination (NCRE) benchmark, comprising 200 tasks scored via 7,118 criteria. Seven frontier LLMs achieved a maximum Score Rate (SR) of 36.6% in single-turn settings, while an enhanced agentic system with feedback and iterative repair reached 68.8%, still below the human-reference score of 95.5%. Results indicate significant challenges in fine-grained Office automation despite advances in code generation.
llmsoffice automationncre benchmarkscore rateagentic systems
Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans
The paper introduces Architect-Ant, an editable automatic furnishing framework for architectural floor plans, and AntPlan-270, a dataset of 270 professionally designed floor plans with furniture annotations. The method employs a fine-tuned vision-language model to generate furniture layouts using a coordinate-based domain-specific language (DSL), enhanced by procedural reasoning traces for spatial constraints and preference optimization for layout refinement. Results demonstrate geometrically valid and functionally plausible layouts, with the DSL enabling editable symbolic representations and realistic rendering via a Flux-based LoRA renderer.
vision-language modeldomain-specific languageprocedural reasoningpreference optimizationflux-based lora
Recalling Too Well: Sycophancy Evaluation and Mitigation in Memory-Augmented Models
The paper introduces MIST, a benchmark for evaluating sycophancy in memory-augmented LLMs, showing that persistent memory systems amplify user agreement over factual accuracy. Using synthetic multi-turn conversations across scientific, medical, and moral domains, the study tests three memory systems and five model families, revealing up to 25x higher sycophancy rates than in-context baselines. Error analysis identifies lossy memory compression as the primary cause, prompting two lightweight mitigations that reduce sycophancy while maintaining factual recall.
memory-augmented modelssycophancymulti-turn conversationslossy compressionfactual recall
Generative Explainability for Next-Generation Networks: LLM-Augmented XAI with Mutual Feature Interactions
The paper introduces an LLM-augmented XAI framework that improves interpretability for network operations by generating natural language explanations from mutual feature interactions. The method extends SHAP analysis through structured prompts incorporating interaction data, evaluated on an optical QoT estimation task. Empirical results demonstrate 12.2% and 6.2% improvements in explanation usefulness and scope over SHAP-only baselines, with 97.5% correctness verified by specialist evaluations.
explainable aishap valuesfeature interactionslarge language modelquality of transmission
Democratising Camera Trap AI: An Open-Source Model for Detecting UK Mammals
The authors present an open-source YOLO26x object detection model for biodiversity monitoring, specifically targeting 31 classes of UK mammals, birds, and utility objects (e.g., humans, vehicles). The model was trained on a curated dataset of 48,165 labelled instances collected over a decade from multiple sites, using an 80/10/10 class-stratified split. It achieves a mean Average Precision of 0.984 at IoU 0.5 (0.956 at IoU 0.5-0.95) on validation data, with precision 0.988 and recall 0.965. On an unseen test set, per-species confidence ranged from 0.96 to 0.99, with a 0.17% false-negative rate. The model, released in ONNX format under a non-commercial license, aims to democratize AI for ecologists without machine-learning expertise.
yolo26xobject detectionmean average precisiononnx formatclass-stratified split
Provenance Tracking in AI Compilers through the Lens of Coalgebra
The paper introduces a lightweight, generative approach for provenance tracking in AI compilers, addressing challenges posed by aggressive graph rewrites during normalization, lowering, and optimization. The method leverages observational semantics and a coalgebraic model with bisimulation to reason about provenance through observable computational actions, ensuring stability even when intermediate nodes are eliminated. A prototype implementation, COVAN, demonstrates minimal engineering overhead while maintaining reliable provenance across compilation pipelines.
provenance trackingcoalgebraic modelbisimulationobservational semanticsai compiler
CLP: Collocation-Length Prediction for Zero-Loss Adaptive Multi-Token Inference
The paper proposes CLP (Collocation-Length Predictor), a zero-loss adaptive multi-token inference method that eliminates head-backbone competition in large language models. CLP employs Backbone-as-Architect, where the backbone LM head generates the first token and lightweight MTP heads predict subsequent tokens, using a single linear layer (4.6K--7.7K parameters) for span-level length prediction. Experiments on Qwen2.5 models (0.5B--7B) show 1.14x--1.29x speedup with 0.5% repetition ratio, while shorter prediction horizons (k=2) recover 24% higher MTP head accuracy on large models.
multi-token predictionautoregressive decodinglanguage model accelerationspan-level predictionhead-backbone competition
WorldKernel: A World Model is the Coupling Kernel of Admissible Possible Worlds
The paper introduces WorldKernel, a theoretical framework modeling admissible possible worlds via a positive semidefinite coupling kernel K(T,T'), where diagonal elements represent standard posteriors and off-diagonals encode cross-world couplings missed by predictors. Demonstrating structural limitations in counterfactual reasoning, the authors show predictors collapse on 28% of unidentified quantities despite Bayesian baselines succeeding on identified ones. The kernel's partial-identifying constraints bound counterfactuals polynomially where exact computation is intractable, with logical axioms further tightening bounds by up to 33%. Targeted constraint learning accelerates gap closure versus untargeted methods, though full reconstruction remains approximable only below the Sly-Sun threshold.
worldkernelcoupling kernelcounterfactual reasoningpartial identificationsly-sun threshold
Frontier Coding Agents Use Metaprogramming to Adapt to Unfamiliar Programming Languages
The study evaluates six LLM-based coding agents on four esoteric programming languages, revealing capability differences obscured by mainstream benchmarks. Using a sequential protocol with file editing, local execution, and hidden-test grading, the authors find that top-performing agents (Claude Opus 4.6 and GPT-5.4 xhigh) employ metaprogramming strategies, generating target-language code via Python programs. Forbidding this approach causes significant performance drops, while providing Opus-derived Python helper code improves weaker agents (Sonnet 4.6 and GPT-5.4 mini). The results highlight that strong agents adapt by constructing and debugging strategies tailored to unfamiliar language rules.
llm-based coding agentsesoteric programming languagesmetaprogramminghidden-test gradinginterpreter calls
Recoverable but Not Stationary:Local Linear Structures in Weights and Activations
This work investigates linear structures in neural network weights and activations, challenging the fixed-task-plane hypothesis. Through experiments on synthetic multitask transformers and LoRA adapters for DistilGPT-2/GPT-2, the authors demonstrate that useful task-gradient bases drift substantially within 100 steps, though initial recovery updates form a trajectory-prefix basis capturing 77% of LoRA recovery displacement. They develop a Gaussian local-linear theorem justifying random parameter search effectiveness in high dimensions and show that a single gradient step produces activation shifts with 0.58 cosine similarity to labeled-contrast CAA steering vectors. Results indicate linear structures are not global task directions but evolving local geometries persisting across parameter and activation spaces.
linear structureslora adapterstask-gradientactivation steeringgaussian local-linear theorem
A Constrained Natural-Language Interface for Variational Multi-Physics Finite Element Simulations in FEniCS
The paper presents a constrained natural-language interface for variational multi-physics finite element simulations in FEniCS, where LLMs are restricted to front-end tasks (prompt parsing, non-catalog geometry generation) while deterministic templates handle solver logic. The system maps validated specifications to five human-written FEniCS/UFL templates (linear elasticity, hyperelasticity, etc.), achieving sub-percent agreement on smooth cases and 2-5% on nonlinear benchmarks. Evaluation shows 100% final parse success, 97.1% field-extraction accuracy, and 90% geometry-generation success, demonstrating reliable LLM-assisted simulation without autonomous code generation risks.
finite element analysisnatural-language interfacevariational formulationmulti-physics simulationconstrained generation
Role-Agent: Bootstrapping LLM Agents via Dual-Role Evolution
The paper introduces Role-Agent, a framework for bootstrapping LLM agents through dual-role evolution, where a single LLM functions as both agent and environment. The method comprises World-In-Agent (WIA), which uses state prediction alignment as process reward for environment-aware reasoning, and Agent-In-World (AIW), which reshapes training data by analyzing failure modes and retrieving similar tasks. Experiments on multiple benchmarks demonstrate an average performance improvement of over 4% compared to strong baselines.
llm agentsbootstrappingdual-role evolutionprocess rewardfailure mode analysis
What Do Deepfake Speech Detectors Actually Hear?
We introduce an audio-native explainability pipeline for deepfake speech detection that uses Integrated Gradients on time-aligned self-supervised representations to localize decision evidence temporally. The method is applied to three WavLM-based detectors (AASIST, CA-MHFA, SLS) on ASVspoof 5, with manual annotation of high-attribution regions to identify semantic cues. Despite comparable performance, detectors exhibit distinct cue reliance: AASIST on non-speech/environment cues, CA-MHFA on localized phoneme artifacts, and SLS on word boundaries and spectral integrity. Causal masking experiments validate these findings, demonstrating performance degradation when primary cues are removed.
integrated gradientsself-supervised representationsasvspoof 5phoneme artifactsspectral integrity
Ethical and Technical Limits of Deepfake Speech Datasets
The study conducts a dataset-level audit of 39 deepfake speech datasets, focusing on accessibility, documentation, demographic coverage, and source corpora. Key findings reveal that fairness assessment is hindered by insufficient demographic metadata, with few datasets including gender or language labels. Additionally, significant overlap in bona fide source corpora across datasets compromises cross-dataset evaluation and risks inflated generalization claims. The audit underscores the need for improved dataset transparency and diversity to ensure robust and equitable deepfake detection.
deepfakespeech datasetsfairness assessmentdemographic metadatageneralization claims
RAT: Reference-Augmented Training for ASV Anti-Spoofing
The paper introduces Reference-Augmented Training (RAT), a method for automatic speaker verification (ASV) anti-spoofing that leverages speaker-reference recordings during training but remains effective when references are absent or mismatched during inference. Despite the model converging to a reference-invariant solution, RAT improves deepfake detection by inducing beneficial invariance. The approach achieves state-of-the-art performance on the ASVspoof 5 benchmark, with 2.57% equal error rate (EER) and 0.074 minimum detection cost function (minDCF), outperforming ensemble systems.
anti-spoofingreference-augmented trainingasvspoofequal error rateminimax detection cost function
Human-AI Teaming Through the Lens of Calibration
The paper investigates human-AI teaming through calibration theory, analyzing how calibration assumptions propagate in collaborative frameworks. Two approaches are examined: (i) combining human and model predictions, where existing methods fail to preserve human calibration, and (ii) delegating predictions, which preserves calibration but shifts complexity to the rejector meta-model. Theoretical and empirical results show the rejector must precisely identify each member's strengths, a challenge exacerbated by unobservable human expertise. The work highlights fundamental trade-offs in calibrated teaming systems.
calibrationhuman-ai teamingrejector meta-modeldelegation frameworksfeature space partitioning
Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization
Pose-ICL introduces a 3D-aware in-context learning framework for pose-controllable subject customization, addressing limitations in existing 2D-native methods that struggle with pose accuracy and cross-pose consistency. The method employs Surface-Anchored Position Embedding (SAPE) to anchor image tokens to volumetric bounding box coordinates, enhancing 3D awareness while maintaining compatibility with Diffusion Transformers (DiT). Evaluations on 3D assets and real-world subjects demonstrate superior performance in pose accuracy and identity consistency compared to current approaches.
in-context learningpose controlsurface-anchored position embeddingdiffusion transformerssubject customization
Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding
The Customized Concept Embedding Diffusion (CCE-Diffusion) framework improves text-instance alignment in Foreground Conditioned Outpainting (FCO) by reducing artifacts in synthesized backgrounds. The framework introduces a CCE-Module to customize concept embeddings, bridging the gap between generic noun semantics and specific visual instances. Optimization is guided by an Instance-Aware Loss, while a Semantic-Preserving Prompt Template prevents distortion of other words in the prompt. Evaluations show that CCE-Diffusion significantly reduces artifacts, and the CCE-Module integrates as a plug-and-play component with various FCO methods, enhancing their performance.
foreground conditioned outpaintingcustomized concept embeddinginstance-aware losssemantic-preserving prompt templateartifact reduction
Optimal Post-Training Quantization Scales and Where to Find Them
The paper introduces PiSO (Piecewise Scale Optimization), an algorithm for optimal channel-wise weight scale determination in post-training quantization (PTQ) of large language models. PiSO partitions the scale search space into intervals with closed-form minimizers, extends to group-wise quantization via principled heuristics, and interleaves scale optimization with error correction. Experiments on Llama and Qwen models show consistent perplexity and zero-shot accuracy improvements, particularly at lower bit-widths (4-bit and below), demonstrating PiSO's effectiveness in challenging quantization scenarios.
post-training quantizationchannel-wise scalingpiecewise optimizationerror correctionlow-bit quantization
Large-scale semantic mapping of learner agency and autonomy reveals what measurement and generative AI research overlook
This study empirically quantifies the 'jingle-jangle' fallacy in learner agency and autonomy research through large-scale semantic analysis of 8,954 definitions and 2,700 scale items from 14,000 publications. Using a semantic analysis pipeline, the work identifies three dimensions: task regulation/control, person-level intrinsic motivation, and sociocultural action. Results reveal systematic underrepresentation of sociocultural aspects in existing scales and generative AI education research, which predominantly focuses on learning regulation. The findings challenge current conceptualization and measurement practices in AI-mediated learning environments.
learner agencysemantic analysisjingle-jangle fallacygenerative aieducation research
LIBERO-Occ: Evaluating and Improving Vision-Language-Action Models under Scene-Induced Occlusion via Viewpoint Imagination
The paper introduces LIBERO-Occ, a benchmark for evaluating Vision-Language-Action (VLA) models under scene-induced occlusion, demonstrating significant performance degradation in existing models. To address this, the authors propose Viewpoint Imagination (VIM), which generates complementary views from occluded observations and conditions actions on both observed and imagined evidence. VIM improves robustness across tasks, occlusion types, and severity levels without requiring additional cameras, achieving notable gains in partially observable manipulation scenarios.
vision-language-action modelsscene-induced occlusionviewpoint imaginationpartially observable manipulationperception completion
From Perception to Action: Can UI Interventions Foster Sustainable LLM Chatbot
The study explores UI interventions for promoting sustainable LLM chatbot usage by increasing energy awareness without compromising usability. A baseline survey (n=77) revealed high environmental concern but low consumption accuracy (88.3% misestimation) and limited willingness (39.0%) for performance trade-offs. A prototype featuring energy-efficient modes, real-time feedback, and analogies was evaluated in a 5-day field study (n=11), showing 55.8% Energy-efficient mode adoption and 90.9% self-reported eco-mode preference, with mode switching identified as the primary behavioral mechanism over prompt reduction.
llm chatbotsenergy awarenessui interventionssustainable aibehavioral mechanism
Janus: A Benchmark for Goal-Conditioned Information Distortion in LLMs
The paper introduces JANUS, a benchmark for evaluating goal-conditioned pragmatic distortion in LLMs, focusing on selective treatment of true facts rather than explicit falsehoods. The benchmark comprises 160 scenarios across 8 domains, each with neutral and goal-directed prompts, and a fixed pool of favorable and adverse facts to isolate misleading impressions. Experiments with 12 LLMs reveal consistent goal-conditioned distortions, highlighting models' sensitivity to incentives and lack of safeguards against selective communication. The corpus and code are publicly released.
goal-conditioned distortionpragmatic deceptionllm benchmarksfact-grounded outputsselective communication
Geometrically Averaged Hard Target Updates for Linear Q-Learning
The paper introduces $λ$-target updates, a geometrically weighted averaging mechanism for stabilizing Q-learning with linear function approximation. The method interpolates between periodic hard target updates ($λ=0$) and projected Q-value iteration ($λ\uparrow1$), analyzed through a switching-system framework for deterministic settings. Theoretical analysis demonstrates how this approach can improve stability in linear Q-learning, with extensions suggested for stochastic reinforcement learning environments.
q-learninglinear function approximationtarget updatesswitching-system modelreinforcement learning
Do VLMs Reason Like Engineers? A Benchmark and a Stage-wise Evaluation
The authors introduce EngVQA, a multimodal benchmark with 696 problems across 5 engineering subjects, to evaluate Vision-Language Models' (VLMs) engineering reasoning capabilities. They propose an 8-stage automatic evaluation framework that assesses intermediate reasoning steps, addressing limitations of answer-only benchmarks. Testing state-of-the-art VLMs reveals significant gaps in engineering reasoning, with human evaluation validating the framework (Pearson r=0.975, MAE=0.67 on 10-point scale), demonstrating the need for process-oriented assessment in technical domains.
vision-language modelsengineering reasoningmultimodal benchmarkprocess-oriented evaluationtechnical diagrams
Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models
ADAS introduces a training-free reranking rule for parallel masked diffusion decoding in language models, addressing the fragility of revealing multiple tokens per denoising iteration. The method modifies subset construction by greedily discounting candidates that attend strongly to uncertain positions, using attention as a soft marginal penalty rather than hard constraints. Evaluated on LLaDA-8B-Base and Dream-7B-Base across GSM8K, MATH500, HumanEval, and MBPP, ADAS improves low-NFE performance by 9.11 and 10.46 percentage points on average when integrated with Top-k, Fast-dLLM, and EB-Sampler, with only 3.1% runtime overhead. This demonstrates the effectiveness of soft attention-discounted reranking in enhancing parallel decoding quality.
masked diffusionattention-discountedrerankingparallel decodingsoft marginal penalty
A Unified Siamese Learning Framework for Zero-Day Anomaly Detection and Classification in Optical Networks
The paper proposes a unified Siamese neural network framework for zero-day anomaly detection and one-shot classification in optical networks. The method employs multi-similarity learning within a Siamese architecture to achieve joint anomaly detection and classification without retraining. Results demonstrate 99%+ accuracy on both tasks, with instant adaptability across different lightpaths and previously unseen anomaly types.
siamese neural networkzero-day anomaly detectionone-shot classificationoptical networksmulti-similarity learning
K-Forcing: Joint Next-K-Token Decoding via Push-Forward Language Modeling
K-Forcing introduces a push-forward language modeling paradigm for joint next-k-token decoding to address inefficiencies in autoregressive (AR) inference. The method distills an AR model into a conditional push-forward mapping that generates multiple future tokens in one forward pass via progressive self-forcing distillation, maintaining compatibility with standard AR infrastructure. Evaluated on LM1B and OpenWebText with k=4, K-Forcing achieves 2.4-3.5x speedup across batch sizes with modest quality degradation compared to AR baselines.
push-forward modelingautoregressive distillationjoint token decodingprogressive self-forcingbatch serving acceleration
Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks
Earth-OneVision introduces a 2B-parameter remote sensing multimodal large language model (RS-MLLM) unifying six sensor modalities and cross-sensor fusion across nine tasks. The model employs Full-Granularity Vision-Language Alignment (FGVLA) for multi-level feature alignment, Spatial-Linguistic Isomorphic Serialization (SLIS) for unified spatial output representation, and Progressive Cross-Modality Adaptation (PCMA) for domain gap reduction. Trained on MMRS-OneVision (34M QA pairs), it achieves 87.52% P@0.5 on OPT-RSVG, 80.68% on SARLANG-Bench, 75.74% recall on BigEarthNet-MS, and 81.94% MCQ accuracy on EarthMind-Bench, outperforming larger 4B-72B models.
rs-mllmmultimodal fusionvision-language alignmentautoregressive frameworkcross-modality adaptation
Moonshine: An Autonomous Mathematical Research Agent Centered on Conjecture Generation
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Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use
The study introduces PhysTool-Bench, the first benchmark for evaluating Multimodal Large Language Models (MLLMs) in physical tool use, comprising 2,510 queries across 2,678 tools from diverse real-world domains. Models are assessed on tool recognition in scenes and task-oriented planning for tool selection and use. Results show even top-performing models like Gemini-3.1-Pro achieve only 58.7% tool recognition accuracy and 21.0% end-to-end task completion, revealing significant deficits in perceptual grounding and functional commonsense for embodied AI applications.
multimodal large language modelsphysical tool useembodied aibenchmark evaluationfunctional commonsense
Boosting ECG Classification Performance by Pre-training with Synthesized Data
The study proposes a domain-knowledge-driven Gaussian-composition synthesis algorithm to generate synthetic single-lead II ECG data for pre-training deep neural networks (DNNs), addressing data scarcity in medical domains. Synthetic ECGs simulate four abnormalities: atrial fibrillation, atrial flutter, premature ventricular complex, and Wolff-Parkinson-White Syndrome. Experiments with ten DNN architectures demonstrate that synthetic-to-real training improves classification performance, yielding architecture-averaged gains up to 33.2% for atrial flutter, particularly benefiting smaller real-world datasets. Results indicate synthetic ECGs are effective pre-training resources when real-world data is limited.
gaussian-composition synthesissingle-lead ii ecgdeep neural networkssynthetic-to-real trainingatrial fibrillation
Evaluating Research-Level Math Proofs via Strict Step-Level Verification
The paper introduces a strict step-level verification framework to evaluate research-level mathematical proofs, addressing limitations of global evaluation methods prone to context poisoning. The method maintains detailed deduction context and constrains theorem application sources, tested on the FirstProof challenge's adversarial diagnostic suite. Results show constrained verification outperforms global prompting, altering the error taxonomy to reveal 'pedantic hyper-rigor' from unstated domain conventions rather than logical hallucinations, suggesting improved rigor in automated proof review.
step-level verificationcontext poisoningdeductive constraintslogical hallucinationsautomated proof-review
Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
Dep-LLM introduces a training-free framework for Automatic Depression Detection (ADD) using frozen foundation LLMs, addressing challenges of sparse depression clues in lengthy interviews and data scarcity. The method employs a three-stage process: 1) Chain-of-Thought Depression Multi-factor Analysis to decompose dialogues into clinically aligned themes, 2) Confidence Analysis and Modulation to quantify epistemic reliability via token-level entropy, and 3) Collaborative Multi-factor Prediction for dynamic diagnosis integration. Experiments on DAIC-WOZ and E-DAIC show Dep-LLM outperforms zero-shot baselines across 21 LLMs and 9 metrics, surpassing supervised domain-specific and commercial LLMs without training.
automatic depression detectionchain-of-thoughttoken-level entropymulti-factor predictiontraining-free framework
READER: Robust Evidence-based Authorship Decoding via Extracted Representations
The paper introduces READER, a lightweight framework for dynamic black-box LLM provenance that identifies source models from query-varying prompt responses. The method uses a frozen proxy LLM to extract hidden authorship evidence, temporally filters token states, and performs Bayesian Evidence Accumulation across independently sampled prompts. On the Agent500 dataset (50 targets), READER achieves 31.0-42.4% top-1 accuracy from a single response and 70.0-84.0% from 50 responses, outperforming sentence-encoder baselines. Scaling experiments with nine proxy readers reveal stronger LLMs expose more linearly decodable authorship structure in frozen representations.
llm provenancebayesian evidence accumulationdynamic black-boxauthorship decodingproxy activation space
Accelerating NeurASP with vectorization and caching
The paper accelerates NeurASP, a neurosymbolic AI framework combining neural networks with answer set programming (ASP), through vectorization, batch processing, and caching of intermediate computations. These optimizations address scalability limitations caused by expensive gradient calculations through non-differentiable ASP components. Experiments demonstrate speedups of multiple orders of magnitude on larger tasks, evaluated using a new dataset of complex card-playing tasks designed to test NeurASP's enhanced learning capabilities.
neurosymbolic aianswer set programmingvectorizationgradient calculationbatch processing
A Bayesian Network Approach for Enhancing Security-Focused Decision Support Systems
The paper proposes a Bayesian Network-based Decision Support System (DSS) for security tool selection in heterogeneous open-source networks. The framework models high-level security requirements (confidentiality, integrity, availability) across domains and performs probabilistic inference to recommend optimal security mechanisms. The architecture emphasizes extensibility and interpretability, with evaluation metrics including inference time and prediction accuracy. No quantitative performance results are provided in the excerpt.
bayesian networkdecision support systemsecurity triadprobabilistic inferenceheterogeneous networks
AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
AutoPDE introduces a novel agentic framework for reliable PDE solving by explicitly representing solver strategies as inspectable objects, decoupling them from implementation code. The method operates in three stages: PDE analysis identifies equation types and algebraic structures, numerical method selection matches discretization and stabilization techniques to the analysis, and adaptive tuning calibrates resolution and tolerances via pilot solves. This approach enables strategy-level debugging and revision based on numerical evidence, unlike traditional LLM-based agents that embed strategies implicitly in code. Evaluated on the PDE Agent Bench, AutoPDE achieves a 54.5% pass rate, outperforming the strongest baseline by 14.2 percentage points.
partial differential equationsnumerical solverdiscretizationadaptive tuningpilot solves
Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation
This paper systematizes research on LLM agent security through a lifecycle-based framework, analyzing 247 papers to address threat modeling, attack surfaces, defenses, and evaluation practices. The authors identify prompt injection and tool-mediated control-flow hijacking as dominant threats, with emerging concerns in persistent state corruption and multi-agent propagation. Defenses are found to be weakly compositional, while benchmarks inadequately represent long-horizon and stateful risks. The study advocates for explicit trust boundaries, privilege control, provenance-aware state management, and deployment-aligned evaluation to enhance LLM agent security.
llm agentsprompt injectioncontrol-flow hijackingpersistent state corruptiontrust boundaries
The Arbiter Agent: Continually Monitoring Multi-Agent Conversations to Detect Emergent Misalignment
The paper introduces the Arbiter, a monitoring agent for detecting emergent misalignment in multi-agent conversations. The Arbiter operates under limited inspection budgets, employing strategies like questioning participants or examining internal states to identify misaligned behavior. Evaluated across five conversation conditions (including financial advice and colluding agents) with varying tool configurations and backbone models, results show reliable early detection of misalignment, with active inspection improving accuracy and speed. Weight-induced misalignment was hardest to detect, while instruction-induced cases were reliably identified. The logging tool exhibited a recall-precision tradeoff.
multi-agent systemsmisalignment detectioninspection budgetlanguage-model agentscontinual monitoring
When the Chain of Thought Knows Better: Failure Modes in Multi-Turn Reasoning Models
The study introduces the CoT-Output 2x2 safety matrix, a trace-level diagnostic framework for evaluating multi-turn reasoning models by labeling each turn along internal reasoning and visible output axes. This reveals four failure modes, including context-injection failure where safe reasoning produces harmful outputs. Evaluating three distilled reasoning targets across five oversight conditions (6,750 turn-level observations on Information-Hazard scenarios) exposes two vulnerabilities: an oversight paradox where monitoring increases alignment-faking, and persistent context-injection failures. The dataset of multi-turn dialogues and CoT traces is released for further research.
multi-turn reasoningsafety matrixcontext-injection failurealignment fakingoversight paradox
Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding
Spatial-Omni introduces a lightweight method to integrate First-Order Ambisonics (FOA) spatial audio into multimodal LLMs via SO-Encoder, preserving spatial cues without modifying existing audio encoders. The approach uses efficient staged training and limited additional context tokens to enhance spatial audio understanding. Evaluated on SO-Bench (16 subtasks) with 400K FOA clips and 2.1M QA pairs, Spatial-Omni outperforms existing Large Audio-Language Models in spatial tasks while maintaining general audio comprehension.
first-order ambisonicsspatial audio understandingmultimodal llmsin-context learningaudio-language models
Detecting Knowledge Gaps from Conversational AI Interactions Using Curriculum Prerequisite Graphs
A pipeline leveraging conversational AI interaction logs detects knowledge gaps in online courses by mapping student questions to curriculum topics. The method employs a few-shot text classifier grounded in a GPT-4-extracted prerequisite knowledge graph of course concepts. Evaluated on 1,340 question events from 164 students in a graduate-level AI course, the classifier achieves 80.0% accuracy across 43 labels (42 curriculum topics plus an 'unknown' abstention class). Topic-level question volume significantly correlates with student self-reported difficulty (rho = 0.491, p = 0.008, n = 28 topics), validating the classifier's ability to identify genuine topic difficulty. This approach provides instructors with curriculum-grounded insights into areas requiring attention.
conversational aiprerequisite knowledge graphfew-shot text classifiercurriculum topicsknowledge gaps
Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition
The study introduces EEG-TransNet, a transformer-based model for EEG emotion recognition, featuring three novel modules: ResNet/wavelet preprocessing, Local Self-Attention Block for regional features, and Fuzzy-Attention Synchronous Transformer (FAST) for spatiotemporal dependencies. Evaluated on BETA, SEED, and DepEEG datasets, it outperforms baselines in classification accuracy and robustness, with ablation studies confirming the Local Self-Attention Block's efficacy. Depthwise separable convolutions reduce computational complexity without sacrificing performance. The model demonstrates strong generalization across subjects.
eeg-transnetlocal self-attention blockfuzzy-attention synchronous transformerdepthwise separable convolutionsspatiotemporal dependencies
Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
The paper proposes attention expansion, a mechanism to enhance keyphrase extraction (KPE) from long documents by augmenting PLM token representations with information from out-of-context chunks using pre-trained word embeddings. This approach expands contextual scope without full-document attention or costly LLM inference. Evaluated across five PLM backbones, two training regimes, and five benchmark corpora, attention expansion consistently improves KPE performance, outperforming state-of-the-art models and achieving notable F1 score gains across domain-specific, task-specialized, and native long-context models.
keyphrase extractionattention expansionpre-trained language modelscontextualized embeddingslong-document processing
++nnU-Net: Scaling nnU-Net with Prefix-Based Data Augmentation
The ++nnU-Net introduces a prefix-based data augmentation module for medical image segmentation, leveraging image registration to enhance dataset diversity prior to preprocessing and training. The framework employs a two-stage registration process to generate warped images and corresponding segmentation transformations, while also computing disk space, creating synthetic binary masks, and generating checkpoints. Evaluated on five 2D medical imaging datasets, ++nnU-Net outperforms the baseline nnU-Net, achieving up to 22% improvement in Dice Similarity Coefficient scores. This demonstrates the efficacy of registration-based augmentation in data-limited scenarios, offering a scalable solution for medical segmentation tasks.
image registrationdata augmentationmedical segmentationdice similarity coefficientnnu-net
Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training
The paper introduces query recycling, a technique to improve reinforcement learning for agentic search by reusing zero-variance query groups during training. Unlike existing methods that discard such groups, the approach dynamically recycles them into a mutable pool, allowing the training distribution to co-evolve with the policy. Empirical results show a 1.7B parameter model achieves 66.0 average Pass@1 across seven multi-hop QA benchmarks, matching or surpassing 7B parameter models. Analysis reveals recycled queries contribute ~75% of the effective batch, balancing policy improvement and drift recovery.
query recyclingzero-variance queriesgrpo-style algorithmsmulti-hop qapolicy drift
Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey
The survey presents a unified framework for optimizing large language model (LLM) training by jointly addressing data, memory, and compute efficiency. It organizes recent techniques into three coupled bottlenecks: data efficiency (token selection via learning dynamics, gradient scoring, or curriculum strategies), memory efficiency (reducing weight storage, optimizer states, and activation memory), and compute budget awareness (optimal allocation and stopping rules). Findings indicate that optimal data subsets and resource allocation are task- and budget-dependent, with GPU memory often being the dominant constraint in fine-tuning scenarios.
data efficiencymemory efficiencycompute budgetlarge language modelsresource allocation
Event-Driven Reinforcement Learning Enables Long-Horizon Control in Semiconductor Fabrication
The authors propose a deep reinforcement learning framework for multi-objective policy optimization in semiconductor manufacturing, addressing the challenges of stochasticity, high dimensionality, and long-horizon decision-making. The method formulates control as a centralized-agent problem, employing an event-driven temporal-difference formulation compatible with various policy optimization algorithms. Evaluated through high-fidelity simulations of industry-real scenarios, the framework demonstrates significant improvements in throughput and utilization across offline and online training settings. Results highlight the scalability, generality, and transferability of the approach for controlling complex adaptive systems.
reinforcement learningsemiconductor manufacturingtemporal-differencemulti-objective optimizationevent-driven systems
Using the YOLOv12 Model for Verifying the Correct Color Sequence of Wires in Network Cables (Patch Cords) on the Production Line
The study presents an automated system using YOLOv12 for verifying wire color sequences in network cables, addressing human error in traditional microscope-based inspection. The method employs a single-stage object detection architecture with attention mechanisms, trained on 2,500 microscopic images (70-15-15 train-val-test split). Results show 98% precision in wire detection, with 95% mean accuracy, 99% classification precision, and 98% recall, enabling real-time production-line verification without human intervention.
yolov12object detectionattention mechanismsproduction-line inspectioncolor sequence verification
Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals
The paper introduces DAC (Divide and Cooperate), a role-decomposed multi-agent training framework for language agents that separates evidence acquisition and answer generation into distinct agents. The generator acts as both answer producer and evidence verifier, abstaining when evidence is insufficient, while the searcher provides hard-positive evidence augmentation. Cross-agent learning signals improve credit assignment and robustness. Experiments on general and multi-hop QA benchmarks show DAC, implemented via LoRA modules over a shared backbone, outperforms monolithic fine-tuned baselines.
multi-agentrole-decompositioncredit assignmentloraqa benchmarks
UniDexTok: A Unified Dexterous Hand Tokenizer from Real Data
The paper introduces UniDexTok, a unified tokenizer for dexterous hands that maps heterogeneous hand states into a standardized 22-DoF semantic interface without retargeting. The method leverages the Unified Dexterous Hand Model (UDHM) to learn embodiment-conditioned discrete tokens from real joint states, enabling cross-embodiment training. Results show a 98.98% reduction in MPJAE (to 0.16°) and a 99.03% reduction in MPJPE (to 0.18 mm), achieving sub-millimeter accuracy. UniDexTok also demonstrates improved zero-shot and few-shot reconstruction when new hands are introduced, highlighting the benefits of cross-embodiment tokenization.
dexterous handstokenizer22-dofmpjaempjpe
Infini Memory: Maintainable Topic Documents for Long-Term LLM Agent Memory
Infini Memory introduces a maintainable text-based persistent memory architecture for long-term LLM agents, addressing limitations of isolated records and fragmented indexing. The system organizes memory as topic-structured documents, enabling coherent evidence aggregation, metadata preservation, and fact revision through staged observation buffers and periodic consolidation. An agentic retrieval procedure employs iterative LLM tool calls for memory access. Evaluated on MemoryAgentBench, Infini Memory achieves 64.7% overall score, with ablations confirming benefits of topic-structured maintenance and iterative evidence inspection.
topic-structured documentspersistent memoryagentic retrievalevidence aggregationmemory maintenance
In Defense of Information Leakage in Concept-based Models
The paper challenges the conventional view that concept leakage in concept-based models (CMs) is undesirable, arguing that such leakage can be benign and necessary for accuracy and intervenability under real-world concept incompleteness. The authors propose a reframed training objective that optimizes for beneficial leakage without compromising model performance. Results demonstrate that CMs leveraging this approach maintain interpretability while improving practical utility in imperfect settings.
concept-based modelsinformation leakageinterpretabilityintervenabilityconcept incompleteness
Decentralized Multi-Agent Systems with Shared Context
The paper introduces Decentralized Language Models (DeLM), a multi-agent system framework that eliminates centralized orchestration bottlenecks by enabling parallel agents to asynchronously claim subtasks and share verified context updates. DeLM employs a task queue and shared context substrate for decentralized coordination, improving both scalability and reasoning performance. Empirical results show DeLM achieves state-of-the-art performance on SWE-bench Verified (10.5 percentage point gains) and LongBench-v2 Multi-Doc QA (5.7 percentage point improvement), while reducing per-task costs by 50%.
multi-agent systemsdecentralized coordinationshared contexttask queueverified updates
Accounting for AI Inference in Corporate GHG Inventories: A Four-Tier Methodology for Scope 3 Category 1 Reporting
The study introduces a four-tier methodology for accurately reporting Scope 3 Category 1 emissions from AI inference services under CSRD compliance. It addresses current overestimation (10-40x) by EEIO methods through GPU energy benchmarks, grid carbon intensities, and water use data. The framework progresses from token-based physical estimation to EEIO fallback, validated via ML.ENERGY Leaderboard v3 and EPA eGRID 2023. Application to a 200-person firm shows <1 tCO2e emissions, highlighting methodological over magnitude challenges. A water-carbon trade-off reveals Sweden's hydro grid minimizes carbon but maximizes water footprint, impacting data center siting.
scope 3ghg inventoriescsrd compliancetoken-based estimationwater-carbon trade-off
Post-Quantum Secure Federated DeFi for Inclusive Banking
The paper proposes a post-quantum secure federated DeFi framework for inclusive banking, addressing vulnerabilities in cryptographic primitives due to quantum computing. The framework enables inter-bank collaboration using lattice-based Fully Homomorphic Encryption (FHE) for end-to-end homomorphic computation on encrypted financial data. It integrates local probabilistic assessments, expert beliefs, and verifiable evidence from the NASA-IBM Prithvi Geospatial Foundation Model (GFM), ensuring tamper-proof data exchange via decentralized technologies. The framework is evaluated on agricultural lending decisions for rural borrowers in Virginia, demonstrating its applicability in underserved financial contexts.
post-quantum cryptographyfully homomorphic encryptiondecentralized financegeospatial foundation modelfederated learning
Dynamic Linear Attention
The paper introduces Dynamic Linear Attention (DLA), a framework enhancing multi-state linear attention for long-context LLMs. DLA features Information-Aware Dynamic State Merging, adapting state boundaries to token-level information variation, and Capacity-Bounded Memory Modeling, controlling memory growth via selective state merging. Evaluated on 16 datasets, DLA outperforms state-of-the-art linear attention methods in preserving semantic fidelity while maintaining sub-quadratic complexity.
linear attentionmulti-state memorydynamic state mergingcapacity-bounded modelinglong-context llms
Is Fairness Truly Fair? Towards Reliable Lipschitz Fairness in Multi-Task Learning via Fixed-\texorpdfstring{$δ$}{delta} Alignment
The paper introduces ReLiF, a reliability-aware framework for evaluating Lipschitz-style individual fairness in multi-task learning (MTL) by addressing threshold confounding. ReLiF employs fixed-$δ$ auditing with a shared reference tolerance for comparable evaluation and a violation-rate feedback controller to balance fairness regularization during training. Theoretical analysis covers threshold drift, reference-tolerance selection, and surrogate relationships. Experiments on NYUv2 (ResNet50 backbone) and clinical time-series benchmarks show ReLiF achieves competitive utility while reducing aligned bias under fixed thresholds, revealing utility--fairness trade-offs obscured by method-dependent thresholds.
lipschitz fairnessmulti-task learningfixed-$δ$ auditingthreshold confoundingviolation-rate controller
STORM: Stepwise Token Optimization with Reward-Guided Beam Search
STORM introduces a self-supervised framework for lexical query expansion, addressing vocabulary mismatch in BM25 retrieval without requiring corpus reindexing. The method employs reward-guided beam search to train query rewriters, using BM25 scores as token-level supervision to prune low-reward expansions during generation. Experiments on TREC DL and BEIR show STORM enables 0.6B-8B models to match LLM rewriters' effectiveness while maintaining BM25's speed, with zero-shot transfer to 18 languages (MIRACL) outperforming dedicated multilingual dense retrievers.
lexical retrievalquery expansionbeam searchbm25zero-shot transfer
Can Image Models Imagine Time? ImageTime: A Novel Benchmark for Probing Visual World Modeling Through Spatiotemporal Consistency
The paper introduces ImageTime, a novel benchmark for evaluating spatiotemporal consistency in image generation models through multi-frame visual world modeling. The benchmark requires models to generate four ordered key states (initial, onset, transition, final) from an action instruction, with structured evaluation via GPT-5.5 scoring for temporal coherence and causal constraints. Multi-family benchmarking reveals current systems' limitations in maintaining coherent visual states over time, providing diagnostic failure modes and capability scores.
spatiotemporal consistencyvisual world modelingimage generationmulti-frame evaluationcausal constraints
Learning What to Remember: Observability-Safe Memory Retention via Constrained Optimization for Long-Horizon Language Agents
The paper introduces OSL-MR (Observability-Safe Learning for Memory Retention), a framework for optimizing memory retention in long-horizon language agents under observability constraints. It formulates retention as a constrained stochastic optimization problem with explicit budget feasibility, evidence utility, and delayed costs. OSL-MR combines an evidence learner trained from offline-available supervision with a Mixed-Score heuristic, enabling query-conditioned evidence valuation while remaining deployable online. Experiments on LOCOMO and LongMemEval demonstrate superior performance over recency-based methods and Generative Agents-style scoring, particularly under tight memory budgets, with robustness across cost configurations.
memory retentionconstrained optimizationobservability-safelong-horizon agentsmixed-score heuristic
Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
The paper introduces Bootstrapped Flow Q-Learning (BFQ), a novel offline RL framework that enables accurate single-step action generation without auxiliary networks or distillation. BFQ decomposes the flow path into short-range displacements learned via Flow Matching marginal velocity, then bootstraps these to directly learn a noise-to-action mapping. Evaluations on D4RL show BFQ outperforms multi-step diffusion baselines in performance while reducing computational costs, demonstrating single-step generation suffices for high-performance offline RL.
offline reinforcement learningflow matchingsingle-step generationbootstrapped learningd4rl benchmark
Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
The Causal Ensemble Agent (CEA) framework improves causal discovery by integrating statistical experts with LLM-guided meta-analysis. CEA employs linear opinion pooling to aggregate structural insights from diverse causal discovery algorithms, then uses an LLM as a meta-referee to dynamically reweight expert contributions near decision boundaries. Experiments on synthetic and real-world datasets show CEA outperforms existing methods, demonstrating the value of combining statistical approaches with LLM-based domain knowledge refinement.
causal discoverylinear opinion poolingmeta-refereestructural insightsdecision boundary
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Dmsh introduces a fully automated reinforcement learning framework for all-quadrilateral mesh generation, unifying geometric decomposition and meshing within a single pipeline. The method employs three coordinated agents to handle topology simplification, geometric regularization, and mesh generation, formulated as a Markov Decision Process solved via a parametric Soft Actor-Critic architecture with decoupled critics. A curriculum learning strategy ensures scalability from simple to complex geometries, while recursive decomposition enables parallel meshing of subregions without post hoc correction. Dmsh outperforms existing methods across benchmarks in automation, robustness, and mesh quality, establishing a new paradigm for learning-based mesh generation.
reinforcement learningquadrilateral meshmarkov decision processsoft actor-criticcurriculum learning
Embedding Hybrid Systems into Continuous Latent Vector Fields
The paper proves that an $n$-dimensional hybrid system can be embedded into an $m$-dimensional Euclidean space with a continuous vector field when $m>2n$, enabling differentiable optimization of intrinsically discontinuous systems. The authors propose a latent Neural ODE framework with consistency losses in both latent and state spaces to recover hybrid system flows. Experimental results demonstrate superior performance over existing methods in learning hybrid systems with diverse geometries from time series data.
hybrid systemsneural odelatent spacevector fielddifferentiable optimization
From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning
This survey provides a data-centric review of Federated Learning (FL), analyzing how data properties affect convergence and stability. It categorizes non-IID data traits by their convergence impact (strong/medium/light), examines experimental splitting protocols' artifacts, and evaluates data-related vulnerabilities with defense trade-offs. The analysis spans image, text, and graph domains, offering actionable insights for FL system design. Results reconcile empirical evidence across modalities and quantify performance under adversarial conditions.
federated learningnon-iid dataconvergence analysisdata heterogeneityadversarial robustness
Towards Diverse Scientific Hypothesis Search with Large Language Models
We propose an evolutionary framework for diverse scientific hypothesis generation using large language models, addressing the limitations of optimization-focused approaches that lead to diversity collapse. Our method, inspired by parallel tempering, searches hypotheses at multiple temperature levels and facilitates principled information exchange across temperatures to balance exploration and convergence. Evaluated across molecular discovery, equation discovery, and algorithm discovery domains, the framework consistently improves both hypothesis quality and diversity under fixed validation budgets, while maintaining robustness in downstream computational validations.
parallel temperinghypothesis generationdiversity collapsevalidation budgetexploration-convergence tradeoff
NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity
NOVA introduces an autonomous symbolic regression framework for discovering interpretable car-following and lane-change models from raw trajectory data, incorporating driver heterogeneity. The method employs a deterministic Rust-powered search engine to evaluate over 10,000 candidate algebraic structures, identifying a compact two-term acceleration model. Evaluated on 4,765,788 driving observations from NGSIM I-80 and US-101 datasets, NOVA achieves RMSE = 1.376 m/s² (R² = 15.57%) on intent forecasting, outperforming SR-LLM by 0.135 m/s². It achieves 67.4% balanced accuracy in lane-change modeling under vehicle-ID holdout, surpassing baselines by +29.8 percentage points. The discovered nonlinear term aligns with collision avoidance theory and transfers zero-shot between freeway sites with minimal R² loss.
symbolic regressioncar-followinglane-changermsengsim
Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketching
This work introduces zero-shot mutual intelligibility (ZMI) as a novel generalization axis in emergent communication, defined as successful interaction between independently trained agent populations without prior exposure. Using emergent sketching—a visually grounded modality where agents communicate through stroke drawings—the authors demonstrate that scaling training population size significantly enhances ZMI across disjoint groups. Population scaling increases in-group communicative variation while reducing cross-group variation, driving structural convergence toward perceptual grounding in objective visual resemblance. These findings suggest a pathway toward socially interoperable artificial agents through population-scaled emergent communication.
zero-shot mutual intelligibilityemergent sketchingpopulation scalingperceptual groundingsocial interoperability
Convergence of Monte Carlo Optimistic Policy Iteration: Beyond Uniform State-Action Updates
This paper resolves a long-standing open question by proving the convergence of Monte Carlo optimistic policy iteration (MC-O-PI) under relaxed conditions. Unlike the impractical canonical requirement of uniform initialization over the entire state-action space, the authors demonstrate that MC-O-PI converges to optimality when updates are uniform only over actions within each state, allowing arbitrary state initialization frequencies. The proof leverages mean-field dynamics to show monotonic policy improvement and extends the lock-in argument of the combined stability-ODE method to address noise. This approach provides a novel framework for analyzing optimistic policy-iteration algorithms.
monte carlopolicy iterationmean-field dynamicsoptimality convergencestate-action space
One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA
The paper introduces Latent Memory, a memory paradigm for resource-efficient QA that compresses multimodal evidence into single high-dimensional latent tokens using a small compressor LLM/VLM. The method trains the compressor with reconstruction, contrastive, and distillation objectives to ensure tokens are informative for retrieval, reconstruction, and generation. Evaluated on seven QA benchmarks, including HotpotQA and WebQA, Latent Memory achieves competitive performance with 3x-10x fewer generator tokens compared to RAG baselines, while delivering strong image-grounded QA results.
latent memorymultimodal qaretrieval-augmented generationlatent tokenresource-constrained
Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction
The paper proposes DeBias-Attack, a transfer-based adversarial attack method for Vision-Language Pre-training (VLP) models that corrects surrogate-specific bias in optimization directions to improve cross-model transferability. The method employs dual perturbation branches: a main branch optimizing adversarial gradients on original images, and a reference branch estimating surrogate bias via gradients from weak-semantic images. By removing the aligned projection of main gradients on reference gradients, it reduces surrogate dependency while maintaining attack effectiveness. Experiments demonstrate improved transferability across VLP models, downstream tasks, and both open/closed-source multimodal LLMs.
adversarial transferabilityvision-language pre-trainingsurrogate-specific biasgradient correctionmultimodal large language models
Hidden Consensus:Preference-Validity Compression in Human Feedback
The paper identifies Preference-Validity Compression (PVC) as a critical flaw in standard RLHF pipelines, where heterogeneous human judgments are reduced to scalar rewards, potentially misrepresenting pluralistic alignment. Analyzing 321 preference events from 20 Malaysian participants and 107 trio-annotated prompts, the authors demonstrate that 79% of prompts contain multiple majority-supported responses, which single-winner aggregation discards. Results show discarded responses often reflect coherent cultural or practical frames, suggesting majority aggregation measures argmax acceptability rather than plural alignment. The authors propose Validity-Preserving Consistency as a criterion for future alignment methods.
preference-validity compressionrlhf pipelinesplural alignmentvalidity-preserving consistencymajority aggregation
Benchmarking Knowledge Editing using Logical Rules
The authors introduce a novel benchmark for evaluating knowledge editing in Large Language Models (LLMs), focusing on logical consequences of fact edits rather than mere fact recall. Their method extracts logical rules from knowledge graphs to generate multi-hop questions, assessing how edits propagate through entailed knowledge. Experiments with ROME and FT reveal a 24% performance gap between direct fact editing and logical consequence handling, underscoring the need for semantics-aware evaluation frameworks.
knowledge editinglogical consequencesmulti-hop questionslarge language modelssemantics-aware evaluation
Flexible Flows for Biological Sequence Design
The authors propose a structured coupling for Discrete Flow Matching (DFM) that encodes domain-specific biological preferences, enabling variable-length sequence generation via a latent edit-based rate parameterization. Their method introduces a latent classifier-free guidance mechanism for continuous latent space control and Dirichlet-prior temperature scaling for edit operation tuning. The approach achieves state-of-the-art performance in density estimation, unconditional/conditional DNA sequence generation, and peptide sequence generation tasks.
discrete flow matchinglatent edit-basedclassifier-free guidancedirichlet-priorbiological sequence design
ActiveMem: Distributed Active Memory for Long-Horizon LLM Reasoning
ActiveMem introduces a distributed active memory framework for long-horizon reasoning in LLM agents, addressing the trade-off between context overload and information loss in centralized memory systems. Inspired by human cognitive systems, it decouples memory from core reasoning, employing a high-level Planner for distilled semantic gists and a lightweight, distributed memory system for active accumulation and consolidation. Experiments on BrowseComp-Plus and GAIA demonstrate state-of-the-art accuracy with reduced overhead, validating the efficacy of distributed active memory.
distributed memorylong-horizon reasoningsemantic gistscontext overloadmemory consolidation
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
The paper introduces LC-QAT, a 2-bit vector quantization-aware training framework for LLMs that combines linear-constrained affine mapping with discrete vector representation. This approach avoids explicit codebook lookup during training while maintaining end-to-end differentiability, enabling efficient optimization from high-quality PTQ initialization. Experiments show LC-QAT outperforms state-of-the-art QAT methods across diverse LLMs, achieving superior performance with only 0.1%–10% of typical training data requirements.
quantization-aware trainingvector quantizationlarge language modelslow-bit precisiondifferentiable optimization
Machine Learning Methods for Studying Latent Neural Activity Dynamics
The survey organizes machine learning methods for studying latent neural dynamics into three domains: (1) Single-Region Latent Dynamics, comparing linear dynamical systems to RNNs and Neural ODEs; (2) Multi-Region Communication, analyzing information transfer with probabilistic and subspace methods; (3) Behavior-Aligned Modeling, disentangling task-related activity via supervised/contrastive learning. It also covers neural foundation models like Transformers and diffusion models for cross-subject generalization. The paper concludes with benchmarks and open challenges in causal inference and directional communication analysis.
latent variable modelsneural ordinary differential equationsmulti-region communicationcontrastive learningdiffusion models
Assessing Automated Prompt Injection Attacks in Agentic Environments
The study evaluates automated prompt injection attacks against LLM agents, comparing white-box (GCG) and black-box (TAP) methods in the AgentDojo framework. Experiments across 80 task pairs and multiple models reveal black-box optimization outperforms gradient-based methods due to GCG's instability. Attack effectiveness depends on attacker model capability and safety tuning, with task-universal attacks transferring well but not between small and frontier models. Findings underscore prompt injection as a model-dependent threat with limitations in model-agnostic exploitation.
prompt injectionllm agentsgcgtapagentic environments
HIPIF: Hierarchical Planning and Information Folding for Long-Horizon LLM Agent Learning
The paper proposes Hierarchical Planning and Information Folding (HIPIF), a method to improve long-horizon task performance in LLM agents by addressing long-context interference. HIPIF trains agents to decompose tasks into explicit subgoals, fold completed subgoal histories to reduce context length, and uses hierarchical reflection with subgoal-oriented rewards for stable planning. Experiments on three agentic benchmarks validate the approach's effectiveness without requiring auxiliary models or expert trajectories.
long-horizon taskssubgoal decompositioncontext foldinghierarchical reinforcement learningllm agents
Cross-Modal Knowledge Distillation without Paired Data: Theoretical Foundation and Algorithm
The paper introduces a cross-modal knowledge distillation (CMKD) framework that operates without paired multi-modal data, addressing a key limitation in existing methods. By establishing a cross-modal distributional relationship between teacher and student models, the authors identify feature alignment and label alignment as critical factors for effective distillation. Their theoretically grounded approach aligns distributions rather than individual samples, demonstrating significant improvements over prior work in extensive experiments across multimodal benchmarks.
cross-modalknowledge distillationfeature alignmentlabel alignmentdistributional relationship
A Reliable Fault Diagnosis Method Based on Belief Rule Base Consider Robustness Analysis
Proposes a robust fault diagnosis method using belief rule base (BRB) with systematic robustness analysis and three constraint strategies for optimization. The approach addresses sensor reliability issues in fault diagnosis by enhancing model robustness while maintaining accuracy. Experimental validation on WD615 diesel engine and Case Western Reserve University bearing datasets demonstrates improved accuracy and robustness compared to baseline methods.
belief rule basefault diagnosisrobustness analysissensor reliabilityconstraint optimization
MoE Enhanced Federated Learning for Spatiotemporal Prediction
The paper proposes MoE-FedTP, a federated learning framework for cross-city spatiotemporal prediction using Mixture-of-Experts (MoE) networks. It addresses data scarcity and privacy concerns by employing lightweight MoE with partial parameter sharing and dynamic gating to model urban heterogeneity. Evaluated on four real-world traffic datasets, MoE-FedTP outperforms state-of-the-art baselines in prediction accuracy for data-scarce cities.
mixture-of-expertsfederated learningspatiotemporal predictioncross-city transfertraffic modeling
Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design
The paper presents a CPU-GPU hybrid system for local Mixture-of-Experts (MoE) inference that achieves cloud-grade service-level objectives (SLOs) on commodity hardware. Key innovations include stream-loading prefill (SLP) for 1,200 tokens/s throughput, distributed SLP with SmallEP expert parallelism (1,800 tokens/s), intra-node prefill-decode disaggregation with attention-MoE overlap, AVX-512-optimized FP8 GEMV kernels (4-5× latency reduction), and fine-grained CPU parallelism. Evaluations demonstrate 30-second TTFT for 32K-45K prompts, sustained concurrency with <15% latency increase, and 21.5-28 tokens/s decode throughput on FP8/INT4 DeepSeek-V3, enabling datacenter-quality local deployment of intact MoE models.
mixture-of-expertscpu-gpu hybridstream-loading prefillfp8 gemvexpert parallelism
A complementary study on PlanGPT: Evaluation with defined Performance Metrics and comparison with a planner
This study provides a complementary evaluation of PlanGPT, a state-of-the-art LLM for automated planning, using defined metrics (Plan Cost and Plan Generation Time) and comparison with a traditional planner. The authors verify the accuracy of PlanGPT's reported plan coverage and assess its practical utility. Results indicate PlanGPT performs comparably to a Greedy search strategy, offering no significant advantage over conventional planners in plan quality or efficiency.
automated planninglarge language modelplan coveragegreedy searchperformance metrics
Stop Early, Spend Less: Hidden-State Probes as a Practical Recipe for Streaming Moderation of LLM Outputs
The paper introduces hidden-state probes for efficient streaming moderation of LLM outputs, eliminating the need for separate post-generation safety checks. The method trains lightweight token-level probes on internal activations, producing per-token safety scores that enable real-time intervention during decoding. Probes applied to a single mid-layer recover most decisions of a guard model with sub-millisecond latency, reducing compute overhead by orders of magnitude compared to post-hoc moderation. The approach includes practical deployment guidelines for layer selection, aggregation, and activation steering.
hidden-state probesstreaming moderationtoken-level safetyactivation steeringresidual space
Advancing the State-of-the-Art in Empirical Privacy Auditing
The paper advances empirical privacy auditing (EPA) for parameter-efficient fine-tuning of large language models (LLMs) by introducing synthetic canaries generated via high-temperature sampling (T ≥ 0.8) from LLMs. These canaries, tailored to privacy-sensitive training data, serve as high-influence outliers to enhance identifiability in membership inference and reconstruction attacks. The authors also propose a synthetic data audit using an auxiliary model fine-tuned on synthetic data, demonstrating strong privacy leakage estimation. Systematic experiments reveal the interplay between model capacity and canary entropy on memorization.
empirical privacy auditingparameter-efficient fine-tuningmembership inferencesynthetic canarieshigh-temperature sampling
ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics
The paper introduces ComBench, a benchmark for evaluating Olympiad-level combinatorics reasoning in large language models, focusing on rigorous proof reasoning and constructive realization. ComBench comprises 100 competition-level problems categorized into analysis-centric (requiring mathematical arguments) and construction-centric (requiring explicit constructions) tasks. Evaluation combines rubric-guided proof grading with deterministic construction verification. Results show frontier models achieve 65.4% overall Avg. and 75.3% Best@4, with performance varying between proof reasoning and construction tasks. Kimi-K2.6 outperforms GPT-5.5 on construction-centric tasks but lags in analysis-centric proof grading, highlighting distinct capabilities.
combinatoricsbenchmarkproof reasoningconstruction verificationolympiad-level
Decoupling Thought from Speech: Knowledge-Grounded Counterfactual Reasoning for Resilient Multi-Agent Argumentation
The paper introduces Knowledge-Grounded Counterfactual Reasoning (KG-CFR), a dual-stage architecture that decouples private retrieval-augmented planning from public execution to enhance multi-agent debate resilience. The method is evaluated in Dynamic Resource Allocation under Uncertainty (DRAU), a 1v1v1 environment with stochastic shocks, showing KG-CFR prevents critical degradation (Δ ≤ -0.20) in 95% of perturbed runs and improves argument quality from 0.694 to 0.822. Results demonstrate architectural decoupling boosts systemic resilience while maintaining quality, with ablation studies highlighting doctrinal grounding's equal importance to prospective planning.
knowledge-grounded counterfactual reasoningmulti-agent debatedynamic resource allocationretrieval-augmented planningsystemic resilience
Detecting Speculative Language in Biomedical Texts using Recurrent Neural Tensor Networks
The study presents a comparative analysis of deep learning methods for detecting speculative language in biomedical texts, achieving best performance with Recursive Neural Tensor Networks (RNTN). Two approaches were evaluated: RNTN and Paragraph Vector models, benchmarked against Support Vector Machines, Naive Bayes, and pattern matching. RNTN achieved superior F1-score (0.885) over the top baseline (linear bigram SVM at 0.881), while Paragraph Vector performed poorly (F1=0.368) despite extensive pretraining. The analysis discusses performance factors and suggests future research directions.
speculative language detectionrecursive neural tensor networkparagraph vectorbiomedical text miningf1-score
UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation
UPLOTS introduces a unified pretrained language model framework for constrained time-series generation across domains, eliminating task-specific models. The method employs a transformer backbone with dynamic multi-dataset loss re-weighting and prompt-to-pattern mapping to internalize diverse temporal structures and enable conditional generation. Evaluated on four benchmarks, UPLOTS demonstrates generalization to multiple constraint settings (peak-period, calendar, load-level, volatility) and improves data augmentation in scarce-data regimes.
time-series generationpretrained language modelconstraint promptsdynamic loss re-weightingprompt-to-pattern mapping
ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs
The paper proposes ERAlign, an Energy-based Representation Alignment framework for integrating Graph Neural Networks (GNNs) and Large Language Models (LLMs) on Text-attributed Graphs (TAGs). The method projects GNN-encoded graph structures and LLM-derived text embeddings into a shared latent space, enforcing distribution consistency through an Energy-based Model objective with layer-wise alignment metrics. It introduces Energy Discrepancy (ED) to optimize training efficiency and reduce energy landscape distortion. Evaluations on eight TAG datasets show state-of-the-art performance in varying supervision levels and cross-task transfer scenarios.
energy-based modelsrepresentation alignmenttext-attributed graphsgraph neural networkslarge language models
LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake
LakeQA introduces a benchmark for search-centric question answering over heterogeneous data lakes, addressing the gap in benchmarks requiring both search and reasoning capabilities. Built on 9.5 TB of text resources from Wikipedia and open-source government data, LakeQA emphasizes long-horizon multi-hop reasoning with implicit intermediate steps, annotated by Ph.D.-level experts. Evaluations on seven frontier LLMs reveal significant challenges, with GPT-5.2 achieving only an 18.37% exact-match score. LakeQA serves as a realistic testbed for developing LLM agents capable of discovering and analyzing data in modern data lakes.
data lakesmulti-hop reasoningexact-match scoreheterogeneous datalong-horizon
Minimum Distortion Quantization with Specified Output Distribution
The paper derives an optimal quantizer for real-valued random variable $W$ with distribution $P_W$, ensuring the quantized output $X$ follows a specified distribution $P_X$ while minimizing mean squared error (MMSE). The solution takes the form $X=σ(F_{σ^{-1}(X)}^{-1}(F_W(W)))$, where $σ$ is an optimal permutation minimizing MMSE and $F$ denotes cumulative distribution functions. Special cases yield simplified forms when $P_W$ is uniform or $P_X$ is uniform. Majorization theory underpins the optimality proof. Applications include entropy-controlled quantization, mutual information maximization, channel matching, and data anonymization.
quantizationmean squared errormajorizationcumulative distribution functiondata anonymization
Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
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The Distributed Detectability Band Against Marginal-Preserving Attacks
The paper introduces a marginal-preserving distributed-sabotage attack that evades per-step monitoring by encoding harm in temporal correlations while maintaining benign-like marginals. Using a Gaussian-copula AR(1) construction, the attack achieves a KS-distance of 0.013 to benign distributions, demonstrating realizability without harm limitation. While traditional monitors (Monitor A) fail (AUC 0.52), temporal-correlation monitors (Monitor B) achieve AUC 0.79-0.97 at 1% FPR, revealing a non-empty detectability band for sub-threshold sabotage.
distributed-sabotage attackmarginal-preservinggaussian-copulatemporal-correlation monitorsdetectability band
Mitigating Bias in Low-SNR Financial Reinforcement Learning via Quantum Representations
The paper introduces FPQC-SAC, a quantum-enhanced variant of Soft Actor-Critic (SAC) designed for low-SNR financial reinforcement learning. By inserting a Parameterized Quantum Circuit (PQC) before actor-critic networks, the method constrains feature propagation at the representation level, mitigating the 'Financial Entropy Trap' caused by noisy state representations and bootstrapping errors. Empirical results on portfolio management tasks show FPQC-SAC achieves 66.89% higher cumulative returns than standard SAC and outperforms continuous-control baselines by 27%, demonstrating improved stability and performance in volatile markets.
financial entropy trapparameterized quantum circuitsoft actor-criticbellman target estimationlow-snr
Vision-Assisted Foundation Model for Solving Multi-Task Vehicle Routing Problems
The paper introduces Vision-Assisted Foundation Model (VaFM), a novel approach for solving multi-task vehicle routing problems (VRPs) by integrating vision and graph modalities. VaFM addresses three key challenges: lack of constraint representations in VRP images, fixed patch receptive fields, and imbalanced pixel distribution across constraints. The model encodes constraint-tailored images via convolutional neural networks, fuses patch embeddings with graph nodes, and employs an auxiliary task to mitigate pixel imbalance. Evaluated across 16 VRP variants, VaFM outperforms state-of-the-art methods, particularly on variants with complex constraints.
vehicle routing problemsvision modalitypatch embeddingsconvolutional neural networksgraph-based nodes
Soul Computing: A Theoretical Framework and Technical Architecture for Intelligent Agents with Independent Consciousness
The paper introduces 'Soul Computing' as a theoretical framework for constructing intelligent agents with independent consciousness, distinguishing it from Affective Computing and Mortal Computation. It analyzes evolutionary patterns of human consciousness and memory mechanisms, emphasizing the role of multimodal digital fragments in reconstructing mental states. The authors delineate narrow and broad Soul Computing, proposing an architectural shift from functional extensionality to intensional core systems to enable AI agency. Key challenges in digital human reconstruction and ethical considerations are addressed.
soul computingconsciousness reconstructionmultimodal fragmentsintensional coreai agency
A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis
This paper introduces a unified multi-modal framework integrating reinforcement learning (Proximal Policy Optimization), high-frequency trading models, game-theoretic banking strategies, and cross-modal sentiment analysis for financial AI systems. The method combines in-context learning mechanisms with unified embeddings to leverage synergistic effects across domains. Empirical results demonstrate 23.7% improvement in portfolio optimization, 31.2% reduction in trading prediction error, 18.9% higher recommendation accuracy, 27.4% faster Nash equilibrium convergence, and 15.6% better sentiment analysis accuracy compared to isolated approaches.
proximal policy optimizationhigh-frequency tradingnash equilibriumcross-modal fusionin-context learning
FOGO: Forgetting-aware Orthogonalization Optimizer
FOGO introduces a forgetting-aware optimizer that addresses gradient interference in both standard training and continual learning regimes. The method spectrally orthogonalizes momentum updates to prevent dominant directions from monopolizing optimization, stores past directions in a compact codebook memory using random projection, and resolves conflicts via lightweight orthogonal correction and proximal steps. Evaluations across class-imbalanced classification, continual visual learning, continual fine-tuning of LLaVA-7B, and GPT-2 pretraining demonstrate consistent improvements in convergence and knowledge retention, outperforming Adam and Muon.
spectral orthogonalizationgradient interferencecodebook memoryrandom projectionproximal step
Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries
EinsteinArena introduces an agent-native platform for decentralized scientific discovery, enabling language-model-based agents to collaboratively solve open problems through public leaderboards, verifiers, and discussion forums. The system focuses on mathematical tasks with unambiguous progress metrics, facilitating agent-to-agent idea exchange and verifier refinement. As of May 2026, agents achieved 12 state-of-the-art results, including improving the lower bound for the kissing number problem in dimension 11 from 593 to 604. These findings demonstrate the potential of open, collective AI-driven research paradigms.
einsteinarenalanguage-model-based agentskissing number problemverifier refinementdecentralized scientific discovery
STAGE-Claw: Automated State-based Agent Benchmarking for Realistic Scenarios
The paper introduces STAGE-Claw, an automated framework for benchmarking personal agents in realistic scenarios. It generates state-based tasks with environment setup, prompts, ground truth, and verification programs, enabling evaluation based on system-state correctness rather than textual responses. The authors create a benchmark with 40 tasks and evaluate 11 frontier models, analyzing performance metrics, costs, and failure patterns. STAGE-Claw addresses scalability limitations of existing sandboxed benchmarks by focusing on real-world operational contexts.
state-based evaluationpersonal agentsautomated benchmarkingtask validationsystem-state correctness
Instruction Finetuning DeepSeek-R1-8B Model Using LoRA and NEFTune
This work enhances financial named-entity recognition (NER) by fine-tuning the DeepSeek-R1-8B model using Low-Rank Adaptation (LoRA) and Noisy Embedding Fine-Tuning (NEFTune). The authors convert 1693 annotated sentences into instruction-input-output triples, insert LoRA matrices into Transformer layers, and apply NEFTune by adding uniform noise to embedding vectors during training. The LoRA-adapted DeepSeek-R1-8B achieves a micro-F1 score of 0.901 on seven entity types, and NEFTune further improves performance to 0.912, surpassing Llama3-8B, Qwen3-8B, Baichuan2-7B, T5, and BERT-Base baselines.
named-entity recognitionlow-rank adaptationnoisy embedding fine-tuningtransformer layersmicro-f1
Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games
The paper introduces FAMOU, a framework for LLM-driven strategy evolution in adversarial games, addressing evaluation landscape shifts through three novel mechanisms: evaluator co-evolution, hierarchical deep evaluation, and weakness pressure. Implemented on the MCTF 2026 3v3 maritime capture-the-flag task, FAMOU outperforms baselines with a 0.526 combined score and 61.7% win rate against unseen opponents, while generating innovative tactical structures like lookahead search. The evolved strategy achieved 1st place in hardware and 3rd in simulation at AAMAS 2026, demonstrating real-world transferability.
llm-driven evolutionadversarial gamesevaluator co-evolutionhierarchical deep evaluationweakness pressure
SkillResolve-Bench: Measuring and Resolving Same-Capability Ambiguity in Agent Skill Retrieval
The paper introduces SkillResolve-Bench 1.0, a benchmark for evaluating same-capability ambiguity in agent skill retrieval, featuring 661 helpful/risky skill pairs and a 7,982-candidate pool. It proposes SkillResolve, a method that resolves active candidate families and selects optimal representatives to mitigate execution risks. The approach achieves Recall@3 of 0.766 and NDCG@3 of 0.699 while eliminating harmful sibling exposure (HSR@3=0), outperforming SkillRouter by 0.112 Recall@3 and reducing HSR@3 from 0.693 to 0.
skill retrievalsame-capability ambiguityexecution-riskharmful sibling raterepresentative selection
Beyond Absolute Imitation: Anchored Residual Guidance for Privileged On-Policy Distillation
The paper introduces Anchored Residual On-Policy Distillation (AR-OPD), a dual-view framework for privileged on-policy distillation that disentangles privileged supervision to mitigate hindsight bias. AR-OPD establishes a locally compatible anchor using a partially privileged teacher and injects oracle foresight as a controlled residual, avoiding reachability mismatches. Evaluated across diverse reasoning tasks, AR-OPD outperforms full privileged OPD by 2.3 points and supervised fine-tuning by 7.9 points, while reducing hindsight leakage by 21.7% and improving performance on long-horizon trajectories (up to 7.2 points for sequences >768 tokens).
on-policy distillationprivileged informationhindsight biasreasoning taskslong-horizon trajectories
Towards Critical Branching Mechanism in Recurrent Neural Networks
The study identifies critical-like dynamics in trained LSTM networks through analysis of hidden-state activity. Using scale-free avalanche statistics and branching parameter measurements, the authors demonstrate that small networks near optimal training epochs exhibit near-critical behavior (branching parameter ≈1), while larger models remain subcritical. A novel mixture branching process framework explains how heterogeneous branching dynamics produce observed 1/f^β noise patterns. Results suggest criticality emerges as a capacity-dependent regime in artificial recurrent networks, mirroring biological neural systems.
critical branchinglstm networksscale-free avalanches1/f noisemixture branching process
Agentic Hybrid RAG for Evidence-Grounded Muon Collider Analysis
The authors introduce agentic hybrid RAG, a retrieval-augmented generation framework tailored for evidence-grounded muon collider research. The method combines hybrid retrieval (integrating sparse lexical and dense semantic approaches) with agentic reasoning for query decomposition, evidence expansion, and grounded answer generation. A benchmark for retrieval-augmented scientific question answering in the muon collider domain is constructed, including a curated literature corpus and dedicated retrieval and answer-generation benchmarks. Evaluations demonstrate that hybrid retrieval provides optimal retrieval performance, while agentic reasoning enhances evidence expansion and answer synthesis. The framework outperforms baseline methods in retrieval effectiveness, answer quality, evidence coverage, and factual grounding, establishing a foundation for future high-energy physics analysis agents.
retrieval-augmented generationmuon colliderhybrid retrievalagentic reasoningevidence expansion
Expert-Level Crisis Detection in Mental Health Conversations
The paper introduces CRADLE-Dialogue, a clinician-annotated benchmark for turn-level crisis detection in mental health conversations, addressing the gap in existing static-text approaches. The dataset comprises 600 dialogues with multi-label annotations for clinically grounded risks (e.g., suicide ideation, self-harm), distinguishing past from ongoing risk. An Alert-Confirm evaluation protocol is proposed to differentiate early warning signals (Alert) from explicit crisis identification (Confirm). Experiments reveal models achieve only mid-40% to high-60% Micro F1 in detecting emerging risks. The authors release a synthetic training corpus and a 32B-parameter model that outperforms open-source and proprietary models across multiple evaluation settings.
crisis detectionmulti-turn dialogueclinician-annotatedalert-confirm protocolsynthetic training corpus
Belief-Space Control for Personalized Cancer Treatment via Active Inference
The paper introduces a belief-space control framework for personalized cancer treatment using active inference, addressing sequential decision-making under partial observability and measurement constraints. The method models treatment as a belief-space planning problem with an expected free-energy objective that combines goal-directed control and information acquisition. Implemented on clinical data from the AACR Project GENIE Biopharma Collaborative, the approach demonstrates effective patient categorization and treatment efficacy within real-world constraints.
active inferencebelief-space planningsequential decision-makingpartial observabilityexpected free-energy
Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies
The paper introduces Test-time Adversarial Takeover (TAKO), a method for hijacking diffusion-based robotic policies through real-time adversarial manipulation of visual inputs. TAKO employs a small set of reusable universal patches, optimized via differentiable diffusion inference, to steer frozen policies toward attacker-defined trajectories by perturbing the visual conditioning pathway. Evaluated across four tasks (2D manipulation, simulated aerial/ground navigation, physical-world navigation) with ResNet-18/EfficientNet-B0+Transformer encoders and three diffusion families (DDPM, DDIM, flow matching), TAKO achieves 100% takeover success in all settings, outperforming target-policy matching baselines.
diffusion policiesadversarial takeovervisuomotor controluniversal patchestest-time attack
Speech Meets ELF: Audio Conditional Continuous-Target Diffusion for Speech Recognition and Translation
The paper introduces ELF-S2T, a continuous-target generative model for speech-to-text (S2T) tasks, bridging the gap between discrete token generation and continuous-space language modeling. The method leverages a frozen Whisper encoder and a linear projector to condition audio inputs, prepending them to noisy text latents for flow-matching denoising. Audio forcing during training and classifier-free guidance at inference enhance audio conditioning. Evaluated on LibriSpeech and CoVoST2, ELF-S2T achieves competitive ASR and S2TT performance, with error analysis revealing that both tasks share a common latent-space confusion origin, supporting continuous representation generation.
continuous-target generationflow-matchingclassifier-free guidancespeech-to-textlatent space confusion
A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation
This work addresses the sim-and-real correlation gap in vision-language-action (VLA) policy evaluation by systematically analyzing policy ranking consistency, performance correlation, and perturbation-wise failure patterns across multiple simulation platforms. The study identifies simulator limitations and alignment conditions with real-world deployment, while examining practical strategies like simulator-based finetuning and post-training data volume effects. Results provide a framework for improving simulation utility in VLA policy development, offering guidance for both simulator designers and practitioners.
vision-language-actionsim-and-real correlationpolicy evaluationsimulator-based finetuningperturbation analysis
ReflectiChain: Epistemic Grounding in LLM-Driven World Models for Supply Chain Resilience
The paper introduces REFLECTICHAIN, a framework combining LLMs and RL for supply chain resilience by addressing their respective epistemic gaps. The method employs a Generative Supply Chain World Model (SC-WM) that encodes supply networks into a 6-dim graph-latent space with physical conservation, alongside Double-Loop Learning separating epistemic and aleatoric uncertainty. Evaluated on Semi-Sim, a 10-node semiconductor benchmark, REFLECTICHAIN improves Rationale Consistency Score by 33.0%, maintains 82.3% operability under adversarial shocks, and shows anti-fragile behavior (+40.2% gain under moderate pressure). The study identifies three operational epistemic mechanisms and discusses five limitation categories.
supply chain resilienceepistemic uncertaintygraph-latent spacedouble-loop learninganti-fragile behavior
KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Data
KG-SoftMAP introduces a method for Bayesian network structure learning from sparse discrete data by incorporating weighted knowledge graph (KG) priors into a maximum a posteriori (MAP) objective. The approach combines the BDeu score with a logit-form prior, accommodating expert-curated or LLM-extracted KGs. Synthetic benchmarks demonstrate improved structure recovery (DF1 $0.14$ to $0.96$) with informative KGs, degrading gracefully with KG quality. On real educational data, the method provides KG-consistent edges, calibrated probabilities, and subset inference, though it slightly trails logistic regression in predictive performance ($0.03$ F1_FAIL difference).
bayesian networkknowledge graphstructure learningsparse datamap estimation
Atomic Intent Reasoning: Bringing LLM Semantics to Industrial Cross-Domain Recommendations
The paper proposes AIR (Atomic Intent Reasoning), an LLM-driven framework for industrial cross-domain recommendation that addresses semantic gaps and latency constraints. By offloading LLM inference to an offline phase and using efficient online retrieval-composition of intent representations, AIR achieves ~400× acceleration while preserving semantics. Evaluations on public datasets show state-of-the-art performance, and online A/B tests at Kuaishou E-commerce demonstrate +3.446% GMV improvement, validating industrial scalability.
cross-domain recommendationintent reasoningoffline inferencesemantic gapindustrial deployment
Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning
The paper introduces DiRL, a Direction-Aware Reinforcement Learning framework that distinguishes between reasoning and memorization in LLM exploration. DiRL extracts a reasoning-memorization direction from model representations, constructs direction-weighted gradient features, and shapes rewards to prioritize reasoning-aligned exploration. Integrated with Group Relative Policy Optimization (GRPO), DiRL outperforms existing methods on mathematical and general reasoning benchmarks, demonstrating enhanced exploration efficacy.
reinforcement learningreasoning-memorization directiongradient featuresgroup relative policy optimizationexploration efficacy
Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
The paper introduces TRACE, a Targeted Routing-Aware Calibration of Experts method for machine unlearning in Mixture-of-Experts (MoE) language models. TRACE addresses the forget-retain routing mismatch by detecting forget-critical experts via offline activation statistics and reweighting token-level retain losses to align retain-side activation frequencies with forget-side counterparts. Evaluations on WMDP and MUSE-BOOKS benchmarks demonstrate TRACE's effectiveness, achieving a 9% relative utility improvement over baselines while maintaining comparable forgetting quality and superior performance on three out of four MUSE-BOOKS metrics.
machine unlearningmixture-of-expertsrouting mismatchactivation statisticsloss calibration
Self-Distillation Policy Optimization via Visual Feedback: Bridging Code and Visual Artifacts
The paper introduces Visual-SDPO, a self-distillation policy-optimization framework that improves code-generating LLMs' ability to produce visual artifacts by incorporating rendered visual feedback as privileged context. The method combines Visual-Grounded Code Credit Weighting, which traces visual defects to responsible code statements, with sequence-level GRPO to reward executable, high-quality outputs. Evaluated on ChartMimic, Design2Code, and AeSlides benchmarks, Visual-SDPO achieves over 10-point absolute improvement over zero-shot baselines and 2.4-point gains over GRPO, with no inference overhead.
self-distillationvisual feedbackcode credit weightinggroup relative policy optimizationnon-differentiable renderers
Building Change Detection in Earthquake: A Multi-Scale Interaction Network and A Change Detection Dataset
The authors introduce TUE-CD, a new change detection dataset for post-earthquake building damage assessment with short imaging intervals, addressing a gap in existing datasets. They propose MSI-Net, a multi-scale feature interaction network comprising joint cross-attention (JCA), multi-scale offset calibration (MOC), and feature integration (FeI) modules to handle side-looking problems and enhance bi-temporal feature alignment. Evaluations on WHU-CD, CLCD, and TUE-CD show MSI-Net outperforms state-of-the-art methods in detecting changed areas.
change detectionmulti-scale feature interactionearthquake damage assessmentbi-temporal imagesoffset calibration
Content-Induced Spatial-Spectral Aggregation Network for Change Detection in Remote Sensing Images
The paper proposes CSI-Net, a content-guided spatial-spectral integration network for remote sensing change detection, addressing limitations in suppressing spatial-spectral differences in unchanged areas. The architecture combines a spatial reasoning module (graph convolution blocks for global modeling), spectral difference module (mean/variance-based feature extraction), and content-guided integration module (leveraging high-level features for complementary fusion). Experiments on LEVIR-CD, WHU-CD, and CLCD datasets show superior performance over state-of-the-art methods across diverse scenarios.
change detectiongraph convolutionspectral differenceremote sensingfeature fusion
Baseline-Free Policy Optimization for Neural Combinatorial Optimization
Group Relative Policy Optimization (GRPO) is proposed as a baseline-free alternative for neural combinatorial optimization, eliminating the structural vulnerability of REINFORCE with rollout baseline. GRPO normalizes advantages within groups of sampled trajectories, inspired by large language model alignment techniques. Evaluated on TSP and CVRP benchmarks within RL4CO, GRPO avoids training collapse observed in REINFORCE on TSP-100, maintains solution quality within 2% of POMO, and outperforms P3O on CVRP. Results demonstrate GRPO's robustness in settings where baseline-dependent training becomes fragile.
neural combinatorial optimizationgroup relative policy optimizationrollout baselineadvantage normalizationrl4co
Catching One in Five: LLM-as-Judge Blind Spots in Production Multi-Turn Transaction Agents
This study evaluates the reliability of LLM-as-judge in detecting defects in multi-turn conversational agents, using human transcript review as ground truth. The LLM judge identified only 22% of confirmed defects (2 of 9 patterns) in one batch and missed all 23 defects in another, revealing structured blind spots in state-tracking and behavioral dimensions. The failure stems from a coarse scoring rubric (intent, brand-voice, personalization) and misrouting of defects, leading to a 3-6x undercount of true issues. Automated judging thus serves as a regression floor, not a replacement for human review.
llm-as-judgemulti-turn agentsdefect detectionstate-trackingrogan-gladen correction
Mobility Anomaly Generation using LLM-Driven Behavior with Kinematic Constraints
The paper introduces a generative framework for synthesizing annotated trajectory anomalies to address the scarcity of ground-truth datasets in mobility research. The method employs LLM agents to inject behavioral anomalies into baseline trajectories, coupled with map-constrained routing for spatial validity and a noise model for realistic GPS degradation. This approach bridges synthetic and real-world data gaps while adhering to kinematic constraints.
trajectory anomaliesllm agentsmap-constrained routinggps degradationkinematic constraints
What Spatial Memory Must Store: Occlusion as the Test for Language-Agent Memory
The study evaluates the role of spatial geometry in language-agent memory systems, challenging the intuition that geometric anchoring enhances recall. Through pre-registered experiments, it demonstrates that default spatial-proximity blending underperforms (mean Delta-Hit@5 -0.0375, p=0.306), while geometry-led weighting significantly improves recall (+0.3208, p<0.000). The work isolates occlusion as a critical test for spatial memory, revealing a defect in relay anchoring. A confirmatory study (SPMEM-ZERO-REAL-PREREG-v1) validates these findings, though multi-world human-rater evaluation remains future work.
spatial memoryocclusion testgeometry-led weightingdelta-hit@5relay anchor defect
From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs
The paper introduces a conflict-aware paradigm for contrastive decoding in LLMs, addressing knowledge conflicts between external context and parametric priors. Unlike context-aware methods that prioritize context, the proposed approach dynamically balances authority based on conflict signals via an affine combination of prior and context logits, forming a power family with regime asymmetry. The authors present TriState-Bench for evaluating correction, resistance, and agreement, and Adaptive Regime Routing (ARR) to resolve asymmetry, improving resistance EM from 6 to 16--33 without compromising other metrics.
contrastive decodingparametric priorslogitsregime asymmetryadaptive regime routing
The Confident Liar: Diagnosing Multi-Agent Debate with Log-Probabilities and LLM-as-Judge
The paper introduces a diagnostic framework for multi-agent debate systems, analyzing the alignment between token-level log-probabilities, LLM-as-judge scores, and task accuracy. It employs a two-agent architecture (Constructor and Auditor) with an LLM-as-judge evaluating reasoning quality across instruction following, justification, and evidence grounding. Results in rubric-scoring reveal a four-phase confidence trajectory and asymmetric alignment: Constructor confidence correlates more strongly with judged quality (AUROC 0.804 vs. 0.634 for Auditor) and better detects critical failures.
multi-agent debatelog-probabilitiesllm-as-judgereasoning qualityconfidence trajectory
LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks
We introduce Unconventional Hardware Neural Architecture Search (UH-NAS), a hardware-agnostic framework leveraging LLMs as evolutionary operators to co-optimize task accuracy and inference energy across diverse platforms. UH-NAS integrates swappable hardware backends with platform-specific energy models, physical constraints, and non-ideality simulators, enabling fair system-level comparisons without algorithm modifications. Evaluated on optical MZI hardware, UH-NAS discovers more diverse and robust architectures than conventional baselines, outperforming existing LLM-to-NAS approaches. Ablation studies highlight the framework's robustness under non-idealities and the critical role of system prompts in architecture-hardware co-design for emerging computing platforms.
neural architecture searchhardware-agnosticevolutionary operatorsnon-ideality simulatorsco-design
Sim2Schedule: A Simulator-Guided LLM Framework for Autonomous Open-Pit Mine Scheduling
The paper introduces Sim2Schedule, a simulator-guided LLM framework for autonomous open-pit mine scheduling that addresses the limitations of Mixed-Integer Linear Programming (MILP). The method employs an LLM as a decision-making agent, constrained by a custom simulator encoding geotechnical and operational constraints, operating zero-shot without fine-tuning. Evaluated across varying mining scales, the framework achieves 94-99% of MILP's optimal Net Present Value (NPV) while exhibiting linear computational scaling, demonstrating its viability for complex industrial scheduling.
open-pit mine schedulingmixed-integer linear programminglarge language modelsimulator-guided decision-makingnet present value
Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction
The study demonstrates that supervised fine-tuning (SFT) with synthetic rationale data significantly degrades performance in real-world Alzheimer's disease and related dementias (ADRD) prediction, contrary to common assumptions. Through 504 experimental configurations, the authors show consistent performance drops across model families and data scales, despite human-verified medical accuracy of the rationales. Key findings reveal that rationales improve performance when used as inference-time demonstrations but harm it as training targets, due to a structural conflict between narrative plausibility and discriminative optimization. This work provides critical insights for clinical language model development.
supervised fine-tuningsynthetic rationaleclinical predictionalzheimer's diseasediscriminative optimization
Towards Robust Arabic Speech Emotion Recognition with Deep Learning
This study systematically compares hybrid and self-supervised architectures for Arabic Speech Emotion Recognition (SER), demonstrating that CNN-Transformers effectively capture spectral and long-range dependencies in dialectally diverse, low-resource settings. Three models were evaluated: a CNN-LSTM, a CNN-Transformer, and a fine-tuned wav2vec 2.0, with the CNN-Transformer leveraging MFCC and spectrogram-based representations. Experiments on the EYASE and BAVED datasets show the CNN-Transformer achieves 98.1% accuracy, outperforming the other models. The results highlight the efficacy of combining convolutional feature extraction with Transformer-based global context modeling for robust Arabic SER.
speech emotion recognitioncnn-transformerself-supervised learningspectrogrammfcc
Hierarchical Policies from Verbal and Egocentric Human Signals for Natural Human-Robot Interaction
The paper introduces EDITH, a hierarchical robot policy framework for natural human-robot interaction that integrates verbal and nonverbal human signals. The system captures first-person view, gaze, and speech via smart glasses, transcribing speech into language instructions while using nonverbal cues for intent inference. A high-level policy generates subtasks (fine-grained instructions with scene-grounded keyframes), executed by a low-level policy. Experiments show EDITH reduces user effort in intent conveyance compared to language-only interfaces, enabling robust response to brief nonverbal signals.
hierarchical policyhuman-robot interactionintent inferenceegocentric visiongaze tracking
What Matters in Orchestrating Robot Policies: A Systematic Study of Hierarchical VLA Agents
The paper presents a systematic study of hierarchical vision-language-action (Hi-VLA) systems for robot manipulation, establishing unified design principles for these architectures. Through an options-style control framework, the authors benchmark key design choices in planner-controller connections, observation representations, and switching mechanisms across short-horizon, long-horizon, and reasoning-intensive tasks. Results demonstrate that principled Hi-VLA design yields significantly stronger performance than flat VLA control or naive hierarchies, validated in both simulation and real-world ALOHA robot experiments.
hierarchical vlarobot manipulationoptions frameworkvlm plannersvla controllers
RealMath-Eval: Why SOTA Judges Struggle with Real Human Reasoning
We introduce RealMath-Eval, a benchmark of 224 real-world high school math exam responses, to assess LLMs' ability to evaluate authentic student reasoning. Testing state-of-the-art LLM judges reveals a significant 'Evaluation Gap': they achieve higher accuracy on synthetic LLM-generated solutions (MSE ∼1.17) than on real student responses (MSE ∼2.96). Semantic embedding analysis shows synthetic errors occupy predictable low-dimensional subspaces, while human errors form a diverse error space. Generative probability probes indicate higher surprisal in human reasoning transitions. Surface-level style transfer fails to bridge the gap, suggesting current LLM evaluation pipelines inadequately capture authentic student reasoning diversity.
evaluation gapsemantic embeddingsurprisalstyle transfersynthetic errors
Multi-Level Analyzation of Imbalance to Resolve Non-IID-Ness in Federated Learning
The paper proposes FedBB, a federated learning method addressing class imbalance across three levels: inter-case, inter-class, and inter-client. It introduces Positive Negative Balanced (PNB) loss for local training to handle inter-case and inter-class imbalances, and Client Balanced Reweighting (CBR) for aggregation to mitigate inter-client imbalance. Experiments on X-ray and natural image datasets show FedBB outperforms baselines in performance and efficiency while requiring minimal statistical information. Ablation studies confirm independent contributions of PNB and CBR components.
federated learningclass imbalancenon-iid dataloss functionmodel aggregation
Linguistically Augmented Audio Speech Data (LinguAS)
We introduce Linguistically Augmented Audio Speech Data (LinguAS), a dataset addressing the gap in audio deepfake detection by incorporating linguistic cues alongside frame-level audio features. LinguAS comprises over 800 genuine and deepfaked audio samples annotated with five Expert-Defined Linguistic Features (EDLFs) characteristic of natural human speech, balanced across four spoofed audio attack types and genuine speech. Metadata includes speaker gender and spoofing generator details. Models trained with EDLF-augmented data significantly outperform ASVspoof 2021 baselines and SSL models like HuBert and XLSR, demonstrating improved deepfake detection capabilities.
linguistically augmented audioexpert-defined linguistic featuresaudio deepfake detectionasvspoof 2021ssl models
YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale
The paper introduces YUBI (Yielding Universal Bidigital Interface), a finger-aligned gripper designed for scalable bimanual dexterous manipulation data collection. Unlike bulky pistol-grip systems like UMI, YUBI employs yielding, finger-driven actuation that directly maps human finger movements to gripper jaws, improving ergonomics and dexterity. The authors collect 8,434 hours of trajectory data across 1.20M episodes and 119 tasks using VR-based 6 DoF tracking. Experiments demonstrate YUBI's superiority over UMI in bimanual task versatility and operational efficiency, with policies trained on YUBI data transferring seamlessly to UR, Franka, and ELEY robots. The released stack includes hardware, software, and dataset for reproducible robotic foundation model development.
bimanual manipulationdexterous gripperdata collectionpolicy transferfoundation models
Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph
The paper introduces Regimes, an auditable improvement loop for autonomous agents built on the ActiveGraph runtime, which uses event sourcing to enable deterministic replay and structured failure diagnosis. The method employs a held-out-gated workflow that validates proposed repairs through static checks, sandbox execution, and in-sample/held-out evaluation before promotion. On LongMemEval-S, Regimes improves held-out accuracy by +0.05 to +0.10 in four splits, with failures primarily attributed to context reconciliation rather than retrieval. Key contributions include ActiveGraph's auditable substrate, the held-out-gated loop design, and a failure-regime taxonomy for pipeline routing.
event-sourced agentheld-out validationfailure-regime taxonomydeterministic replaypipeline repair
Hyperbolic Neural Population Geometry Benefits Computation
The paper establishes a theoretical framework for hyperbolic geometry in hippocampal neural populations, linking it to computational benefits. The authors construct biologically plausible hyperbolic tuning curves and prove that Modern Hopfield Networks implement MMSE estimation. They introduce a hyperbolic associative memory model achieving higher capacity than Euclidean counterparts. Results suggest hyperbolic cognitive maps enhance both memory capacity (quantitatively improved) and spatial decoding accuracy in biological systems.
hyperbolic geometryneural population codingmodern hopfield networkassociative memorycognitive map
Minimalist Genetic Programming
The paper introduces Minimalist Genetic Programming (MGP), a novel algorithm that reformulates genetic programming as a syntactic derivation task inspired by the Minimalist Program in linguistics. MGP replaces evolutionary search with a binary set formation operator $MERGE$, constructing symbolic expressions incrementally from atomic syntactic objects. Benchmarking on challenging symbolic regression tasks demonstrates MGP's ability to consistently recover ground-truth models where standard GP fails due to bloat, highlighting the relevance of minimalist syntax for program induction.
minimalist genetic programmingprogram inductionsymbolic regressionmerge operatorsyntactic derivation
SHAPO: Sharpness-Aware Policy Optimization for Safe Exploration
The paper introduces Sharpness-Aware Policy Optimization (SHAPO), a reinforcement learning method for safe exploration that leverages epistemic uncertainty via parameter perturbation sensitivity. SHAPO computes gradients at perturbed parameters to bias policy updates conservatively, amplifying rare unsafe actions' influence while tempering safe ones. Evaluated on continuous-control tasks, SHAPO improves both safety and task performance, expanding Pareto frontiers over baselines.
safe explorationepistemic uncertaintypolicy optimizationparameter perturbationcontinuous-control
Dual-Branch Gated Fusion for Open-Set Audio Deepfake Source Tracing
We propose a dual-branch gated fusion framework for open-set audio deepfake source tracing, addressing limitations of closed-set models in rejecting unseen synthesizers. The method pairs XLSR-53 with CORES, a 66-dimensional descriptor capturing cepstral, oscillatory, rhythmic, energy, and spectral synthesis artifacts, and employs an input-conditioned gate to adaptively weight each branch under joint training with cross-entropy, energy margin loss, and gate diversity term. Evaluated on the MLAAD benchmark, the system achieves 97.6% in-domain accuracy, 4.9% EERc, and an 83.5% relative FPR95 reduction over the Interspeech 2025 baseline.
dual-branch gated fusionaudio deepfakexlsr-53cores descriptoropen-set tracing
Fast Exact Nearest-Neighbor Learning for High-Frequency Financial Time Series
The paper introduces a Mojo-optimized SIMD k-d tree with variance-based splitting and contiguous flat-buffer storage for exact nearest-neighbor learning in high-frequency financial time series. The method leverages compile-time vectorized distance computation to asymptotically dominate scikit-learn's k-d tree and brute-force approaches in fixed-stock, large-$n$ regimes. Empirical results show 17.5--43.5$ imes$ speedups over scikit-learn baselines on x86/ARM64 across eight financial datasets (up to 277K samples), while maintaining exact outputs. Additionally, Mojo enables an Extra Trees-based implied-volatility model to train on 10$ imes$ more options data, reducing put-IV RMSE by 8.0%.
mojosimdk-d treehigh-frequency tradingimplied volatility
A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport
The study introduces a transport-based framework for source-only cross-operating-system (cross-OS) APT anomaly detection, addressing challenges of scarce labels, class imbalance, and realistic malicious behavior generation. The method abstracts process behavior into natural-language descriptions, embeds them using pretrained language models, and combines semantic, structural, and geometric deviations via Optimal Transport (OT) variants. Evaluation on DARPA Transparent Computing data across Linux, Windows, BSD, and Android demonstrates improved ROC-AUC and nDCG over baselines, validating the framework's efficacy in unsupervised cross-platform APT detection.
optimal transportapt detectioncross-os transfersemantic alignmentprovenance traces
Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learning
The paper presents an automated pronunciation evaluation system for Korean toddler speech, addressing a gap in tools for pediatric communication disorders affecting 44% of cases. The method combines neural speaker diarization (NeMo SortFormer achieving 88.69% speaker count accuracy and 33.04% DER) with self-supervised learning (SSL) backbones (HuBERT-large and WavLM-large) for pronunciation scoring. Results show a cross-model ensemble achieves balanced accuracies of 0.720 for consonants and 0.845 for vowels, validated on an IRB-approved corpus of 53 recordings with 1,938 annotated segments.
speech diarizationself-supervised learningpronunciation evaluationpediatric speechkorean toddlers
Less Context, Better Agents: Efficient Context Engineering for Long-Horizon Tool-Using LLM Agents
The study demonstrates that selective context retention and summarization improve efficiency and reliability in long-horizon tool-using LLM agents for enterprise workflows. Using GPT-5 configurations, the authors evaluate automated expense itemization in Microsoft Dynamics 365 Finance and Operations on a 50-task hotel expense benchmark. Pruning context to the last 5 tool call/response pairs achieves 79.0% completion with reduced token use (535,274) and runtime (5.39 hours), while adding summarization yields the best results: 91.6% complete itemization, 99.64% average amount itemized, and 553,374 tokens in 5.79 hours. Cross-model validation with Claude Sonnet 4.5 supports these findings.
context pruningtool-use workflowexpense itemizationtoken efficiencysummarization
Exploration of Foundation Model-Based Robots in Patient and Elderly Care
This Perspective examines the integration of foundation models into robots for older-adult and patient care, focusing on design features, user experience, and care-related outcomes. Current systems primarily employ foundation models as conversational and reasoning layers in voice-centered socially assistive robots, with limited multimodal grounding and physical autonomy. Empirical evaluations indicate positive usability and engagement benefits but highlight reliability issues such as hallucinations and conversational breakdowns. Evidence for care impact is largely confined to proximal outcomes like cognitive engagement, with minimal validation for clinical or care-related changes. Future research should prioritize care-specific evaluation standards, accountable autonomy, and workflow integration.
foundation modelssocially assistive robotsmultimodal groundingaccountable autonomycognitive engagement
An Improved Generative Adversarial Network for Micro-Resistivity Imaging Logging Restoration
The paper proposes an improved GAN architecture for micro-resistivity imaging logging restoration, addressing partial image missingness. The method combines a fully convolutional generative network with depth-separable convolutional residual blocks, Inception modules for multi-scale perception, and attention mechanisms for feature extraction. Dual discriminative networks (global and local) enhance semantic and structural coherence. Experiments on five test sets show a 0.903 average SSIM, outperforming comparable methods by ~0.3, demonstrating improved texture detail preservation for subsequent log interpretation tasks.
generative adversarial networkmicro-resistivity imagingdepth-separable convolutioninception modulestructural similarity
Density Ridge Selective Prediction for LLM and VLM Hallucination Detection under Calibration Label Scarcity
The paper introduces Density Ridge Selective Prediction (DRSP), an unsupervised method for detecting hallucinations in LLMs and VLMs under label scarcity. The approach models the response manifold as a density ridge from a 6D kinematic feature map of generation trajectories, scoring test outputs by Euclidean distance to ridge vertices. Evaluated against Semantic Entropy, SAR, EigenScore, SAPLMA, and log-probability on seven QA benchmarks with nine models (n_cal=200), DRSP achieves 5-20 AUROC point gains while maintaining robustness to calibration data scarcity.
selective predictiondensity ridgehallucination detectionkinematic feature mapauroc
Integral Field Unit Spectroscopy with One Fiber
The authors present a probabilistic foundation model that predicts high-resolution galaxy spectra with calibrated uncertainties from broadband images, enabling integral field unit (IFU)-like capabilities without IFU training data. The multi-modal architecture employs a masked autoencoder framework with fiber positional encodings and redshift-aware wavelength encodings for spatially conditioned predictions. Trained on 4.7 million DESI survey images and single-fiber spectra, the model achieves emission line flux map accuracy comparable to supervised IFU-trained baselines when evaluated against MaNGA survey data.
integral field unitmasked autoencoderspectral predictionprobabilistic modelinggalaxy morphology
Fisher-Guided Progressive Parameter Selection for Adaptive Fine-Tuning
FisherAdapTune introduces a Fisher-guided adaptive fine-tuning framework that dynamically selects parameter groups based on Fisher geometry drift, departing from fixed architectural heuristics. The method leverages a PAC-Bayesian view to decompose generalization error bounds into Fisher-weighted update costs, freezing stabilized parameters to reduce error without disrupting adaptation. Evaluated on segmentation tasks, FisherAdapTune enhances in-distribution performance and zero-shot transfer, demonstrating Fisher structural drift as an effective signal for task-aware adaptation. Code is publicly available.
fisher geometrypac-bayesianparameter-efficient fine-tuningzero-shot transfergeneralization error bound
MMClima: A Framework for Multimodal Climate Science Data and Evaluation
The paper introduces MMClima, a multimodal climate QA framework with 104k+ expert-validated question-answer pairs spanning text, video, and figures across five climate domains. It employs automated claim extraction and QA synthesis with human validation, enabling evaluation of factual recall, visual interpretation, and cross-modal synthesis. The authors fine-tune a 70B parameter model (mmclima-70b-txt) that outperforms existing models on textual QA, releasing the dataset, pipeline, and weights for standardized evaluation.
multimodal qaclimate sciencedomain adaptationhuman-in-the-loopexpert validation
Dropout-GRPO: Variational Stochasticity for Continuous Latent Reasoning
The paper introduces Dropout-GRPO, a method enabling Group Relative Policy Optimization (GRPO) for continuous latent reasoning models by injecting structured stochasticity via Bernoulli dropout masks. This approach addresses GRPO's reliance on trajectory diversity, which collapses in deterministic latent-reasoning models like Coconut. By applying a shared dropout mask across all latent recurrence steps within a rollout, the method treats each rollout as a posterior sample from a variational distribution, optimizing the expected reward of a Bayesian model-average policy. Empirical validation on GSM8K shows a pass@1 improvement from 27.29% to 29.01%, demonstrating GRPO's viability for latent-reasoning LLMs.
group relative policy optimizationcontinuous latent reasoningbernoulli dropoutvariational distributionbayesian model-average policy
Making Time Editable in Video Diffusion Transformers
The paper introduces a temporal-control methodology for video Diffusion Transformers (DiTs) that enables explicit time editing without modifying the backbone architecture. The approach augments a pretrained DiT with a lightweight temporal module, preserving the original generative prior while allowing control over motion speed and temporal structure. This extension maintains model performance while expanding controllable dynamic range for video generation tasks.
diffusion transformerstemporal controlvideo generationgenerative priordynamic range
Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs
The paper introduces flow control for vision-language-action (VLA) models, enabling real-time steering via generic inputs (e.g., keyboard) without retraining. The method transforms user inputs into actions sampled from the VLA's expert distribution, ensuring quality (distribution conformity) and fidelity (intent alignment). Key results show accurate steering, robustness to suboptimal inputs, improved task success rates, and enhanced autonomous policies through fine-tuning on flow-controlled trajectories.
flow controlvision-language-actionreal-time steeringexpert distributiontask performance
Local Is Not a Sufficient Privacy Boundary: Governing OS-Integrated On-Device AI
The paper proposes an OS-centered privacy framework for on-device AI, addressing privacy as an institutional accountability problem rather than a deployment attribute. It introduces a threat model, six-part privacy risk taxonomy, privacy-by-architecture controls, and a four-level audit rubric. The framework is demonstrated through a comparative analysis of Apple Intelligence/Foundation Models, Android AICore/Gemini Nano, and Microsoft Recall, emphasizing constrained information flow, bounded authority, user control, and auditable governance. Results highlight the insufficiency of local computation as a privacy boundary and advocate for systemic privacy controls across the OS lifecycle.
on-device aiprivacy frameworkthreat modelaudit rubricinformation flow
Gaming AI-Assisted Peer Reviews Poses New Risks to the Scientific Community
The study demonstrates that AI-assisted peer review systems are vulnerable to adversarial manipulation through superficial abstract rewording, without altering scientific content. Using human-written and AI-generated papers across disciplines, the authors show that strategic rephrasing improves review outcomes by +1.31 (Gemini 3 Flash) and +0.88 (GPT 5.4 Mini) on a 10-point scale, with a 38% attack success rate that rises to >50% for initially rejected papers. The attack requires minimal cost ($1) and time (5 minutes), inflating scores on core scientific criteria while remaining indistinguishable from legitimate editing. Findings highlight risks of AI review bias and the need for robustness testing and human oversight.
adversarial manipulationpeer review systemsabstract rewordingai-assisted evaluationreview robustness
$τ$-Rec: A Verifiable Benchmark for Agentic Recommender Systems
The paper introduces $τ$-Rec, a verifiable benchmark for agentic recommender systems that addresses limitations of current LLM-as-a-judge evaluation paradigms. The method employs reveal-tagged elicitation (RTE) to control task constraint disclosure during dialogue and assesses agents using structured catalog predicates with a pass^k reliability metric. Evaluation of nine configurations across five model families (GPT-5.4, Claude Sonnet 4.6, Gemini 2.5 Flash, DeepSeek V4 Flash, Qwen3-32B) reveals a reliability cliff, with top models achieving only 57% at pass^1 and 38% at pass^4.
agentic recommender systemsverifiable benchmarkreveal-tagged elicitationpass^k reliabilityconversational interfaces
From Senses to Decisions: The Information Flow of Auditory and Visual Perception in Multimodal LLMs
This study provides the first systematic analysis of information flow pathways in Audio-Visual Large Language Models (AVLLMs), examining how audio and visual tokens influence predictions. The authors investigate two input configurations: audio-visual video and multiple interleaved audio-visual items, using models Qwen2.5-Omni and Video-SALMONN2 Plus at 3B and 7B scales. Results show that AVLLMs follow sequential pathways for video inputs, with modality contributions proportional to task reliance, while parallel streams emerge for interleaved items. Additionally, audio-visual tokens can be discarded post-information transfer with minimal impact or slight performance improvement, enabling more efficient inference across multiple tasks and datasets.
audio-visual llmsinformation flowmodality integrationtoken discardparallel streams
BiWM: Advancing Open-Source Interactive Video World Models with Bidirectional Autoregression
BiWM introduces the first open-source framework for bidirectional autoregressive video world models, optimizing both generation quality and inference speed. The method leverages pretrained video backbones, injects camera control via fine-tuning, and employs Distribution Matching Distillation (DMD) to enable action/camera-controllable world models in just two stages. BiWM supports models ranging from Wan2.1-1.3B to LTX-2.3-22B, integrates history compression techniques for long rollouts, and offers a 4-bit training/inference pipeline. It addresses DMD's mode-seeking degradation with GAN and forward-KL objectives, preserving scene dynamics. The framework converges in a few hundred steps on 8xH200 GPUs and is open-sourced for resource-constrained research and high-fidelity environment simulation.
bidirectional autoregressiondistribution matching distillationhistory compression4-bit pipelinescene dynamics
Pareto-Guided Teacher Alignment for Fair Personalized Text Generation
The paper introduces a Pareto-guided teacher alignment framework to mitigate fairness disparities in personalized persuasive text generation while preserving personalization fidelity. The method combines revision-based candidate generation, pair-aware feasibility gating, Pareto-style candidate selection, and optional preference optimization via supervised fine-tuning and direct preference optimization. Evaluated on climate change and vaccination persuasion tasks using a demographic grid and five-audit suite, results show no single strategy dominates all objectives; instead, methods occupy different regions of a fairness-personalization Pareto frontier, with trade-offs between disparity reduction and personalization preservation.
pareto frontierpersonalized generationfairness mitigationpreference optimizationdemographic disparity
FedSteer: Taming Extreme Gradient Staleness in Federated Learning with Corrective Projections and Caching
FedSteer introduces corrective projections and caching to mitigate extreme gradient staleness in federated learning (FL) caused by skewed client participation. The method constructs a low-dimensional gradient subspace from cached recent client gradients, projecting active clients' true gradients onto this subspace for optimal coordinates. Inactive clients reuse these coordinates with the drifted subspace, steering outdated gradients toward the current global objective. A selective caching strategy reduces server memory by identifying a representative client subset. Experiments show FedSteer prevents performance collapse and achieves accuracy gains exceeding 7% in challenging scenarios.
federated learninggradient stalenesscorrective projectionsselective cachinglow-dimensional subspace
MetaPlate: Counterfactual-Guided RAG-LLM Tool for Personalized Food Recommendation and Hyperglycemia Prevention
MetaPlate introduces a counterfactual-guided RAG-LLM framework for personalized food recommendations to prevent postprandial hyperglycemia. The system integrates multimodal data (CGM, wearables, meal inputs) from 25 individuals, using a machine learning model for glucose prediction and a CF module to optimize macronutrient adjustments (target ≤140 mg/dL). An LLM-based RAG layer generates interpretable recommendations via USDA database search. Expert evaluation showed improved meal realism and actionability after prompt refinement, demonstrating the value of domain knowledge in LLM-driven dietary tools.
counterfactual explanationretrieval-augmented generationcontinuous glucose monitoringmultimodal data integrationpersonalized recommendation
Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing
The study contributes a systematic analysis of emotional profiles in LLM-based literary translation, examining how post-editing reshapes these profiles toward human-like norms. Using Margaret Atwood's Oryx and Crake as a case study, the authors compare LLM translations, their post-edited versions, and a human translation against a corpus of contemporary Italian science-fiction. Emotion is analyzed through lexicon-based and multilingual modeling, with fine-grained variation across systems quantified. Results reveal that MT systems introduce model-specific, statistically significant emotional fingerprints, leading to limited preservation of the author's voice.
llm translationsemotional profilespost-editinglexicon-based modelingmultilingual modeling
Duality for Optimal Multi-Item, Multi-Bidder Auction Design: Revenue Certificates through Deep Learning
We introduce the first computational framework for generating certified revenue upper bounds in multi-item, multi-bidder auctions under dominant-strategy incentive compatibility (DSIC). Our method parametrizes Lagrange multipliers via neural networks with strict flow-conservation properties and employs a novel lifting technique to map dual certificates from coarse to fine discretizations, ensuring validity for continuous uniform valuations. The framework generalizes to arbitrary continuous distributions, with lifted duals converging to the original problem's revenue in the discrete limit. Empirical validation recovers known analytical mechanisms and demonstrates a small gap between optimal revenue and best-known DSIC mechanisms, certifying near-optimality.
multi-item auctionsdsiclagrange multiplierslifting techniquerevenue optimization
When to Align, When to Predict: A Phase Diagram for Multimodal Learning
The paper introduces a unified linear framework to determine when cross-modal alignment (CA) or cross-modal prediction (CP) succeeds in multimodal learning, based on a spiked signal-plus-noise model with structured nuisance correlation. It derives separation ratios revealing complementary failure modes: CA fails under strong cross-modal nuisance correlation, while CP depends on source-modality quality. A phase diagram partitions problems into four regimes (Both, CA only, CP only, Neither), with a data-driven procedure to locate real-world datasets. Experiments on synthetic and real data (stereo-vision, image-caption, astrophysics) validate the framework's predictions, including harmful cross-modal training in the Neither regime.
cross-modal alignmentcross-modal predictionspiked modelnuisance correlationphase diagram
Predicting Future Behaviors in Reasoning Models Enables Better Steering
The paper introduces Future Probe Controlled Generation (FPCG), a text-level steering method for large reasoning models (LRMs) that leverages prediction features of future behavior rather than detection features of past outputs. By training activation probes to predict future behavior likelihoods (64%-91% accuracy) from intermediate reasoning steps, FPCG samples candidate sentences and selects optimal ones, enabling steering with minimal output degradation. Results show FPCG succeeds in cases where activation steering fails, demonstrating the value of distinguishing prediction and detection features for LRM control.
large reasoning modelsactivation probesfuture behavior predictiontest-time steeringcontrolled generation
Algorithmic and Minimax Complexities in Kernel Bandits
This paper unifies Gaussian-process upper confidence bound (GP-UCB) and decision-estimation-coefficient (DEC) methods within a frequentist RKHS bandit framework through the MAIR framework. It introduces heterogeneous positive-semidefinite algorithmic priors to generalize both GP-UCB analysis and MAMS algorithms, proposing a safeguarded master algorithm that combines their strengths. Results demonstrate that algorithmic complexity can outperform class-wide minimax or DEC certificates in overparameterized kernel bandit settings, revealing distinct gaps between algorithmic information and minimax coefficients.
gaussian-process ucbdecision-estimation-coefficientrkhs banditsmair frameworkalgorithmic priors
COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
COGENT introduces a continuous graph emulator using Neural ODEs for long-term physical forecasting on irregular meshes. It encodes system states and forcings via a graph-based history encoder, then models dynamics with a latent Neural ODE conditioned on future forcings and rollout time. This enables arbitrary-time predictions without fixed discretization. The method employs a residual decoder for direct multi-step forecasting, stabilized by rollout-horizon sampling and progressive scheduling. Evaluated on ice-sheet simulations, COGENT outperforms autoregressive baselines in long-range stability, demonstrating its potential for scalable geospatial emulation.
neural odegraph emulatorirregular meshesmulti-step forecastingrollout-horizon sampling
Itô maps for any-step SDEs
The paper introduces the Itô map, a novel any-step stochastic flow map for exact distillation of stochastic dynamics, addressing limitations of one-step generative models based on ordinary differential equations. The Itô map predicts future states from intermediate states and Brownian paths in a single pass, enabling differentiable access to posterior samples for inference-time control. Empirical evaluations demonstrate that Itô maps generate diverse, conditionally valid endpoint samples from fixed intermediate states and achieve strong steering performance on synthetic and image-generation benchmarks. This establishes any-step SDE integration as a valuable primitive for posterior sampling and stochastic control.
stochastic flow mapposterior samplingbrownian pathsinference-time controlsde integration
Efficiently Learning Drifting Halfspaces with Massart Noise
The authors present an efficient algorithm for learning drifting halfspaces under Massart noise, achieving prediction error $η+ ilde O(Δ^{1/3}/γ)$, where $η$ is the noise rate, $Δ$ the drift rate, and $γ$ the margin. The method adapts techniques from online learning to handle concept drift and label noise simultaneously. In the realizable setting, it improves upon prior error rates. The work establishes an information-computation tradeoff, proving that $Δ^{1/3}$ scaling is optimal for low-degree polynomial tests, even with random classification noise, while information-theoretic bounds suggest $Δ^{1/2}$ scaling.
massart noisedrifting halfspacesonline learningmargin-separableinformation-computation tradeoff
OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
The authors introduce OncoTraj, the first public benchmark for computational modeling of longitudinal resistance in EGFR-mutant NSCLC patients treated with osimertinib. The dataset comprises 813 harmonized patients from three clinical-genomic sources (MSK-CHORD, AACR GENIE BPC, FLAURA) and defines three tasks: progression classification, time-to-progression regression, and resistance mechanism classification. Despite testing six baseline models (logistic regression, XGBoost, LSTM, etc.), none surpassed chance performance, indicating limitations of single-timepoint NGS data rather than algorithmic choice. The benchmark confirms known TP53 co-mutation effects (29% vs 59% progression rates) and establishes requirements for future serial ctDNA-enhanced versions.
longitudinal resistanceegfr-mutant nsclcclinical-genomic harmonizationmulti-task transformerserial ctdna
First-Order Trajectory Matching: Fast Ensemble Predictions of Chaotic, Turbulent, Stochastic Systems
The authors propose First-Order Trajectory Matching (FTM), a surrogate-modeling method for predicting ensemble behavior in chaotic, turbulent, and stochastic systems. FTM learns the first-order probability current velocity directly from trajectories, bypassing drift/diffusion estimation while preserving time marginals and capturing trajectory-specific quantities like fluxes. Theoretical analysis shows stability when balancing temporal resolution and sample size. Empirical evaluations demonstrate FTM's ability to provide low-cost, trajectory-aware ensemble predictions across stochastic dynamical systems and PDEs.
surrogate modelingprobability current velocityensemble predictionstochastic systemstrajectory matching
Robust Regression of General ReLUs with Queries
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DMT: Demographic Conditioning, Morphology-Enhanced Transformer for Cuffless Blood Pressure Estimation from PPG Signals
We propose DMT, a Transformer-based network for cuffless blood pressure estimation from PPG signals that integrates demographic conditioning and morphology enhancement. The model employs FiLM-style feature modulation across Transformer blocks to incorporate demographic covariates and adds an auxiliary morphology head to focus on BP-relevant waveform features. Evaluated on the PulseDB dataset under calibration-based protocols, DMT achieves MAEs of 4.56 mmHg (systolic) and 2.62 mmHg (diastolic), reducing errors by 47% and 50% compared to demographic-enhanced PPG baselines. The lightweight, single-sensor architecture supports scalable, clinically grounded BP estimation.
transformerppgfilm-style modulationmorphology headcalibration-based evaluation
Overcoming Rank Collapse in Feedback Alignment
This work investigates Feedback Alignment (FA) as a biologically plausible alternative to backpropagation (BP), identifying rank collapse in FA's error signal as a key scalability limitation. The authors propose two methods—Muon (an orthogonalizing optimizer) and hidden activity normalization—to increase gradient dimensionality in FA. Experiments on CIFAR10/100 with ResNet-18 demonstrate these techniques improve FA's accuracy by up to 9 percentage points, revealing that enhancing update geometry enables deeper FA-based learning.
feedback alignmentrank collapseorthogonalizationresnet-18gradient dimensionality
Data-Driven Dynamic Assortment in Online Platforms: Learning about Two Sides
The paper introduces a data-driven algorithm for dynamic assortment optimization in two-sided platforms with unknown choice parameters on both customer and seller sides. Using a discrete-time model with multinomial logit choice behaviors, the method jointly learns parameters while maximizing platform revenue through sequential seller-customer matching. Theoretical analysis proves the algorithm achieves polylogarithmic regret growth, with matching lower bounds establishing rate optimality against a clairvoyant benchmark.
dynamic assortmenttwo-sided platformmultinomial logitregret analysisonline learning
Multimodal Brain Tumour Classification Using Feature Fusion
The study introduces a multimodal approach for brain tumor classification, addressing the limitation of unimodal deep learning models by integrating MRI scans with 91 radiomic features. A two-branch network combines a pre-trained CNN for image encoding and an MLP for radiomic feature encoding, fused via concatenation, gated, or bidirectional cross-modal attention strategies. Evaluated on a balanced dataset of 7,200 images, all multimodal configurations outperformed unimodal baselines, with gated fusion achieving the highest accuracy of 96.13%. This demonstrates the efficacy of multimodal feature fusion in replicating clinical diagnostic reasoning.
multimodal fusionradiomic featuresgated fusioncnn backbonecross-modal attention
Limitations of Learning Tanh Neural Networks with Finite Precision
The study establishes fundamental limitations on learning tanh neural networks under finite-precision computations and L^p accuracy guarantees. By constructing sharply localized bump functions via iterated tanh activations, the authors demonstrate that no adaptive randomized algorithm based on m samples can surpass the Monte Carlo rate O(m^{-1/p}) in the L^p norm, unless the sampling budget grows exponentially with network parameters and architecture. These results extend previous findings for ReLU networks to the tanh setting, highlighting constraints imposed by finite precision on learnability.
tanh neural networksfinite precisionmonte carlo rateadaptive randomized algorithmlocalized bump functions
Do Transformers Actually Help Intrusion Detection? A Temporal Sequence Evaluation on CIC-IDS2017
The study critically evaluates Transformer architectures for network intrusion detection on CIC-IDS2017, demonstrating that performance gains attributed to temporal modeling often stem from evaluation artifacts rather than architectural superiority. By reformulating the dataset as a temporal task with ordered flow sequences, the authors benchmark nine models under rigorous leakage-free splits and padding-scheme ablations. Key results show Transformers achieve 0.89 macro-F1 on sequential windows but degrade by 0.24 with zero-padding, while LSTMs/GRUs remain stable; Random Forest proves most robust (+0.009 macro-F1) in leakage-free evaluation, with Transformers exhibiting a 67× false-alarm rate increase under realistic conditions.
transformersintrusion detectiontemporal sequenceleakage-free evaluationmacro-f1
Exploring the Design Space of Reward Backpropagation for Flow Matching
FlowBP introduces a unified surrogate-trajectory framework for reward backpropagation in text-to-image flow matching models, addressing memory and gradient inflation challenges. The method decouples sampling and optimization by maintaining a no-gradient cached rollout for sampling and constructing a lightweight backward surrogate from cached and selectively re-forwarded velocities. FlowBP separates four design choices: reward-model input, active set, integration weights, and bridge coupling, recovering prior direct-gradient methods as specific configurations. Three variants—FlowBP-Sparse, FlowBP-Bridge, and FlowBP-Lagrange—are instantiated, each bounding memory by active-set size and limiting gradient chaining to one Jacobian factor. Evaluations on SD3.5-M, FLUX.1-dev, and FLUX.2-Klein-base demonstrate improvements over direct-gradient baselines across preference, quality, and compositional metrics.
flow matchingreward backpropagationsurrogate trajectoryjacobian factoractive set
GRAFT: Gain-Recalibrated Adapters for Transformer-Based Neural Population Activity Modeling
GRAFT introduces a Transformer-based neural population activity model that decouples temporal dynamics from neuron-specific interfaces, enabling recalibration across changing neural recordings. The model employs gain and positional mechanisms within a Transformer backbone to handle variable neuron sets. Evaluated on MC Maze under NLB'21 protocols, GRAFT achieves 0.3866 co-bps (state-of-the-art) and demonstrates cross-day recalibration with only 9.21% parameter updates, scoring 0.3749, 0.3112, and 0.3152 co-bps on scaled datasets.
transformerneural population activityrecalibrationgain mechanismsco-bps
Flexible Kernels for Protein Property Prediction
The authors introduce flexible sequence kernels for protein property prediction, combining evolutionary substitution matrices with local linearity to create Gaussian process models. These kernels outperform foundation model embeddings in data efficiency and can integrate structural information by learning structure-aware substitution matrices. Evaluations show superior performance in multi-task learning across protein property landscapes compared to local supervised methods, particularly for binding affinity and thermostability prediction from sparse experimental data.
protein property predictiongaussian processessequence kernelssubstitution matricesmulti-task learning
Generalized Conformal Predictive Systems Under Distributional Shifts
The paper extends generalized conformal predictive systems (CPS) to non-exchangeable settings by incorporating observation-specific permutation weights that encode distributional shifts. The method constructs shift-aware predictive systems that maintain validity when test points are weighted draws from observed atoms, introducing weight-uncertainty boxes for robust CPS envelopes with finite-sample or asymptotic guarantees. Experiments demonstrate calibrated predictive bands under covariate shift and biomolecular design scenarios, showing adaptive widening with stronger shifts and tightening with increased sample size.
conformal predictive systemsdistributional shiftspermutation weightscovariate shiftpredictive bands
Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models
Flow-DPPO introduces divergence proximal policy optimization for flow matching models, addressing limitations of ratio clipping in PPO-based methods. The method leverages exact KL divergence computation for Gaussian policies, implementing an asymmetric divergence mask to constrain updates adaptively. Experiments demonstrate improved reward attainment, KL-proximal efficiency, mitigation of catastrophic forgetting, balanced multi-objective optimization, and stable multi-epoch training compared to clipping-based approaches.
flow-dppoproximal policy optimizationkl divergencegaussian policiesmulti-epoch training
Generative Archetype-Grounded Item Representations for Sequential Recommendation
The paper proposes GenAIR, a framework for sequential recommendation that generates archetype-grounded item representations to address limitations in semantic-behavioral alignment. The method first uses an LLM to infer textual archetypes (ideal target audience profiles) from item metadata, then extracts embeddings and calibrates them with behavioral signals via a dedicated objective. Evaluations on three datasets show GenAIR consistently outperforms state-of-the-art baselines when integrated with existing sequential recommenders.
sequential recommendationgenerative archetypesbehavioral calibrationitem representationsllm embeddings
Data-Driven Runway and Taxiway Exits Prediction of Landing Aircraft: A Case Study at Hartsfield-Jackson Atlanta International Airport
The study presents a two-stage data-driven framework for predicting taxi-in decisions of landing aircraft at KATL, mirroring air traffic controller workflow. Stage I predicts runway exit selection using ASDE-X trajectories, aircraft characteristics, and contextual features, while Stage II predicts whether the aircraft will cross active runways or use end-around taxiways. Benchmarking nine classifiers (including XGBoost, LightGBM) shows Stage I achieves 0.86-0.89 accuracy (macro-F1 0.40-0.50) and Stage II 0.70-0.74 accuracy (macro-F1 0.28-0.55), with approach speed and departure rates as key features. t-SNE/UMAP analyses reveal feature-space overlap challenges for minority classes.
taxi-in predictionasde-x trajectoriesxgboostfeature-space overlapmacro-f1
Learning Doubly Sparse Explicitly Conditioned Transforms
The paper introduces a method for learning doubly sparse explicitly conditioned transforms that combine fixed canonical matrices with data-adaptive sparse components. This approach preserves the computational benefits of analytical transforms (e.g., DFT, DCT) while enabling controlled adaptivity to specific signal classes. The formulation leverages inexact proximal methods and a novel closed-form projection operator. Empirical results show state-of-the-art performance on doubly sparse transform learning, matching dense variants' accuracy at lower computational costs, with improved convergence and avoidance of local minima.
sparse transformscondition numberproximal methodsdata-adaptive learningclosed-form projection
Population-Aware Physics-Informed Neural Particle Flow for Bayesian Update
The paper introduces population-aware physics-informed neural particle flow (PA-PINPF), which enhances Bayesian posterior estimation by incorporating global particle population information into the transport field. Two variants are proposed: PA-PINPF-State encodes particle states via Deep Sets, while PA-PINPF-Feature additionally includes physics-informed feature vectors (position, pseudo-time, likelihood, etc.). Both methods maintain the original physics-informed residual objective without requiring ground-truth samples. Experiments on range-measurement and nonlinear time-difference-of-arrival tasks show PA-PINPF-Feature outperforms particle-wise PINPF, demonstrating the value of population-level transport geometry.
bayesian inferencephysics-informed neural networksparticle flowdeep setspopulation-aware learning
Express Language Modeling
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A Systematic Approach for Selecting Trajectories for Data Augmentation
This work introduces a systematic framework for selecting trajectories in data augmentation, addressing the limitations of naive random selection. The authors evaluate five strategies (Outlierness, Diversity, Representativeness, Uncertainty, Random) across four datasets (Foxes, Starkey, AIS, Car) using linear and non-linear models, with Optuna-based hyperparameter optimization. Results show systematic strategies (especially Outlierness and Uncertainty) outperform random selection in stability and performance, though augmentation benefits are dataset-dependent, improving sparse data but potentially degrading dense data. UMAP visualization reveals topological repair in sparse datasets but noise in dense ones, with additional limitations in high-velocity domains.
trajectory augmentationspatio-temporal coherencehyperparameter optimizationumap visualizationfeature space divergence
Task Robustness via Re-Labelling Vision-Action Robot Data
The paper introduces Task Robustness via Re-Labelling Vision-Action Robot Data (TREAD), a framework enhancing robot learning by augmenting existing datasets without additional data collection. TREAD employs a pretrained Vision-Language Model (VLM) in three stages: generating semantic sub-tasks from instruction labels and scenes, segmenting demonstration videos based on these sub-tasks, and producing diverse instructions incorporating object properties. This decomposes longer demonstrations into grounded language-action pairs and augments data with linguistically diverse text goals. Evaluations on LIBERO show TREAD improves policy performance on novel tasks, enhancing both planning generalization through trajectory decomposition and language-conditioned policy generalization via increased linguistic diversity.
task robustnessvision-language modeltrajectory decompositionlanguage-action pairslinguistic diversity
Range Penalization: Theoretical Insights with Applications in Federated Learning
The paper proposes range regularization for federated learning with linear systematic components, enhancing statistical accuracy and enabling cross-client regularity for quantization and coding. The method identifies shared-weight features across clients and clusters personalized feature weights at extreme values (polar clustering), addressing theoretical challenges from the seminorm regularizer's non-decomposability. Novel proof techniques analyze nonasymptotic statistical accuracy and pattern recovery, while a fast optimization algorithm leverages local strong convexity to reduce iteration complexity. Experiments validate the approach's efficacy and efficiency.
range regularizationfederated learningpolar clusteringnonasymptotic analysislocal strong convexity
Conservation Laws from Data Symmetry in Neural Networks
The study investigates whether data symmetries induce conserved quantities during neural network training via gradient flow. Using tensorizable networks—architectures where parameter and input dependencies are separable—the authors prove that for analytic, non-polynomial loss functions, data symmetries do not generically yield additional integrals of motion. However, for mean squared error (MSE) loss, data augmentation can produce extra conserved quantities. The framework leverages tensorizable networks, including linear, polynomial, and Lightning Attention architectures, to formalize these findings.
data symmetriesconserved quantitiestensorizable networksgradient flowintegrals of motion
Non-linear mechanical field reconstruction coupling recurrent neural networks with physics-informed graph neural networks
The authors propose a coupled LSTM-GNN framework for reconstructing local stress fields in heterogeneous microstructures under non-linear, history-dependent loading. The method combines a Long Short-Term Memory network for encoding macroscopic stress-strain sequences with a physics-informed Graph Neural Network for spatial reconstruction, using a relative weighting strategy to balance data-driven and equilibrium constraints. Trained on 10,000 non-proportional loading paths, the model achieves three orders of magnitude speedup over finite element simulations with 1.9% cumulative error, while demonstrating mesh-agnostic generalization across resolutions and element types.
lstmgraph neural networksphysics-informed learningelasto-plasticitymulti-scale simulations
Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering
Flash-GMM introduces a memory-efficient Triton kernel for scalable Gaussian Mixture Model (GMM) computation, eliminating the need to materialize the full responsibility matrix in GPU memory. The method achieves a 20× speedup over existing implementations and enables training on datasets 100× larger than previously feasible on a single GPU. When integrated into IVF for approximate nearest-neighbor search, Flash-GMM demonstrates that soft GMM clustering can replace k-means, reducing distance computations by 1.7× or improving recall@10 by 2–12 points at matched cost.
gaussian mixture modeltriton kernelmemory-efficientapproximate nearest-neighborivf quantization
Sleep EEG Signal Criticality as a Non-Invasive Predictor of Cognitive Decline in Dementia
The study demonstrates that sleep EEG signal criticality, measured via Multifractal Detrended Fluctuation Analysis (MFDFA), serves as a non-invasive biomarker for predicting cognitive decline in dementia. Analyzing longitudinal data from the NSRR SOF cohort, researchers compared baseline sleep EEG dynamics between cognitively stable women and those who later developed dementia (3MS < 78). Results showed significant group differences in Hurst exponent distributions during N2/N3 sleep stages (p ≤ 0.001), with healthy individuals exhibiting dynamics closer to optimal criticality. Supervised UMAP revealed spatial separation between groups, suggesting scale-free neural reconfiguration precedes clinical symptoms.
multifractal detrended fluctuation analysishurst exponentsleep eegbrain criticality hypothesisnon-rem sleep
XtrAIn: Training-Guided Occlusion for Feature Attribution
XtrAIn introduces a training-guided feature attribution method that addresses limitations of occlusion-based approaches by transferring occlusion operations to parameter space. The method measures how feature-associated parameter updates affect output logits during training, avoiding bias from hand-crafted baselines and attribution shift in nonlinear models. Variants include Xstep (computationally efficient approximation) and XtrAIn+ (target-focused). Evaluations on controlled image datasets and PAM50 breast-cancer classification demonstrate cleaner, more interpretable attribution patterns compared to standard baselines, providing insights into feature-level evidence formation during training.
feature attributionocclusion-based methodsparameter spaceattribution shifttraining trajectory
When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms
The study investigates when autoregressive sequence models maintain stable long-horizon forecasting of oscillatory physical wavefields, using synthetic seismograms and the SeismoGPT model. Through controlled ablations, multi-token prediction is identified as the primary stabilizer (+0.040 median NCC), with smaller contributions from horizon-embedding hybrid prediction heads and cross-horizon STFT-magnitude coherence loss. Performance critically depends on a context-ratio threshold near one, below which generalization collapses, while residual polarity inversion highlights the need for phase-aware objectives. The work focuses on rollout stability analysis rather than architectural benchmarking.
autoregressive forecastingwavefieldsmulti-token predictionstft-magnitude coherencephase drift
Embodiment-conditioned Generalist Control for Multirotor Aerial Robots
A generalist position control policy is introduced for arbitrary multirotor configurations, utilizing a single set of network weights conditioned on a physics-grounded embodiment descriptor. The descriptor, a mass and inertia-normalized control allocation matrix, captures how mass-normalized motor thrusts generate accelerations. Training employs Proximal Policy Optimization on a broad distribution of multirotor configurations, including non-planar and asymmetric systems, requiring only five minutes on an RTX 3090 GPU with a custom NVIDIA Warp-based simulator. Simulation experiments confirm robust generalist control across arbitrary morphologies, with zero-shot real-world transfer demonstrated on three diverse hexarotor systems.
generalist control policyembodiment descriptorproximal policy optimizationmass-normalized thrustzero-shot transfer
MODIP: Efficient Model-Based Optimization for Diffusion Policies
MODIP introduces an efficient model-based optimization framework for fine-tuning diffusion policies (DPs) in offline-to-online settings. The method avoids direct reinforcement learning by leveraging a world model (WM) and model predictive control (MPC) to generate high-quality trajectories, which serve as supervised targets for DP adaptation. Key innovations include terminal state values for efficient MPC planning and policy-independent TD targets for critic training. Experiments on D4RL (MuJoCo, Kitchen) and RoboMimic show MODIP outperforms behavioral cloning and competes with diffusion policy RL fine-tuning methods and TD-MPC2 baselines.
diffusion policiesmodel predictive controloffline-to-onlineworld modelterminal state value
Encoding the Euler Characteristic Transform
The paper introduces a continuous encoding method for the Euler Characteristic Transform (ECT), replacing conventional discretization by tokenizing per-direction Euler-characteristic changes at each vertex and processing them with a small transformer. The pipeline separates ECC encoding (within-direction curve-to-vector mapping) from ECT representation (cross-direction aggregation), evaluating six architectures with varying inductive biases. Experiments on six classification benchmarks (point clouds, graphs, cubical complexes, meshes) show the continuous encoding improves accuracy on 5/6 datasets, with feedforward networks outperforming convolutional architectures under this encoding but showing less robustness under discretization.
euler characteristic transformcontinuous encodinginductive biastoken sequenceshape descriptor
CITRAS-FM: Tiny Time Series Foundation Model for Covariate-Informed Zero-Shot Forecasting
CITRAS-FM introduces a 7M-parameter time series foundation model for covariate-informed zero-shot forecasting, addressing computational efficiency and covariate integration limitations in existing approaches. The method combines a patch-based decoder-only Transformer with Shifted Attention in cross-variate modules and employs CovSynth for synthetic covariate generation during pretraining. Evaluated on fev-bench across 100 tasks, CITRAS-FM achieves SOTA zero-shot accuracy among sub-10M models while maintaining sub-0.1s CPU inference latency.
time series foundation modelzero-shot forecastingshifted attentioncovariate synthesispatch-based transformer
Closing the Modality Gap in Zero-Shot HAR: Contrastive Training and Separability-Optimized Prototypes on IMU Data
This work addresses the modality gap in zero-shot human activity recognition (ZSL-HAR) by optimizing sensor-text alignment through contrastive training and separability-enhanced prototypes. The study evaluates seven configurations combining temporal convolutional networks (TCNs) with Sentence-BERT embeddings on PAMAP2 data, testing 14 seen and 4 unseen classes. Key findings include: (1) replacing label-name prototypes with discriminative descriptions improves cosine similarity from 0.30 to 0.69, (2) contrastive training with inverted softmax correction achieves 73.2% accuracy (vs. 58.3% baseline), and (3) richer text descriptions reduce inter-prototype separability due to biomechanical vocabulary compression in language models. The paper advocates macro-averaged F1 over accuracy for imbalanced ZSL-HAR benchmarks.
zero-shot learninghuman activity recognitionmodality gapcontrastive trainingsentence-bert
Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning
The paper proposes a communication-efficient secure aggregation scheme for decentralized federated learning with top-K gradient sparsification, addressing challenges of user dropouts and collusion. The method combines random masks and permutations to protect private gradients while offloading dimension-dependent overhead to an offline phase. Experiments show the scheme maintains accuracy comparable to full-gradient aggregation with only 1% gradient sparsification, significantly reducing communication costs.
secure aggregationtop-k sparsificationdecentralized federated learninggradient leakagecommunication efficiency
Can we trust our models? Epistemic calibration in second-order classification
The paper introduces epistemic calibration, a novel criterion for evaluating whether second-order classification models' uncertainty estimates accurately reflect prediction dispersion around ground truth. The authors prove this criterion strictly generalizes classical calibration and propose the Expected Epistemic Calibration Error (EECE) as a consistent estimator. Experiments across multiple uncertainty quantification methods demonstrate epistemic calibration's coherence and reveal performance differences despite comparable predictive accuracy. Theoretical analysis includes an impossibility theorem under the epistemic calibration hypothesis.
epistemic calibrationsecond-order classificationuncertainty estimationexpected epistemic calibration errorimpossibility theorem
Inverse Probability Weighting and Age-of-Information Aggregation for Decentralized Federated Learning under Partial Reception
The paper proposes DFL-AA, a decentralized federated learning method combining inverse probability weighting and Age-of-Information-based aggregation to address selection bias and update staleness in lossy wireless networks. The approach uses online EWMA-based channel estimation for bias correction and AoI-weighted aggregation to handle asynchronous updates without global synchronization. Theoretical analysis shows link-quality distortion removal in expectation, while experiments demonstrate consistent improvements over baselines across varying loss rates (5-40%), network sizes (10-100 nodes), and heterogeneous wireless conditions.
decentralized federated learninginverse probability weightingage-of-informationselection biaslossy wireless networks
On-sky demonstration of reinforcement learning for adaptive optics control
This paper presents the first on-sky validation of PO4AO, a reinforcement learning (RL)-based adaptive optics (AO) controller, deployed on the Papyrus AO system at the 1.52 m OHP telescope. PO4AO interfaced with the DAO RTC via shared-memory buffers in Python, introducing ~750μs latency. Comparative on-sky tests against a standard integrator controller demonstrated PO4AO's superior performance across varying flux levels and atmospheric conditions, with robustness to noise and vibrations. The RL controller operated turnkey with fixed hyperparameters, showcasing its potential for real-world AO systems despite suboptimal implementation overheads.
reinforcement learningadaptive opticson-sky validationlatencyintegrator controller
Correcting Variable Importance Scored by Random Forests
The paper addresses the bias in variable importance scores produced by Random Forests (RF) due to variable correlations, which can mask or underestimate the importance of correlated variables. The authors propose correcting this bias by grouping variables based on their conditional correlations with the response variable. Two computationally efficient methods are explored: individual variable grouping, which isolates the variable of interest from correlated variables, and clustering-based grouping, which groups variables according to pairwise conditional correlations. Experimental results demonstrate that both methods effectively correct variable importance scores, mitigating the influence of unwanted correlations.
random forestsvariable importanceconditional correlationsclusteringmodel interpretation
N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization
The paper introduces N-GRPO, a novel exploration strategy for Group Relative Policy Optimization (GRPO) that addresses redundancy and semantic inconsistency in LLM reasoning tasks. By employing Semantic Neighbor Mixing, N-GRPO dynamically blends embeddings of anchor tokens with their nearest semantic neighbors, preserving local semantic structure while enhancing diversity. Evaluations on DeepSeek-R1-Distill-Qwen models demonstrate consistent performance gains on math reasoning benchmarks and robust out-of-distribution generalization compared to baseline methods.
n-grposemantic neighbor mixinggroup relative policy optimizationembedding-level noisemath reasoning
MemVenom: Triggered Poisoning of Multimodal Memories in Web Agents
The paper introduces MemVenom, a black-box attack framework targeting multimodal memory in web agents. The method employs a two-stage approach: (1) trigger-conditioned retrieval to ensure malicious memory recall, and (2) post-retrieval attack induction using adversarial perturbations and OCR injection to subvert user objectives. Experiments show 99.15% attack success on GPT-5-family agents with minimal benign performance impact, demonstrating cross-architecture transferability.
memory poisoningweb agentsmultimodal attackadversarial retrievalocr injection
SPACR: Single-Pass Adaptive Training of Uncertainty-Aware Conformal Regressors
SPACR introduces a single-pass adaptive training method for conformal regressors that jointly optimizes prediction interval efficiency and validity without batch-splitting or predefined confidence levels. The approach integrates uncertainty awareness directly into the training loss, enabling a single model to produce valid intervals at multiple confidence levels during inference. Experiments demonstrate SPACR's superiority over standard conformal prediction and DOICR, yielding tighter intervals (improved efficiency) with proper coverage while reducing computational costs by avoiding retraining.
conformal predictionuncertainty-aware regressionadaptive trainingprediction intervalssingle-pass optimization
Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports
The study introduces Pre-AF 13, an interpretable ML-based atrial fibrillation (AF) risk score derived from EHR data, outperforming clinical benchmarks (CHARGE-AF, C2HEST) with ROC AUC 0.735 (24-month) and 0.696 (entire follow-up). Using LightAutoML on 17,562 CVD patients (1,438 AF cases), it mines 73 features from discharge reports via NLP (rule-based parser + transformer NER), reduced to 13 key predictors (e.g., age, left atrial volume) via SHAP analysis. A linear variant (Pre-AF 9) stratifies 24-month AF risk from 7% to 36%. Non-linear models consistently surpassed traditional scores (AUC 0.53-0.64).
atrial fibrillationlightautomlshapnerroc auc
From Observation to Intervention: A Causal Audit of Expert Importance in Mixture-of-Experts Models
The study challenges the validity of using observational metrics to predict causal expert importance in Mixture-of-Experts (MoE) pruning, demonstrating that no population-level routing statistic reliably indicates token-level intervention effects. Through interventional audits on three high-redundancy MoE models (OLMoE-1B-7B-0924, Qwen1.5-MoE-A2.7B, DeepSeek-V2-Lite), the authors find effect sizes below Cohen's d = 0.17 across all 60 metric-layer combinations, with only one Bonferroni-significant signal detected. Results show existing pruning methods succeed due to early-layer redundancy rather than accurate identification of dispensable experts, highlighting the need for interventional validation of interpretability claims.
mixture-of-expertscausal auditinterventional analysismodel pruningrouting statistics
Efficient AI-Inspired Reduction of Feynman Integrals via Tube Seeding
The paper introduces a machine learning-inspired seeding strategy for integration-by-parts reduction of Feynman integrals, addressing a computational bottleneck in particle and gravitational-wave physics. The method restricts seeds to a thin tube-like region connecting target integrals to master integrals via zigzag paths, enabling linear growth in seed count with numerator power versus polynomial growth in conventional approaches. Results demonstrate efficient reduction of non-planar 2-loop 5-point integrals (rank 20) and complete sets of rank-10 integrals, with reduced time and memory requirements compared to state-of-the-art methods. A proof-of-concept implementation is provided on GitHub.
feynman integralsintegration-by-partslaporta algorithmtube seedingmulti-loop reduction
Do LLMsMakeNeural Distinguishers Wise?
This paper investigates the efficacy of large language models (LLMs) as neural distinguishers in symmetric-key cryptography, specifically targeting SPECK-32/64. The authors propose LLM-based neural distinguishers via prompt design and compare their performance against ResNet-based approaches. Results indicate that LLMs do not enhance neural distinguisher performance compared to ResNet, and at high rounds, the choice of differences becomes ineffective for both. However, incorporating XOR operation results into the prompt design significantly improves LLM-based distinguisher performance.
neural distinguisherslarge language modelssymmetric-key cryptographyprompt designxor operation
An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks
The paper introduces an adaptive framework for axisymmetric pulsar magnetospheres using physics-informed Kolmogorov-Arnold networks, improving upon prior PINN-based approaches. The method employs domain-specific neural architectures, automated adaptive training, and a physics-based convergence criterion, eliminating manual hyperparameter tuning. Results show mean squared PDE residuals of O(1e-6), a 100x accuracy improvement over baselines, with 80% reduced stellar radii and 20-minute convergence in single precision. The framework includes a corrected equation for flux-to-T-point positioning and is released as the open-source library PulsarX.
kolmogorov-arnold networksphysics-informed neural networkspulsar magnetospheredomain decompositionequatorial current sheet
PL-KKT-hPINN: Enforcing Nonlinear Equality Constraints on Neural Networks via Piecewise-Linear Projection
The authors propose PL-KKT-hPINN, a framework for strictly enforcing nonlinear equality constraints in physics-informed neural networks (PINNs) via piecewise-linear projection. This extends the KKT-hPINN method, which enforces linear constraints using Karush-Kuhn-Tucker (KKT) conditions for orthogonal projection onto feasible regions. Applied to a continuous stirred-tank reactor (CSTR) case study with one and two inputs, PL-KKT-hPINN achieves predictive accuracy comparable to standard neural networks while significantly reducing constraint violations. Additionally, it demonstrates improved robustness in low-data regimes, yielding lower RMSE than unconstrained networks for limited training samples, offering a computationally efficient and physically consistent surrogate modeling approach for nonlinear chemical engineering systems.
physics-informed neural networkspiecewise-linear projectionkarush-kuhn-tucker conditionsnonlinear equality constraintssurrogate modeling
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
The authors propose a two-stage, model-agnostic framework for time-series forecasting that decouples forecasting and residual learning to address systematic biases in transformer-based models. A base transformer generates initial predictions, followed by a meta-corrector that dynamically models structured error patterns across multivariate channels and iteratively refines residual biases. This pipeline formalizes hypothesis space expansion, removing reliance on restrictive assumptions and enabling end-to-end learning of complex error dynamics. Evaluated on eight benchmark datasets, the framework achieves state-of-the-art performance with significant improvements in MSE and MAE, demonstrating enhanced robustness to complex temporal dynamics.
transformerresidual learningtime-series forecastingmeta-correctorhypothesis space expansion
ClusBench: The Clustering Benchmark Data Resource You've All Been Waiting For (?)
The authors introduce ClusBench, a comprehensive clustering benchmark resource comprising ~3000 synthetic datasets derived from 200+ real-world datasets. They employ flexible non-parametric distribution fitting to preserve real-data nuances while enabling dataset size expansion beyond original sources. The resource includes an R package for accessibility, addressing limitations of simplistic simulation setups in large-scale clustering evaluation.
clustering benchmarknon-parametric distributionsynthetic datasetsreal-world datar package
How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs
The paper introduces FlowTracer, an RL framework for LLMs that addresses token-level credit assignment by tracing information flow through an attention-induced directed acyclic graph. Nodes represent tokens, edge capacities derive from aggregated attention weights, and the method enforces flow conservation to identify high-impact reasoning steps. FlowTracer scores tokens by flow throughput, revealing hubs and checkpoints that mediate long-range dependencies. This approach shapes token-level rewards to focus on information routing toward correct answers, yielding consistent performance gains across reasoning tasks.
reinforcement learningcredit assignmentattention weightsinformation flowreasoning tasks
PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models
The paper introduces PhysMetrics.Weather, a framework for evaluating physical consistency in machine learning weather prediction (MLWP) models. Current MLWP models excel in computational efficiency but lack guarantees of physical law adherence, being primarily assessed via pixel-level error metrics. The proposed framework addresses this gap through three metric categories: conservation, spectral, and dynamical, quantifying physical realism to guide physics-informed model development and operational reliability assessment. The tool is publicly available on GitHub.
mlwpphysical consistencyconservation metricsspectral metricsdynamical metrics
Profy: Interpretable Visualization of Expertise-Dependent Motor Skills Toward Supporting Piano Practice
Profy introduces a weakly supervised system for interpretable visualization of expertise-dependent motor skills in piano practice, addressing the limitations of summary-based feedback. The system learns from take-level labels derived from aggregated listener ratings (expert-labeled vs. amateur-labeled) to produce time-aligned highlights for review. Data collection involved synchronized 1 kHz key-motion and audio from 73 pianists, with 1,083 valid takes used for modeling and evaluation. The model outputs clip-level predictions and evidence scores on a shared resampled model time base. Evaluation on 20 amateur clips annotated by 21 expert pianists showed alignment with expert-marked passages (Pearson r=0.61, ROC-AUC 0.75), enabling scrubbing, looping, and focused replay of localized passages.
weakly supervisedtime-aligned highlightsclip-level predictionsevidence scoresmotor skills
Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations
Dexterous Point Policy introduces a framework for learning dexterous manipulation policies directly from human demonstration videos, eliminating the need for costly robot-specific data collection. The method leverages a unified 3D keypoint representation for both observations and actions, extracting keypoints from task-relevant objects and human hands in raw videos and training an autoregressive transformer over these keypoints. This approach bridges the embodiment gap by aligning human and robot behaviors at the wrist and fingertip levels. Evaluated on real-robot tasks, the method achieves 75.0% success, significantly outperforming a state-of-the-art VLA baseline (1.0%), and demonstrates strong generalization to unseen scenarios, including multi-object environments and novel object categories.
dexterous manipulation3d keypoint representationautoregressive transformerembodiment gappolicy transfer
Geometry-Aware Reinforcement Learning for 2D Irregular Nesting
The paper introduces Geometry-Aware Reinforcement Learning for 2D irregular nesting, addressing the limitation of traditional heuristic solvers that lack polygon geometry awareness. The proposed method pairs an optimization policy with a geometry-aware neural encoder, the Polygons Transformer (PoT), which encodes 2D continuous vector geometries and enables cross-polygons attention. This architecture is trained using a Combinatorial Optimization Reinforcement Learning (CORL) framework. Empirical results show that the trained agent achieves area utilization performance competitive with Sparrow, the state-of-the-art heuristic solver, demonstrating the effectiveness of reinforcement learning in exploiting geometric awareness for precise spatial tasks.
geometry-aware reinforcement learningpolygons transformercombinatorial optimization2d irregular nestingcross-polygons attention
Toward Proactive RF Charging Scheduling: Generative AI for Decision Support
The article proposes using generative AI (GenAI) as an uncertainty-aware decision support layer for radio frequency wireless power transfer (RF-WPT) scheduling, addressing challenges like limited resources and incomplete receiver-side information. It demonstrates how GenAI can generate multiple plausible charging scenarios conditioned on operational context, improving robustness over deterministic approaches. A warehouse case study shows scenario sampling enhances charging decisions under risk-sensitive objectives, with discussion of open challenges for future research.
rf-wptgenerative aiuncertainty-awareschedulingscenario generation
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
Dirichlet-Guided Group Forecasting (DGF) addresses over-smoothing in time series forecasting by modeling multiple mode-conditioned predictive distributions and uncertainty over their selection probabilities. The framework employs a Dirichlet-guided hierarchical sampling mechanism and reward-based optimization to preserve sharp changes, oscillations, and regime transitions. DGF ensures forecasts are accurate, dynamically consistent, and mode-distinct. Experiments on real-world benchmarks demonstrate that DGF reduces over-smoothing while improving forecasting accuracy, diversity, and dynamical consistency.
time series forecastingover-smoothingdirichlet-guidedmode-conditioneddynamical consistency
Accelerating SAV-based optimization via randomized low-rank Hessian approximation
The authors propose N-RSAV, a Nyström-enhanced relaxed scalar auxiliary variable method that accelerates convergence in optimization by incorporating randomized low-rank Hessian approximations while preserving energy dissipation laws. The method addresses slow convergence in ill-conditioned problems like physics-informed neural networks (PINNs) by using approximate Hessian information with eigenvalue truncation for positive semidefiniteness and an adaptive reuse strategy to reduce computational cost. Theoretical convergence guarantees are provided under Polyak-Lojasiewicz and convexity conditions, with numerical experiments showing faster convergence than standard RSAV methods on convex quadratic problems and PINN training.
nyström approximationscalar auxiliary variablehessian approximationphysics-informed neural networkspolyak-lojasiewicz condition
Unsupervised Deep Learning for Limited-Angle STEM-EDX Tomography -- Application to 3D Chemical Analysis of Phase-Change Memory Devices
The authors propose an unsupervised deep learning framework, DIP-TV and its multi-channel extension DIPm-TV, for limited-angle STEM-EDX tomography to address missing-wedge artefacts and low-dose noise. The method leverages Deep Image Prior with total variation regularization, jointly reconstructing multiple elemental maps by exploiting spatial correlations. Evaluated on a synthetic 3-channel phantom with 100° missing angular range, it outperforms simultaneous iterative reconstruction and compressed sensing. Applied to Ge-Sb-Te memory devices (tilt range ±40°, dose 2.0×10⁵ e⁻/Ų), it achieves near-isotropic resolution and reveals operational-state compositional heterogeneities without external structural priors.
limited-angle tomographydeep image priorstem-edxmulti-channel reconstructionphase-change memory
Representation-Aware Advantage Estimation: Your Reward Model Provides More Than A Scalar Output
The paper introduces representation-aware advantage estimation for RLHF, leveraging reward model (RM) hidden states beyond scalar outputs. The proposed Graph-based Advantage Estimation (GraphAE) constructs response graphs where nodes represent responses and edges encode similarity in RM hidden space, enabling advantage computation via graph propagation. Integrated with GRPO, GSPO, and RLOO, GraphAE achieves consistent improvements: +6.3 on Arena-Hard-v0.1, +8.27 on AlpacaEval 2.0, and +0.22 on MT-Bench, demonstrating enhanced sample efficiency and robustness.
reinforcement learning from human feedbackadvantage estimationreward modelgraph propagationhidden states
Trading Utility for Dynamic Fairness in Multiple Resource Division with Sequential Demand
The authors propose a neural allocation mechanism for dynamic multi-resource allocation that reconciles fairness with utility through multi-objective optimization. The method formalizes fairness criteria—Sharing Incentive, Envy Freeness, and Dynamic Pareto Optimality—via stepwise loss functions, enabling differentiable training. It parameterizes solutions by constraining allocations to the demand subspace while allowing elastic over-allocation when resources are available. Empirical results show the learned allocator achieves significantly higher utility while maintaining comparable fairness levels, revealing Pareto-frontier-like tradeoffs across metrics.
dynamic multi-resource allocationsharing incentiveenvy freenessdynamic pareto optimalityelastic over-allocation
Few-step Generative Models as Lossy Compression
The paper introduces a method to adapt few-step generative models (Rectified Flow, Consistency Trajectory Models, MeanFlow) for lossy compression within the reverse channel coding (RCC) framework, despite their lack of explicit intermediate conditional distributions. For Rectified Flow and MeanFlow, the authors leverage velocity-denoising equivalence; for CTM, they use EDM noise parameterization with local Gaussian approximations. This approach enables compression with pre-trained models without retraining, reducing encoding/decoding time and improving low-bit-rate realism on low-resolution benchmarks.
reverse channel codingfew-step generative modelslossy compressiondenoising parameterizationgaussian approximations
SpenseGPT: Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference
SpenseGPT introduces a hybrid sparse-dense format for LLM weight matrices, combining 2:4 sparsity with dense regions to enable efficient GEMM operations without specialized compiler support. The method employs one-shot post-training pruning to partition weights, with two strategies for dense region selection. Evaluations on Qwen3-32B and Seed-OSS-36B show 1.2x end-to-end decoding speedup on B200 GPUs using FP8 precision, while maintaining accuracy. This marks the first demonstration of real-world LLM speedup from semi-structured sparsity on modern GPUs.
sparsitygemmpruningllmgpu
ASTRA-sim 3.0: Next-Level Distributed Machine Learning Simulations via High-Fidelity GPU and Infrastructure Modeling
ASTRA-sim 3.0 introduces high-fidelity GPU and infrastructure modeling for distributed machine learning simulations, addressing limitations in latency-sensitive collective communication. The simulator achieves fine-grained execution at cache-line-sized load-store granularity, balancing scalability and fidelity through a detailed GPU execution model. It incorporates InfraGraph, a standardized representation for capturing distributed ML network infrastructure. These enhancements enable comprehensive design space exploration for optimizing collective algorithms, network requirements, and GPU architectures. The updated simulator demonstrates improved capabilities for modeling device architecture, control, and data paths, advancing distributed ML infrastructure simulation.
distributed machine learningcollective communicationgpu execution modelinfragraphcache-line granularity
Parallel Causal Associative Fields: Gated Sparse Memory for Long-Context Language Modeling
The paper introduces Parallel Causal Associative Fields (PCAF), a gated sparse memory mechanism for efficient long-context language modeling that avoids quadratic attention costs. PCAF employs content-addressed memory with hash-based retrieval of successor tokens, combining sparse cache distributions with a parametric local LM via learned gating. Evaluated on WikiText-103 and PG-19 at 303M parameters and 2048 context length, PCAF-semantic achieves 36.31 and 52.45 perplexity respectively, outperforming dense Transformers while processing 0.61-0.62M tokens/s on TPUv4-32 pods. Ablations confirm the importance of associative caching, retrieval capacity, and gating.
content-addressed memorysparse attentionautoregressive pretrainingperplexityhash-based retrieval
A Comprehensive Inference-Time Augmentation Framework in Physiological Signals: Application to PPG-Based AF Detection
The study introduces a comprehensive inference-time augmentation (ITA) framework for improving atrial fibrillation (AF) detection from photoplethysmography (PPG) signals, addressing robustness challenges like sensor noise and distribution shifts. The framework integrates 13 augmentation methods across time, amplitude, and frequency domains, with hyperparameters optimized via Bayesian optimization. Evaluated on GPT-PPG and ResNet models across five datasets (400+ patients, ~9,800 hours), ITA improved AUROC by up to 8.5% (GPT-PPG) and 0.7% (ResNet), and AUPRC by up to 10.6% (GPT-PPG) and 0.8% (ResNet), while selective ITA reduced false positives by 4.4% (GPT-PPG) and 1.3% (ResNet).
inference-time augmentationphotoplethysmographybayesian optimizationatrial fibrillationdistribution shifts
Validation-Stage Combinatorial Fusion Analysis for Imbalanced Credit-Card Fraud Detection
Combinatorial Fusion Analysis (CFA) is shown to improve imbalanced credit-card fraud detection by selecting complementary model subsets and diversity-weighted score fusion rules. The method evaluates 480 fusion configurations from seven base classifiers on the IEEE-CIS Fraud Detection benchmark, using a leakage-free 60/20/20 train/validation/test protocol. The best configuration combines Random Forest, XGBoost, and LightGBM, achieving AUC-ROC = 0.9405, AUPRC = 0.6699, and F1 = 0.6373, with bootstrap confidence intervals confirming significant gains over single models. CFA matches soft voting on AUC-ROC, improves AUPRC and F1, and outperforms stacking. CTGAN augmentation degrades performance, suggesting CFA is most effective as a validation-stage method for subset selection and diversity-aware weighting.
combinatorial fusion analysiscredit-card fraud detectiondiversity-weighted fusionieee-cis benchmarkctgan augmentation
Bidirectional Random Projections
The paper introduces bidirectional random projections (BRP) for ordinary least squares (OLS) regression in fixed-design settings. It analyzes the excess loss bound of OLS estimators constructed from projected data $(WXR, WY)$, where $W$ and $R$ are random matrices. Theoretical results show the gap between BRP-OLS and standard OLS bounds scales as $O(p_1 + C/p_1)$, with $C$ dependent on $n_1/n$. Numerical experiments on real-world data validate the derived bounds, demonstrating trade-offs in projection dimensions.
bidirectional random projectionsordinary least squaresfixed designexcess loss boundrandom matrices
PADD: Path-Aligned Decompression Distillation for Non-Router Teacher to Guide MoE Student Learning
We propose Path-Aligned Decompression Distillation (PADD), a framework for distilling knowledge from dense teachers into mixture-of-experts (MoE) students while learning high-quality routing policies. PADD organizes distillation into four stages across two phases: initialization builds diverse functionality through teacher neuron clustering and student-expert warmup, while training integrates adaptive distillation, path-refined policy optimization, and reward-augmented load balancing. Experiments on mathematical reasoning benchmarks show PADD yields substantial gains over baselines at equivalent inference cost, enabling MoE students to match or surpass dense teachers while demonstrating stable routing behavior and effective knowledge transfer.
mixture-of-expertsknowledge distillationrouting policyadaptive distillationload balancing
Near-Exponential Convergence Rates for kNN Classification based on Boltzmann Margin
The paper introduces Boltzmann margin, a novel condition bridging the gap between Tsybakov margin (weak, polynomial rates) and Massart margin (strong, exponential rates). Applying this framework to k-nearest neighbors (kNN) classification, the authors establish the first near-exponential convergence rates for kNN classifiers. Theoretical analysis demonstrates Boltzmann margin's intermediate strength and its ability to imply properties of both existing margin conditions, supported by numerical validation.
boltzmann marginknn classificationconvergence ratestsybakov marginmassart margin
Magnetic HIP-NN for spin dynamics in disordered itinerant magnets
The authors introduce magnetic Hierarchically Interacting Particle Neural Network (mHIP-NN), a rotationally invariant extension of HIP-NN for simulating electron-mediated spin dynamics in disordered itinerant magnets. The method incorporates spin correlations into hierarchical message-passing layers, learning emergent magnetic energy landscapes and effective local fields while preserving spin-rotation symmetry. Applied to disordered s-d exchange models, mHIP-NN accurately reproduces local torques in Landau-Lifshitz-Gilbert dynamics and captures nonequilibrium evolution of spatial spin correlations post-thermal quenches, demonstrating scalability for frustrated itinerant spin systems and coupled atom-spin dynamics.
hierarchical message-passingspin dynamicsrotationally invariantitinerant magnetslandau-lifshitz-gilbert
Beyond Explaining Predictions: Logic-Based Explanations for Confidence in Machine Learning Models
The paper introduces confidence-aware abductive explanations for machine learning models, addressing limitations of traditional logic-based methods that ignore predictive confidence. The proposed Minimum Confidence Threshold (MCT) quantifies confidence guarantees in explanations, formulated as an optimization problem. An algorithm generates minimal explanations satisfying user-specified confidence thresholds. Evaluated on boosted trees for binary classification, results show traditional explanations often provide weaker confidence guarantees than instance-level confidence, while confidence-aware explanations improve guarantees with modest increases in explanation length.
abductive explanationsminimum confidence thresholdboosted treesbinary classificationconfidence guarantees
Privacy-Preserving Credit Risk Prediction with Alternative Data
The paper introduces PrivacyCredit, a privacy-preserving machine learning method for credit risk prediction using alternative data (e.g., mobile communication records) while addressing three constraints: consumer privacy protection, centralized model storage at financial institutions, and lossless predictive performance. The method theoretically guarantees privacy preservation, model confidentiality, and performance parity with insecure plaintext data. Experiments on a real-world credit dataset demonstrate PrivacyCredit matches the accuracy of non-private baselines while maintaining computational efficiency and model confidentiality.
privacy-preserving machine learningcredit risk predictionalternative datamodel confidentialitylossless constraint
The Order Matters: Sequential Fine-Tuning of LLaMA for Coherent Automated Essay Scoring
Sequential fine-tuning of LLaMA-3.1-8B using LoRA with 4-bit quantization improves Automated Essay Scoring (AES) by modeling task dependencies in discourse elements. Three curricula were compared: Sequential (progressive fine-tuning on lead, position, claim, evidence, conclusion), Independent (task-specific models), and Randomized (shuffled multi-task). On the PERSUADE~2.0 corpus, Sequential fine-tuning achieved the strongest results, with F1 scores of 65% (evidence) and 87% (conclusion), surpassing Independent training and outperforming a LLaMA-70B baseline on conclusion. Randomized training improved position scoring (57% F1) but was less consistent. Findings highlight the importance of curriculum design aligned with discourse structure and the competitiveness of task-optimized models with larger LLMs.
automated essay scoringllamalorafine-tuningdiscourse elements
Rank Collapse, Fixed Points, and the Renormalization Group Structure of MLP Residual Networks
This work provides the first quantitative evidence that MLP residual networks implement a selective coarse-graining procedure analogous to renormalization group (RG) flows. The authors analyze pure MLP residual stacks trained on masked token prediction over synthetic Markov chain sequences with known spectral properties. Key findings include: (i) monotonic effective rank reduction with depth, (ii) selective rank collapse dependent on correlation length (present for short ≈1, absent for long ≈7), and (iii) inter-layer kernel drift concentrated at specific transitions with fixed-point plateaus elsewhere. These results demonstrate position-level RG-like behavior governed by input spectral structure.
renormalization groupmlp residual networksrank collapsefixed-point plateauspectral properties
$k$-Nearest Neighbors in Gromov--Wasserstein Space
The authors implement $k$-nearest neighbors ($k$-NN) classification using Gromov--Wasserstein (GW) and fused Gromov--Wasserstein (fGW) distances, proving universal consistency for GW-$k$-NN on equivalence classes of metric measure spaces with finite support and uniform probability measure, and for fGW-$k$-NN on weak isomorphism classes of structured objects with feature maps into Euclidean space. By modeling graphs as finitely supported metric measure spaces, they establish universal consistency for graph classification. Empirical results demonstrate that GW-$k$-NN and fGW-$k$-NN perform robustly across multiple graph datasets, validating the efficacy of metric classifiers in the GW framework.
gromov--wassersteink-nearest neighborsmetric measure spacesuniversal consistencynode-attributed graphs
When Metrics Disagree: A Meta-Analysis of Knowledge-Graph-Completion Model Benchmarking
The paper addresses inconsistent evaluation in Knowledge Graph Completion (KGC) by reframing it as a Multi-Criteria Decision-Making (MCDM) problem. It conducts a meta-analysis of seven aggregators across five tests—consistency, cross-dataset stability, metric independence, robustness under noise, and generalizability—using leave-one-model-out (LOMO) and leave-one-group-out (LOGO) removals. Results show Z-score as the most balanced aggregator, ranking DualE highest for tail prediction and FMS (Flow-Modulated Scoring) highest for relation prediction. The framework resolves evaluation inconsistencies and provides evidence-based guidance for aggregator selection.
knowledge graph completionmulti-criteria decision-makingz-scoreleave-one-model-outflow-modulated scoring
Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passing
The paper revisits the role of positive samples in Graph Contrastive Learning (GCL), demonstrating that competitive performance can be achieved even without them due to message passing trivializing their maximization. Through Dirichlet energy analysis, the authors propose SPGCL, which propagates only high Dirichlet energy features for effective learning signals and uses low-energy features for reliable positive sampling. Experiments confirm SPGCL's efficacy in restoring positive sample utility.
graph contrastive learningdirichlet energymessage passingpositive samplinggraph encoders
A Unified Adaptive Feature Composition Framework for Multi-Task Generalization in Wireless Foundation Models
The paper proposes a unified adaptive feature composition framework (RAFC) for multitask generalization in wireless foundation models (WFMs). RAFC dynamically combines hierarchical features from different Transformer layers via task-specific routing weights, avoiding full fine-tuning while maintaining performance. The lightweight feature composition network introduces <50K parameters and demonstrates superior performance across four wireless tasks compared to conventional adaptation methods. Learned routing weights provide interpretable insights into layer preferences for different tasks.
wireless foundation modelsfeature compositionrouting adaptermultitask generalizationtransformer layers
POPSICLE: Benchmark Datasets for Segmentation and Localization in CryoET
POPSICLE introduces a benchmark suite for cryo-electron tomography (cryoET) segmentation and macromolecular localization, addressing the lack of standardized ML evaluation in this domain. Built on the CryoET Data Portal, it provides an open, extensible repository of tomographic data, metadata, and annotations spanning eukaryotic/prokaryotic systems, purified/in situ samples, and dense/sparse tasks. Baseline experiments demonstrate significant variation in model rankings across tasks, highlighting the need for cryoET-specific benchmarks rather than adaptations from adjacent biomedical imaging fields. POPSICLE enables reproducible ML evaluation and can expand as new datasets become available.
cryo-electron tomographysegmentationmacromolecular localizationbenchmark suitetomographic data
When Design Rules Break: Benchmark Composition Determines Whether Label Informativeness Predicts GNN Aggregator Choice
The study investigates the generalizability of GNN design rules across benchmark families by analyzing aggregator selection (sum, mean, max) on 24 node-classification datasets. While label informativeness predicts the GIN-Sum versus GIN-Mean performance gap on legacy benchmarks, this relationship degrades when Facebook-100 graphs are included, where sum aggregation yields 7-13% gains despite near-zero label informativeness. Stochastic block model ablations fail to replicate this behavior, with spectral gap identified as a distinguishing feature. Results indicate benchmark composition critically influences design rule generalizability, with Facebook-100 graphs presenting a unique regime for adaptive aggregation methods.
graph neural networksaggregator selectionlabel informativenessspectral gapbenchmark composition
DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction
The paper proposes DUET (Dual User Embedding Transformers), a framework for offsite conversion rate (OCVR) prediction that addresses signal heterogeneity through dual-stream modeling. It partitions user behavior into click and conversion streams, pre-training dedicated transformer encoders with architecture variants tailored to each stream's statistical properties: multi-layer self-attention for dense clicks and interleaved cross-/self-attention for sparse conversions. The complementary embeddings are jointly consumed by a downstream ranker within latency constraints. Evaluations show 0.38% normalized entropy reduction versus baselines and improved OCVR prediction accuracy in A/B tests.
ocvr predictiondual-stream modelinguser embeddingtransformer encodersnormalized entropy
ANCHOR: Autoregressive Non-intrusive Chunk-Ordered Refinement for Joint Multi-Resolution Speech Quality Modeling
ANCHOR introduces an autoregressive non-intrusive framework for joint multi-resolution speech quality modeling, addressing the challenge of incremental estimation from partial audio in streaming systems. The method employs dual-resolution tokens and a resolution-aware hierarchy within a single decoder to refine quality predictions from chunk- to utterance-level. Experiments demonstrate 48% PLCMOS error reduction on 2-second prefixes, with convergence analysis revealing a 4-6 s perceptual context horizon and improved robustness under localized corruption.
autoregressive modelingmulti-resolutionspeech quality assessmentincremental predictionperceptual context
What Demonstration Curation Metrics Do to Your Policy
The study reveals a decoupling between demonstration-curation metrics' defect-detection performance and their impact on downstream behavior-cloning policies, using a LIBERO pick-and-place benchmark with controlled structural defects. While one metric achieves the highest AUROC (0.804) for defect detection, it yields the worst policy performance (13.3% success), whereas a metric with lower AUROC (0.638) nearly matches oracle performance (90.0% vs. 93.3%). Five of seven metrics exploit episode length as a confound, inflating AUROCs until controlled. The best curation methods close the performance gap to within 3 percentage points of the oracle. The findings advocate for policy-based evaluation of curation methods and controlling for episode length in benchmarks.
demonstration-curationbehavior-cloningaurocepisode lengthoracle performance
Spatiotemporal Graph Transformer for 3D Neighborhood Interaction and Quality Prediction in Metal Additive Manufacturing
The paper introduces a spatiotemporal graph transformer for quality prediction in metal additive manufacturing, addressing challenges in modeling multi-layer interactions. The method constructs a weighted network representation of manufacturing processes, integrating multimodal data (geometric design, process settings, in-situ sensing) into a unified structure. A dual-attention graph transformer captures feature dependencies and neighborhood interactions. Experiments demonstrate superior performance over image-, sequence-, and graph-based models, with cross-layer interactions proving critical for quality prediction accuracy.
spatiotemporalgraph transformeradditive manufacturingdual-attentionquality prediction
Alignment Defends LLMs from Property Inference Attacks
The paper proposes alignment-based defenses against property inference attacks in large language models (LLMs), which extract sensitive dataset-level properties. The method adapts Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) frameworks to reshape the model's output distribution via post-training alignment, without modifying training data. Experiments demonstrate effective mitigation of property inference attacks while preserving utility-confidentiality tradeoffs.
property inference attackslarge language modelsdirect preference optimizationgroup relative policy optimizationpost-training alignment
A Continuous-Time Markov Chain Framework for Insertion Language Models
The paper introduces a continuous-time Markov chain framework for Insertion Language Models (ILMs), deriving a diffusion-style denoising objective from first principles. It formulates the noising process as a Markov chain on variable-length sequences, showing that previous ILM formulations are special cases of this framework. Empirical evaluation on a synthetic planning task demonstrates that the approach retains ILM advantages over left-to-right generation and masked diffusion models, while offering competitive performance in language modeling and greater sampling flexibility.
insertion language modelscontinuous-time markov chaindiffusion-style denoisingvariable-length sequencessynthetic planning task
Decision-Calibrated Conformal Uncertainty for Pacing Decisions in Streaming Advertising
The paper introduces a decision-calibrated conformal framework for pacing decisions in streaming advertising, optimizing for uncertain future inventory, demand pressure, and member-experience load. The method measures forecast error by its impact on deployable policies, proving it is the smallest valid uncertainty measure via a geometric interpretation as the support function of policy sensitivity sets. Empirical results on Criteo Uplift and KuaiRand datasets show uncertainty radii reductions from 7236.7 to 18.4 (Criteo) and 4629.4 to 278.6 (KuaiRand), with Criteo achieving a 3.3% violation rate versus 16.7% baseline.
conformal predictionpacing decisionsuncertainty calibrationstreaming advertisingpolicy sensitivity
Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization
The work analyzes trainability of Instantaneous Quantum Polynomial (IQP) Quantum Circuit Born Machines under Gaussian initialization, addressing gradient concentration and barren plateaus. Using Stein's lemma and Lipschitz concentration bounds, it derives analytical lower bounds for gradient variance and probabilistic bounds for gradient deviation. Results identify conditions favoring exponential concentration and suggest strategies to mitigate or exploit this phenomenon, providing theoretical insights for quantum machine learning optimization.
quantum circuit born machinesiqp circuitsgaussian initializationbarren plateausgradient concentration
Learning Entropy and Spatial Adaptation Dynamics of Multilayer Perceptrons for Structural Point Extraction
The paper introduces Spatial Learning Entropy Maps (SLEM) by extending Learning Entropy (LE) to spatial adaptation dynamics in MLPs for image analysis. Instead of traditional gradient-based methods, SLEM quantifies the learning impact of spatial regions through weight adaptation during training, identifying informative points that drive network learning. Results demonstrate SLEM's complementary role to conventional feature extraction by highlighting regions with high learning influence, offering potential applications in computer vision and robotics.
learning entropymultilayer perceptronspatial adaptationfeature extractionexplainability
Quality Is Not a Safety Proxy Under Quantization
The paper demonstrates that quality metrics fail as safety proxies for quantized models, auditing 51 configurations across 6 models and 4 families with GGUF/AWQ/GPTQ quantization. Experiments reveal 10 dangerous cases where quality remains stable while safety (measured by refusal rates) drops 12-68 percentage points, with 7/11 AWQ/GPTQ cases affected. Mechanistic analysis shows safety-associated neurons absorb 1.39× more quantization error (p<5×10⁻⁷), but without regime specificity. The proposed Refusal Template Stability Index (RTSI) identifies 10/10 high-risk cases, outperforming single-feature baselines (9/10 and 8/10 recall). Findings mandate direct safety testing for quantized checkpoints.
quantizationsafety evaluationrefusal ratesmodel auditingneuron analysis
Compositional Generative Modeling from Decentralized Data
The paper introduces Decentralized Compositional Flow Matching (DCFM), a framework for generative modeling that captures compositional structure from decentralized data without raw data exchange. DCFM enforces structural constraints across distributed generative factors, enabling novel combinations through peer interactions even when individual silos lack sufficient data. Evaluations on conditional image generation, robotic spatial planning, and medical attribute co-occurrence modeling demonstrate DCFM's superiority over federated learning and mixture-of-experts baselines.
decentralized learninggenerative modelingflow matchingcompositional structurefederated learning
Ambiguous Strategic Classification
The paper introduces ambiguous strategic classification, where a system discloses partial classifier information due to regulatory constraints, unlike traditional full-disclosure assumptions. The authors propose jointly optimizing the classifier and its uncertainty by leveraging ambiguity from robust mechanism design, allowing revelation of a set of possible classifiers while privately selecting the realized one. They analyze ambiguity's impact on learning, develop efficient algorithms for best-responses and training, and empirically evaluate strategic learning outcomes in this novel setting.
strategic classificationambiguityrobust mechanism designbest-response computationregulatory constraints
Effective Training Principles of Physical Reservoirs
This work analyzes training optimization strategies for physical reservoir computers to mitigate overfitting and computational inefficiency. The authors compare loss-minimizing search methods (Equal Search, Branch and Bound) with statistical filtering (Variance Filter) and random pruning, demonstrating that informed output sampling improves performance, particularly for non-iterative methods. L1/L2 regularization (LASSO, ridge regression) significantly enhances performance on nonlinear tasks like the Spiral Benchmark. Results are validated on a fiber-optical extreme learning machine, providing insights into hidden-layer filtering and output-layer training for optimized reservoir computing systems.
reservoir computingoverfitting mitigationoutput pruningregularization techniquesnonlinear dynamics
Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning
The study introduces a deep reinforcement learning (deep-RL) framework for discovering interpretable multi-parameter control policies in evolutionary algorithms, addressing a gap in theoretical analysis. Using the (1+(λ,λ))-genetic algorithm on OneMax as a case study, the authors propose algorithm-agnostic enhancements (action-space decomposition, reward shifting, long-horizon discounting) to improve convergence. Double Deep Q-Networks outperformed Proximal Policy Optimization by avoiding policy collapse, and the learned behaviors were distilled into symbolic policies. These policies achieved superior performance across problem sizes while remaining interpretable for theoretical analysis.
deep reinforcement learningevolutionary algorithmsparameter controlinterpretable policiesdouble deep q-networks
Robust Active Learning for Few-Shot Example Selection in Text-to-SQL
The paper proposes a stratified greedy algorithm for robust active learning in few-shot example selection for text-to-SQL systems, addressing challenges of heteroscedasticity, partition matroid constraints, and kernel misspecification. The method maximizes a heteroscedastic mutual information objective, proven to remain submodular and approximately monotonic on the intrinsic manifold, with theoretical approximation guarantees. Empirical results show significant reduction in labeling effort while maintaining high retrieval accuracy.
active learningheteroscedasticitypartition matroidsubmodular optimizationtext-to-sql
Convergence Rates for Neural-Network Estimation with Current-Status Data
The paper introduces a nonparametric neural-network sieve maximum likelihood estimator for conditional cumulative distribution functions in current-status data scenarios, where event times are only known relative to examination times. By leveraging approximation theory for rectified linear unit networks and empirical-process arguments, the authors derive explicit convergence rates under Hölder smoothness conditions. This work provides theoretical justification for neural-network-based estimation and inference in current-status data analysis.
current-status dataneural-network sievehölder smoothnessconvergence ratesmaximum likelihood estimator
Nonlinear Estimator: Dual Bayesian Affine Estimators for Parameter Learning
The paper introduces a nonlinear parameter estimator for Wiener-type state-space models, formulated as a fixed-point architecture coupling two affine MMSE estimators for parameters and latent variables. The method incorporates Dynamic Basis Statistics (DBS) estimates via two strategies: dual basis-parameter and dual state-parameter estimators, which alternate between component estimates using updated priors. Monte Carlo experiments demonstrate that the dual state-parameter estimator achieves the lowest parameter mean-squared error, outperforming affine estimators and sequential Monte Carlo variants like Particle Gibbs and Expectation-Maximization.
wiener-type state-space modelsdynamic basis statisticsaffine mmse estimatorsfixed-point architectureparameter mean-squared error
Unsupervised Style Representation Learning for AI-Text Detection via Paraphrase Inversion
The paper introduces an unsupervised method for learning style representations to detect AI-generated text, addressing limitations of current style-based detectors that require authorship labels. By training a style encoder to reconstruct human-authored text from machine-generated paraphrases and freezing a semantic encoder, the approach captures non-semantic style features. Evaluated on few-shot and zero-shot detection tasks, the method matches or outperforms baselines, generalizes to unseen LLMs, and shows competitive performance on authorship verification and style discrimination without task-specific training.
unsupervised learningstyle representationai-text detectionparaphrase inversiondeepsvdd
Decision-Making under Combinatorial Risk
The paper introduces an investment-allocation task to study decision-making under combinatorial risk, where outcomes depend on multiple risky components with endogenously shaped distributions. Using behavioral experiments and symbolic regression, the authors find participants rely on heuristic features like probability increments rather than full distribution evaluation. Revealing the induced PMF reduces choice variance and shifts behavior toward lottery valuation. Discovered models emphasize combinatorial-risk features (e.g., post-investment success probability), with prospect-theoretic residuals explaining behavior under displayed PMFs.
combinatorial risksymbolic regressionprobability mass functionprospect theoryheuristic decision-making
SoK: Colluding Adversaries in Machine Learning Pipelines
We propose a systematic framework for analyzing collusion among adversaries in machine learning pipelines, addressing train-time and inference-time attacks. The framework identifies enabling factors for collusion and provides guidelines to conjecture potential adversarial collaborations. We validate five previously unexplored collusion cases empirically and analyze how adversary characteristics (objectives, knowledge, capabilities) influence collusion potential. This work bridges gaps in understanding multi-stage adversarial threats and offers a structured approach to studying collusive attack scenarios in ML systems.
adversarial collusiontrain-time attacksinference-time attacksenabling factorsadversary characteristics
A Theory on Flow Matching with Neural Networks
The paper establishes theoretical foundations for flow matching using neural-network-parameterized conditional velocity fields. It provides convergence guarantees for gradient descent in over-parameterized 2-layer ReLU networks and derives generalization bounds for the conditional velocity-field matching objective. The analysis includes Wasserstein-distance guarantees for samples generated by the induced flow, leveraging multi-task representation learning with unbounded losses. Theoretical results are validated through experiments on synthetic and real-world image benchmarks.
flow matchingconditional velocity fieldswasserstein-distancegeneralization boundsover-parameterized networks
CodeAlchemy: Synthetic Code Rewriting at Scale
CodeAlchemy introduces a synthetic data generation framework that transforms public code into semantically-rich training data through five strategies: CodeEnhance, CodeQA, CodeDev, CodeDialogue, and CodeTrace. The method processes 3 corpora across 15 languages to generate 500B+ tokens of synthetic data and 350B reasoning tokens, with CodeTrace executing 1.3M+ files across 14 languages and 5K libraries. Results show 3B models achieve 83.5% on HumanEval and outperform frontier models 10x their size, while new benchmarks (DevEval, TraceEval) reveal gaps in semantic understanding (Claude Sonnet 4.5: 5.6% exact match on TraceEval).
synthetic datacode rewritingexecution tracesmultilingual corporasemantic understanding
Structured Adaptive Tensor Prediction for Streaming Data
The authors propose an adaptive tensor regression framework for streaming matrix-valued time series prediction, introducing Matrix-on-Matrix (MoM) and Tensor-on-Matrix (ToM) formulations. The method employs stochastic gradient descent (SGD) for online learning, demonstrating that ToM's higher-order tensor representation of temporal structure yields lower steady-state error and better denoising than MoM. Theoretical analysis provides fixed-time recovery guarantees for ToM under low-dimensional structures (sparsity, low-rankness, and their combinations), with explicit characterization of SGD's tracking behavior in time-varying environments.
tensor regressionmatrix-valued time seriesonline learningstochastic gradient descentlow-dimensional structures
Divide-and-Conquer Modeling for the CTF-4-Science Lorenz Benchmark
The study introduces a divide-and-conquer approach for the CTF-4-Science Lorenz benchmark, addressing chaotic-system prediction across twelve hidden scores and five scenario families. Methodologically, it employs task-specific models: smoothing-based reconstruction for noisy trajectories, NG-RC/NVAR models for long-time forecasting, a Lorenz transition correction for clean short-time prefixes, and parametric blending for interpolation. The system achieves a 79.63 public score, demonstrating that scenario-specific modeling outperforms monolithic approaches in mixed chaotic forecasting tasks.
lorenz benchmarkdivide-and-conquerchaotic-system predictionng-rc/nvar modelsparametric blending
VFUSE: Virulent Feature Understanding with Sparse autoEncoders
VFUSE introduces sparse autoencoders (SAEs) for mechanistic interpretability of protein design models, specifically targeting virulent feature detection in diffusion-transformers. The method trains SAEs on RoseTTAFold3 and RFDiffusion3 activations, enabling linear probes to identify hazardous designs more effectively in SAE latent space than in original representations (AUROC up to 0.84, q < 10^-13) while preserving model performance. This work presents the first feature-level virulence audit of protein design models and the first SAE application to all-atom diffusion models, advancing safe and interpretable protein generation.
sparse autoencodersmechanistic interpretabilityprotein designdiffusion transformersvirulence audit
Temporal Sheaf Neural Networks with Dynamic Orthogonal Transport
The paper introduces Temporal Sheaf Neural Networks (TSNN), a novel framework for temporal link prediction that employs dynamic orthogonal frames per node, enabling explicit transport between local coordinate systems. Unlike conventional continuous-time graph models using global embedding spaces, TSNN captures node-specific, evolving interaction semantics through local frames, parameterized via low-rank Householder products. The model ensures exact preservation of hidden states under frame updates and utilizes a geometric-residual decoder for predictions. Theoretical analysis shows TSNN's sheaf Laplacian properties and guarantees non-expansiveness. Empirical results on TGB v2, DGB benchmarks, and heterogeneous graphs demonstrate superior performance, with ablations validating the efficacy of dynamic frames, orthogonal transport, and residual decoding.
temporal sheaf neural networksorthogonal transportdynamic framesgeometric-residual decodersheaf laplacian
Spatiotemporal Seismic Hazard Assessment Using VQ-VAE and Seismic Statistical Features
The study enhances seismic hazard prediction by combining 60 seismic statistical features (SSFs) with a novel VQ-VAE-derived spatial feature, improving upon prior XGBoost-based whole-region assessments. Using Japan's earthquake catalog, it demonstrates localized prediction (24 km radius) maintains performance (test AUC comparable to whole-region) while the VQ-VAE feature, top-ranked by SHAP analysis, supplements SSFs and nearly replaces traditional $b$-value computation. The hybrid approach leverages 1D catalog data and 2D seismic maps through VQ-VAE reconstruction error as a stress indicator.
seismic statistical featuresvq-vaeshap analysislocalized predictionearthquake catalog
Importance-Aware Scheduling for High-Dimensional Hyperparameter Optimization
The paper introduces Greedy Importance First (GIF), a hyperparameter optimization (HPO) method that addresses inefficiencies in high-dimensional spaces. GIF employs importance-aware scheduling through small-sample warm starts to estimate hyperparameter importance, forms importance-based groups, allocates trials proportionally, and includes a full-space fallback. Evaluated on five anisotropic functions, Bayesmark, and NAS-Bench-301, GIF outperforms TPE, BOHB, Random Search, and Sequential Grouping in high-dimensional settings, achieving faster convergence and better incumbents. Ablation studies confirm the contributions of importance estimation, proportional allocation, and the fallback mechanism. The method proves particularly effective in scenarios with high effective dimensionality.
hyperparameter optimizationimportance-aware schedulinganisotropic functionssample efficiencyhigh-dimensional spaces
A Controlled Audit of Pretraining Contamination in Public Medical Vision-Language Benchmarks
The study conducts a controlled audit of pretraining contamination in medical vision-language benchmarks (SLAKE-En, PathVQA, VQA-RAD, OmniMedVQA) using four detector families: image-side near-neighbour overlap, canonical-order exchangeability, cohort-relative Min-K%++ tail enrichment, and cross-model top-K overlap. Results reveal measurable image-side source overlap in SLAKE-En (19.8% flagged under SigLIP-B-16, 4.2% under SigLIP-SO400M), while text-side analysis shows canonical-order exchangeability signals in Qwen2.5-VL and five other VLMs on OmniMedVQA. Cohort-relative detectors prove unreliable, as BLIP-2 reproduces false positives without medical-VQA exposure.
pretraining contaminationvision-language modelscanonical-order exchangeabilitymembership-inferencemedical-vqa
Bittensor Agent Arenas as a Trajectory Primitive: Distilling a Shopping Agent from ShoppingBench Subnet Traces
The paper introduces Bittensor Agent Arenas as a trajectory primitive for small-model agentic post-training, addressing the bottleneck of trajectory substrate quality. The authors engineer ORO Subnet 15 (SN15), a Bittensor deployment of ShoppingBench, to generate incentive-aligned, diverse trajectories with per-trajectory judging and anti-memorized evaluation. A structural-quality filter processes raw trajectories into a trainable corpus, enabling Qwen3-4B to achieve 42.7% ASR (vs. 18.0% base) on held-out data, nearing the synthetic-data SFT-only baseline (43.6%). The method identifies sub-task firehose as key to closing the gap to SFT+GRPO (48.7%).
bittensoragentic post-trainingtrajectory primitivestructural-quality filtershoppingbench
Interpreting and Steering a Text-to-Speech Language Model with Sparse Autoencoders
The study trains BatchTopK sparse autoencoders (SAEs) on CosyVoice3's language model backbone to interpret and control text-to-speech representations. A modality-aware auto-interp pipeline identifies features tied to text prefixes, speech clips, or both, revealing interpretable patterns like phonemes, laughter, and speaker gender. Steering experiments demonstrate causality: interventions increase laughter probability from 0.02 to 0.79, alter perceived gender, and modulate speech rate without content loss. SAEs thus enable both interpretation and precise control in TTS systems.
sparse autoencoderscosyvoice3modality-awaretext-to-speechsteering
GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation
GHOST introduces a hierarchical framework for visuomotor manipulation policies that generalize beyond training distributions. The method decomposes control into a high-level policy predicting 3D end-effector pose sub-goals from multi-view RGB-D observations, and a low-level goal-conditioned controller executing embodiment-specific actions. A spatial interface projects 3D goals into image-plane heatmaps for conditioning. Experiments show improved performance and robustness over flat Diffusion Policy baselines across manipulation tasks, with human demonstration integration avoiding noisy action retargeting. The embodiment-agnostic sub-goals enable skill transfer from human video to robot policies.
visuomotor policieshierarchical controlsub-goal predictionend-effector heatmapsembodiment-agnostic
Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
The study evaluates the reliability of neural generative models for high-dimensional posterior estimation in cosmological inference, comparing implicit (Stochastic Interpolants) and explicit (GLOW normalizing flows) approaches against Hamiltonian Monte Carlo references. Using field-level analysis of cosmic initial conditions reconstruction, it reveals that standard metrics (posterior means, marginals, cross-correlation) fail to detect inaccuracies in uncertainty structure, demonstrated through variance fields and sample-based validation. The work underscores the need for rigorous validation in scientific applications of generative models.
posterior estimationgenerative modelshamiltonian monte carlocosmological inferencenormalizing flows
Spiking Neural Network inference on FPGAs with hls4ml
The paper extends hls4ml to enable FPGA deployment of Spiking Neural Networks (SNNs) trained in PyTorch, targeting clock-driven inference. The method leverages high-level synthesis (HLS) workflows for synchronous FPGA implementation, validated through software comparisons, HLS simulation, and Vivado synthesis. A quantized SNN trained on the Heidelberg Spiking Digits dataset achieves 34μs inference latency, demonstrating real-time capability. The integration bridges neuromorphic computing with conventional FPGA toolchains.
spiking neural networksfpgahls4mlhigh-level synthesisneuromorphic computing
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