Daily Digest — 2026-07-31
253 items · 3 research labs, 243 arxiv papers, 7 industry media
🏛️ Research Labs (3)
Advancing the price-performance frontier with GPT-5.6
OpenAI introduces significant price-performance improvements for GPT-5.6 variants, reducing costs by 80% for Luna (high-volume workload model) and 20% for Terra (balanced model) while maintaining quality. Architectural optimizations include enhanced token-generation efficiency (+15%), kernel optimizations (-20% serving costs), and agentic workflow improvements. The API now offers Fast mode for GPT-5.6 Sol, delivering 2.5× speedups at 2× cost. Evaluations show Luna matches predecessor performance at 6% cost/task and outperforms Fable 5 at 1% cost/task on professional benchmarks. Pricing updates include $0.20/M input tokens for Luna and backward-compatible Fast mode integration.
token-generation efficiencyagentic workflowkernel optimizationprice-performance frontierinference systems
How enabling two settings tripled our scores on the ARC-AGI-3 benchmark
Enabling retained reasoning and compaction in the ARC-AGI-3 benchmark harness tripled GPT-5.6 Sol's performance and reduced output tokens by 6x. The study identified that the generic harness discarded private reasoning and employed rolling truncation, hindering the model's ability to learn and reason coherently. By adopting settings used in ChatGPT and Codex—retaining reasoning messages and replacing rolling truncation with compaction—the model achieved higher scores with fewer tokens. These findings highlight the impact of API settings and harness design on benchmark performance.
retained reasoningcompactionarc-agi-3harness designoutput tokens
GPU Management: Why Idle GPUs Are the New Grounded Aircraft
The article identifies GPU utilization as the emerging bottleneck in enterprise AI, drawing parallels to aircraft fleet management. It argues that while GPU procurement addresses capacity, efficient orchestration of workloads (training, inference, quantization) across heterogeneous GPU clusters determines ROI. The authors propose GPU Management as a distinct discipline for real-time allocation decisions, emphasizing that specialized models free capacity but require active orchestration to maximize utilization. The analysis highlights the shift from model-centric intelligence to infrastructure-level optimization as the key constraint in production AI systems.
gpu utilizationorchestration layerspecialized modelsreal-time allocationinfrastructure optimization
📜 arXiv Papers (243)
Can AI agents conduct open-ended AI research? Early evidence from two case studies
This paper introduces shadow evaluations, a novel method to assess AI agents' capability to conduct open-ended AI research by having them address core research questions from high-quality unpublished papers, with evaluations performed by the original authors. Two frontier AI agents were tested on unpublished NeurIPS 2026 submissions, given six days and substantial compute resources. While the agents completed all engineering tasks autonomously, they failed to make significant progress on the research questions, leading to unambiguous rejections by the authors. Five recurring failure modes were identified: poor judgment on publishable research standards, uncreative responses to design flaws, ineffective backtracking, poor resource awareness, and instruction drift. Robustness checks confirmed these findings.
shadow evaluationsopen-ended researchfailure modesai agentsneural networks
APEX-Accounting
APEX-Accounting introduces a benchmark to evaluate frontier models on accounting tasks, including reconciling accounts, accruing expenses, posting transactions, and producing reports. Developed by Mercor and Ramp, the private evaluation set comprises 160 tasks across 10 worlds, each containing accounting systems, spreadsheets, and PDFs. Tasks were authored and graded by accounting experts. Among nine frontier models, Claude-Fable-5 (Max) achieves the highest Mean Criteria@3 score of 56.4%, while Muse-Spark-1.1 (xHigh) leads in Pass@8 at 21.5%. Experiments reveal Simpson's paradox: increasing token budgets improves scores, but within constrained budgets, higher token usage correlates with lower task performance.
benchmarkaccounting taskstoken budgetsimpson's paradoxmean criteria@3
The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making
The study investigates how AI teammates reshape human-human communication in small-team decision-making, using Group Communication Analysis (GCA), surveys, and lexical analysis. Sixteen teams with an AI teammate and seventeen all-human teams completed a moral-dilemma task. Results show the AI was the most talkative yet least informative member, reducing human responsivity, social impact, and perceived belonging. AI dominance correlated with lower perceived team value, with effects immediate and persistent. The findings highlight social costs of AI teammates, prompting a research agenda for voice-based and longitudinal settings.
group communication analysisai teammatemoral-dilemma tasksocial impactlexical analysis
Partner Capability Estimation for Task-Agnostic Adaptation in Ad-Hoc Teamwork
The authors introduce CE-CM (Capability Estimation via Contextual Models), an approximate Bayesian method for task-agnostic ad-hoc teamwork that infers task-invariant capability vectors from simulation-based sampling. CE-CM induces a contextual Multi-agent Markov Decision Process for planning without population pre-training, refining beliefs online from few tasks. Its extension, CE-CM-Div, evaluates capability hypotheses against diverse planner rollouts to account for human unpredictability. Simulated experiments show CE-CM rapidly recovers hidden capabilities, reduces infeasible action assignments, and adapts over time. In an offline human study with 225 trajectories from 15 participants, CE-CM-Div outperformed CE-CM in capability estimation, demonstrating the importance of behavioral diversity for robust human-AI teaming.
capability estimationad-hoc teamworkmulti-agent markov decision processtask-agnostic adaptationbehavioral diversity
Improving Item Discoverability in e-Commerce Search via Related Intent Generation
We present a scalable system for discovery-augmented e-commerce search that improves item discoverability via intent-conditioned recall expansion. The hybrid architecture combines closed-weight large language models (LLMs) for head queries with a finetuned small language model (SLM) for tail queries, trained via LoRA adapters and teacher-student distillation. Evaluation employs LLM-as-a-judge metrics and session-level purchase analysis, demonstrating improved intent generation quality and retrieval effectiveness. The system extends discovery coverage from 60% to 80% of query traffic at 30% of the teacher model's inference cost, enabling query-conditioned exposure for long-tail and emerging supply.
discovery-augmented searchintent-conditioned recalllora adaptersteacher-student distillationllm-as-a-judge
OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
The paper introduces OmegaUse-OfficeVal, a benchmark for evaluating LLM agents on 100 long-horizon office-suite tasks (avg. 2.32 human labor hours) with economic grounding via human labor time and task price proxies. It develops code-based verifiers from fine-grained rubrics for stable evaluation. Evaluations of frontier LLMs show they are cheaper and faster than humans but lag in deliverable quality. The benchmark and code are open-sourced.
llm agentsbenchmarkingeconomic groundingoffice-suite taskslong-horizon
Anatomy Contextualized Adaption of CT Foundation Models
Anatomy Contextualized Adaptation (ACA) adapts frozen CT foundation models for anatomy-level vision-language alignment while preserving global context. ACA decomposes CT volumes into anatomy-level embeddings using TotalSegmentator, refines them via a cross-anatomy transformer, and aligns them to per-anatomy and scan-level text from radiology reports. On Merlin and CT-RATE, ACA outperforms frozen foundation models and fine-grained methods in zero-shot finding classification, with <1 hour training post-embedding caching, and exhibits plausible cross-anatomy context routing via attention weights.
ct foundation modelsvision-language alignmentzero-shot classificationtotalsegmentatorcross-anatomy transformer
Cost-Sensitive Conformal Prediction and Human-in-the-Loop Abstention for Imbalanced High-Stakes Decision Support: A Multi-Domain Benchmark
The study addresses unreliable minority-class coverage in standard marginal conformal prediction (CP) for high-stakes imbalanced decision systems by benchmarking marginal CP, class-conditional (Mondrian) CP, and cost-controlled abstention across 15 tabular datasets, 7 classifiers, and 3 calibration techniques (3,150 runs). Mondrian CP improves minority-class coverage by 61.7 percentage points (p < 1e-80) over marginal CP, while cost-controlled abstention reduces expected decision costs under human review budgets. Break-even thresholds for human deferral are quantified, providing deployable guidance for cost-sensitive uncertainty quantification.
conformal predictionclass imbalancemondrian cpcost-sensitive abstentionuncertainty quantification
DLAM: Distributional Latent Actions with Temporal Constraints
DLAM introduces a distributional latent-action model for vision-language-action tasks, addressing limitations in existing structured methods by representing transitions as diagonal Gaussians. It employs reconstruction conditioned on reference frames, normalized composition, and reversal over equal-gap triplets to constrain mean and variance. Variance composition uses a shared-correlation coefficient for adjacent transitions, while reversal negates the mean and preserves variance. Downstream policy learning involves freezing the encoder and training a flow-matching policy for joint generation of mean transitions and robot actions. DLAM outperforms baselines in temporal consistency, reconstruction, and policy performance on MetaWorld MT50, LIBERO, and real-world manipulation tasks, with normalized mean constraints contributing most to reconstruction gains.
distributional latent-actiondiagonal gaussianflow-matching policytemporal consistencyvariance composition
Linguistic Monoculture in LLM-Assisted Language Use
The paper introduces 'linguistic monoculture' as a potential consequence of widespread LLM-assisted writing, where population-level linguistic diversity decreases due to shared model usage. It develops a mathematical framework modeling authors and LLMs as linguistic feature distributions, analyzing three interaction mechanisms: fixed shared models, recursively updated shared models, and personalized models. Results show shared models converge authors to a common norm, recursive feedback shifts norms without altering spread, while personalization preserves distinct equilibria. Strategic conformity choices create negative externalities, with unbounded 'price of monoculture' when distinctiveness outweighs authenticity.
linguistic monoculturelarge language modelscoevolution dynamicsnegative externalitystrategic conformity
AgentMap: Joint Equivalence and Subsumption Discovery for Ontology Matching
AgentMap introduces Hybrid Ontology Matching (HOM), unifying equivalence and subsumption discovery in ontology matching. The framework employs a multi-agent LLM-based approach, integrating semantic retrieval, hierarchical search, and collaborative reasoning to identify either equivalent concepts or the most fine-grained subsumers in target ontologies. Evaluated on extended OM datasets, AgentMap demonstrates promising performance in hybrid settings and outperforms baselines in equivalence-only and subsumption-only scenarios.
ontology matchinghybrid ontology matchingequivalence discoverysubsumption matchingmulti-agent llm
MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning
The paper introduces MMAC, a Massive Multi-dimensional benchmark for Audio Captioning, addressing the need for fine-grained evaluation of AudioLLMs. MMAC comprises 5,638 audio clips from over 20 sources, organized into 6 capability categories and 15 evaluation dimensions. It assesses both information coverage (mention of relevant details) and description reliability (consistency with reference labels). Evaluations of open-source and proprietary AudioLLMs reveal significant variations across dimensions. The benchmark and evaluation code will be publicly released.
audiollmsaudio captioningbenchmarkinformation coveragedescription reliability
SciFigQual-Bench: A Benchmark for Scientific Figure Quality Assessment with Full-Manuscript Context
SciFigQual-Bench introduces a benchmark for assessing scientific figure quality in full-manuscript contexts, addressing limitations of existing image quality assessment methods. The benchmark evaluates 6,308 images from top computer-science conferences (2020-2025) across five dimensions: clarity, layout, caption fit, context relevance, and misleading risk, with gold-standard annotations by domain experts. A staged cross-modal evaluation framework, SFQ-Agent, was designed for automated assessment, leveraging modal evidence fusion. SFQ-Agent (F3) with GPT-5.6-Sol achieved the lowest average absolute error (0.418) and highest consistency rate (93.4%), outperforming direct and auxiliary evaluation schemes.
scientific figure qualitycross-modal evaluationcaption alignmentmisleading riskgold-standard annotations
Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents
The paper introduces cost-aware marginal decision-focused stopping (CAM-DF), a method for optimizing tool acquisition in LLM agents by balancing relevance rankings with heterogeneous costs. CAM-DF trains on the offline gap between immediate stopping and optimal continuation, ensuring Bayes-aligned decisions and outperforming score-only rules. Evaluated on 1,343 tasks across five domains, CAM-DF achieves the highest payoff on τ-bench Retail, reducing tool exposure by 37% while maintaining task success, particularly under high cost pressure or weak rankings.
llm agentstool acquisitioncost-aware stoppingbayes-alignedoffline training
On-Policy Distillation for LLM Safety: A Routing Approach to Template-Robust Realignment
Routing-based On-Policy Distillation (ROPD) introduces a robust LLM realignment framework addressing vulnerabilities in fine-tuning, such as template-mismatch risks and catastrophic forgetting. ROPD models divergence between aligned and compromised output distributions rather than fitting specific prompt templates, mitigating template-mismatch risks. Extensive experiments on three datasets and three base models demonstrate ROPD's superior robustness in defense effectiveness and capability preservation compared to four state-of-the-art baselines. While not entirely immune to template shifts, ROPD exhibits negligible performance degradation, setting a new standard for robust LLM realignment.
fine-tuningtemplate-mismatchon-policy distillationllm realignmentcapability preservation
MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
MemSecBench introduces a task-grounded benchmark for evaluating the lifecycle security of agent memory systems, tracing malicious semantics across persistence, downstream consequences, and selective repair. The benchmark comprises 310 cases across 48 realistic contexts, employing a Write--Execute--Forget protocol in isolated runtimes with exact agent configurations. A 24-configuration matrix evaluates two agent harnesses, four memory backends, and three LLM backends. Results show malicious memory persists in 84.2% of cases, with a 50.3% success rate for the full Write--Execute chain. Selective repair is achieved in 56.1% of successfully poisoned cases, with significant variation across configurations.
memory poisoningselective repairagent harnessllm backendlifecycle security
Parameter-Free Dynamic Regret for Online Convex Optimization under Heavy-Tailed Noise
The authors propose HT-PAder, a parameter-free algorithm for online convex optimization (OCO) in non-stationary environments with heavy-tailed noise, where stochastic gradients admit only a finite p-th central moment (p ∈ (1, 2]). HT-PAder combines restarted AdaGrad experts over geometric block lengths with AdaGrad-Hedge, a pathwise meta-algorithm requiring no moment conditions on meta-losses. For domain diameter D, Lipschitz constant G, noise level σ, and comparator path length P_T, HT-PAder achieves expected universal dynamic regret of Õ(GD√[T(1+P_T/D)] + σDT^(1/p)(1+P_T/D)^[(p-1)/p]), without prior knowledge of problem parameters. The authors prove a matching lower bound, establishing optimality of the path-length exponent.
online convex optimizationheavy-tailed noisedynamic regretadagradparameter-free
Visual Credit Audit for Multimodal Spatial Reasoning
The Visual Credit Audit (VCA) framework decomposes multimodal spatial reasoning benchmarks into three estimands: correctness, image-supported decisions beyond text-only contexts, and relation-specific visual evidence response. The method employs training-free audits using image permutations, relation reversals, and controlled contrast sets to quantify uncredited correct decisions. Evaluations on four multimodal large language models (MLLMs) across two benchmarks reveal 12.73-26.25% correct-but-uncredited decisions, with image permutation reducing dependence-credited correctness by 21.25-47.80 percentage points. Relation reversal tests show 81.57-100% response consistency among uncredited agreements, while 32.11% of edits trigger answer changes. A 3×3 evidence-source factorial design demonstrates limitations of null controls in identifying relation-specific responses.
visual credit auditmultimodal spatial reasoningdependence-credited correctnessrelation reversalevidence-source factorial
SciFigAlign: Scoring Scientific Figures by Fine-tuned Alignment of Visuals with Manuscript Evidence
SciFigAlign introduces a fine-tuned multimodal scorer for scientific figure quality assessment, addressing limitations of traditional image quality evaluation methods. The approach leverages a dataset of 3,857 annotated scientific figures, rated across Clarity, Relevance, Informativeness, and Structure. SciFigAlign fine-tunes CLIP and SciBERT end-to-end, employing per-modality cross-attention and CubeMLP fusion, optimized with SmoothL1 regression and a ranking hinge loss. Evaluated under paper-level splits, it achieves a macro MAE of 0.3524 and 81.64% pairwise accuracy, demonstrating a 59% error reduction over LLM-as-judge baselines. Ablations highlight the necessity of manuscript-grounded inputs and ranking supervision for effective alignment of visual content with manuscript evidence.
multimodal scorerscientific figure assessmentclipscibertcubemlp
ScratchSim: A Procedural Synthetic Data Pipeline for Surface Scratch Detection
ScratchSim introduces a procedural rendering pipeline for generating synthetic annotated training data to address the scarcity of real-world defect datasets in industrial quality control. The pipeline leverages BlenderProc to produce configurable material appearances, camera modes, and domain randomization, with automatic COCO-format annotations. Four training strategies—synthetic-only, real-only, mixed, and fine-tuning from synthetic weights—were evaluated on two objects with different material properties using YOLOX, YOLO26, and LW-DETR detectors. Results demonstrate that fine-tuning from synthetic weights consistently outperforms real-only training, while mixed training effectively mitigates performance loss under scarce real-data conditions. The approach enables scalable defect detection for on-device industrial inspection.
procedural renderingdomain randomizationcoco-formatfine-tuningedge-deployable
Setoka: A Benchmark for Hierarchical User Understanding in Personalized Agents over Heterogeneous Data
The paper introduces Setoka, a benchmark for evaluating hierarchical user understanding in memory-augmented personalized agents across four levels: semantic memory, episodic memory, behavior pattern, and personality trait. The benchmark employs a psychometrics-based pipeline to synthesize diverse, coherent heterogeneous user data for privacy-preserving evaluation. Evaluation of 3 language models with 5 memory systems on 10 synthetic users reveals declining performance from semantic memory (strong) to episodic memory (weaker) and further to behavior patterns and personality traits (weakest), highlighting the need for improved memory mechanisms for cross-source integration and long-term abstraction.
personalized agentshierarchical user understandingmemory-augmented systemspsychometrics-based synthesisheterogeneous data integration
CoCaRS: Correlation Calibration-Based Redundancy Suppression for Heterogeneous Knowledge Distillation
The paper proposes CoCaRS, a correlation calibration-based redundancy suppression method for heterogeneous knowledge distillation (KD) that addresses representation discrepancies between teacher and student models. The method employs Confusion Evidence Estimation (CEE) and Strength Allocation Control (SAC) to calibrate feature decorrelation while preserving structural information, alongside Adaptive Coefficient Regulation (ACR) to dynamically adjust the suppression objective's contribution. Experiments on CIFAR-100 and ImageNet-1K demonstrate improved distillation performance and reduced sensitivity to coefficient settings compared to prior approaches.
knowledge distillationheterogeneous architecturesredundancy suppressionfeature decorrelationadaptive coefficient regulation
BayesAME: Bayesian Active Model Evaluation
BayesAME introduces a Bayesian framework for active model evaluation that automatically determines coreset size by iteratively selecting items based on information gain until performance uncertainty and estimate fluctuation meet user-defined thresholds. The method models performance as a random variable with latent abilities for item groups, leveraging historical model performances via a joint prior distribution. A multi-target extension captures performance correlations across models to reduce coreset size. Experiments demonstrate BayesAME's superiority over sequential adaptations of existing methods, validating non-random coreset selection and the benefits of continuous response log-likelihoods over binary scores.
bayesian frameworkcoreset selectionlatent abilityinformation gainlog-likelihoods
SymmGrid: Super-Scaling On-Robot Learning with Parallelized Symmetries and Egocentric-Exocentric Visual Perception
SymmGrid introduces a trajectory-level augmentation framework leveraging parallelized symmetries to accelerate on-robot learning in egocentric and exocentric visual setups. The method models a Markov Decision Process (MDP) under a symmetry tree, employing homographies to warp visual scenes and proprioception data, generating diverse symmetric equivalences for replay buffer enrichment. Evaluated on real robot manipulation tasks (peg-insertions, cable routing, object relocations), SymmGrid achieves wall-clock training convergence speed-ups of 1.37-2.17x, success rate improvements of 1.09x-1.27x, and normalized AUC ratios up to 2.59x, demonstrating sub-10 minute training feasibility for manipulation tasks.
symmetry treemarkov decision processhomographiesreplay bufferegocentric-exocentric
Progressive Multimodal Alignment for Continual Instruction Tuning
The paper introduces Progressive Multimodal Alignment (PMA), a framework addressing projector-level forgetting in Multimodal Continual Instruction Tuning (MCIT) by dynamically adapting the visual-language projector while preserving prior alignment. PMA employs a lightweight representation descriptor to detect distribution shifts, progressively expands projector experts via an expandable router, and retains the original projector as an anchor. Experiments on two MCIT benchmarks show consistent improvements over state-of-the-art methods, with sub-linear parameter growth and compatibility across diverse MLLM backbones.
multimodal continual instruction tuningprojector-level forgettingprogressive multimodal alignmentexpandable routerdistribution shift detection
Belief-Guided Decision Making with Uncertainty Gating in the Game of Go
The paper introduces a Belief-Guided architecture for Computer Go that decouples policy decisions from epistemic uncertainty modeling via a dedicated Belief head. This head functions as an internal simulator and critic, employing memory mechanisms (Transformer/GRU) to handle long-term dependencies and Ko rules, while a gating mechanism filters overconfident policy errors. Experiments show the approach improves search-free win rates and reduces hallucination, enabling professional-level play without reliance on computationally intensive Monte Carlo Tree Search (MCTS) on consumer hardware.
belief-guided architectureepistemic uncertaintymonte carlo tree searchko rulehallucination reduction
What Does It Take to Detect an AI Agent? Minimal Feature Sets for Behavioral Detection under Browser Automation
Proposes a three-class detection framework (human, bot, AI agent) to address architectural limitations of binary classifiers, which misclassify 34.5-39.1% of AI agents as human. Evaluates evasion resistance via a five-level ladder (n=2299 sessions) using MLPs, SAINT transformers, and GBMs, achieving 100% agent recall with two behavioral features (mouse_event_rate, teleport_click_ratio) across all evasion levels (macro-F1=0.991). Shows the signal stems from browser-automation artifacts (absent raw pointer-move streams) and remains robust under feature ablation.
behavioral detectionbrowser automationevasion resistancemulti-class classificationfeature ablation
Defending Against Backdoor Attacks via Alignment Checking in Model-Contrastive Federated Learning
FedDAB introduces a two-phase defense against backdoor attacks in Federated Learning (FL) by combining local contrastive regularization with alignment checking. The method first enhances direction and magnitude consistency among benign updates via a model-contrastive term, then evaluates updates using alignment checking to exclude anomalous ones. Theoretical analysis confirms FedDAB's robustness with a convergence rate of $\mathcal{O}(1/T)$. Experiments demonstrate superior performance over existing defenses, achieving higher accuracy in backdoor attack scenarios.
federated learningbackdoor attackscontrastive regularizationalignment checkingconvergence rate
BioVLN: A Simulation Platform for Visual Language Navigation in Biomedical Laboratories
BioVLN introduces a simulation platform for visual-language navigation in biomedical laboratories, addressing the inadequacy of household-oriented navigation models by representing instruments with three regions: physical body, clearance area, and operation side. The platform supports procedural and manual scene generation (47 scenes, 1667 episodes) with standardized interfaces for navigation and reinforcement learning. Experiments demonstrate geometric exploration achieves 74.4--87.5% success, while sampling multiple valid positions improves to 83.3--92.5% and reduces unsafe proximity.
visual-language navigationbiomedical laboratoriesprocedural scene generationreinforcement learningoperation area
Actions Have Consequences: Detecting Outcome Performativity using Intervention Testing
The paper introduces Outcome Performativity A/B Detection (OPAB), a method for detecting outcome performativity—where predictions causally influence their predicted outcomes—through intervention testing. OPAB assesses distributional dissimilarity between outcome groups under different prediction interventions, with derived sample complexity bounds validated empirically. Results demonstrate OPAB's effectiveness across various performative scenarios while identifying regions of indistinguishability where detection fails due to insufficient interventions. A case study on the Open Bandits dataset illustrates practical applications, highlighting limitations in data-scarce or ethically constrained settings.
outcome performativityintervention testingsample complexitydistributional dissimilarityopen bandits dataset
From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence
Pegasus introduces a framework for translating human manipulation videos into robot-learnable data via structured knowledge transfer, addressing the embodiment gap. The method constructs graph-based intermediate representations (Task Graph, Affordance Graph, Constraint Graph, Robot Planning Graph) and employs a hierarchical affordance latent space for generalization. A physics verifier ensures kinematic feasibility. Evaluated on GTEA Gaze+ and EPIC-KITCHENS-100, Pegasus demonstrates reliable cross-embodiment translation, reframing robot data generation as a scalable knowledge transfer problem.
embodied intelligenceaffordance graphtask graphkinematic feasibilitycross-embodiment translation
Human diversity fuels collective creativity that large language models cannot simulate or sustain
The study demonstrates that human linguistic diversity, particularly between native (L1) and non-native (L2) English writers, fuels collective creativity in metaphor generation, a process current large language models (LLMs) cannot replicate. In a preregistered experiment, L2 writers contributed greater collective diversity than L1 writers, especially when using their native language. AI-generated ideas (AI ideation) homogenized output diversity, erasing the L2 advantage, while AI refinement preserved it. Simulations using persona-based LLM prompting (across three model families) failed to match human diversity without degenerate text. Despite individual performance gains from AI ideation, collective diversity suffered, highlighting a trade-off between private incentives and public good in human-AI collaboration.
collective creativitylinguistic diversityai ideationllm simulationhuman-ai collaboration
Hearsay: Vision-Language Medical Diagnoses Without an Image
The study identifies systematic confabulation in vision-language models (VLMs) when diagnosing medical conditions without visual input, structured by patient demographics. Using Claude Opus-4.7, GPT-5.4, and Gemini-3.1-Pro, the authors queried models with demographic descriptors across chest X-ray, brain MRI, and dermatology cases, omitting images. Results show that diagnoses shift predictably based on demographics: Claude Opus-4.7 frequently diagnosed Melanoma for a 65-year-old white man with a skin mole and Sarcoidosis for a 32-year-old Black woman with a chest X-ray. GPT-5.4 exhibited broader confabulation, particularly naming Sarcoidosis for young Black patients. The study highlights structured output dissociation and probe-word sensitivity as critical failure modes, emphasizing the need for direct structured output auditing in clinical VLM deployment.
vision-language modelsconfabulationdemographic descriptorsstructured outputprobe-word sensitivity
Think Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents
We propose Think Short, Defer Smart (TSDS), a framework for edge-deployed LLM agents that optimizes reasoning budget while maintaining reliability. TSDS integrates a lightweight convergence probe for halting on-device reasoning and a perplexity-based deferral rule for escalating uncertain actions to cloud-side models, jointly calibrated via multi-objective Learn-Then-Test (LTT) with finite-sample guarantees on episode reward and cloud-call rate. Evaluated on ReAct benchmarks including GSM8K, HotpotQA, MBPP, and household robot tasks, TSDS reduces per-episode thinking compute by 43%-73% over deferral-only baselines while maintaining certified performance guarantees.
react paradigmconvergence probeperplexity-based deferrallearn-then-testedge llm agents
ReCo: Reweighting GRPO Against Distributional Concentration
The paper introduces ReCo, a reweighting method addressing distributional concentration in Group Relative Policy Optimization (GRPO) for language model post-training. ReCo normalizes response contributions by expected group occurrence and replaces GRPO's token-level importance ratio with a variance-based ratio favoring non-saturated decisions. Evaluated on Qwen2.5-Math-1.5B/7B and Llama-3.1-8B-Instruct across five math reasoning benchmarks, ReCo improves Pass@k for large k while matching GRPO performance for small k, mitigating GRPO's tendency to reduce reasoning path coverage.
group relative policy optimizationreweighting methodvariance-based ratiomathematical reasoningpass@k
From Representations to Behaviors: Exploring the Person-Situation-Behavior Triad in LLMs
This work introduces a framework for analyzing trait-like representations in LLMs based on Funder's personality triad, adapting Person, Situation, and Behavior components for model analysis. Using contrastive behavior pairs and sparse autoencoder (SAE) decomposition, the study identifies internal features associated with personality traits, validated through token-level activations and robustness to paraphrasing. Feature-level interventions induce bidirectional trait shifts across diverse situations while preserving response validity. Application to social intelligence tasks reveals behavioral changes consistent with human personality research, demonstrating LLMs' controllable trait-like representations linking internal states, situational expression, and behavioral outcomes.
trait-like representationssparse autoencodertoken-level activationfeature-level interventionsocial intelligence tasks
Budget-Aware LLM Discovery via Cost-Calibrated Frontier Utility
CostAda, a cost-calibrated adaptive controller, optimizes large language model discovery by integrating token cost and budget constraints into frontier utility evaluation. It dynamically adjusts local exploration intensity, frontier allocation, and tactic intervention based on cost-calibrated frontier utility, which measures progress relative to action cost and remaining budget. CostAda outperforms baselines, achieving full-budget quality with at most half the budget on 12 of 16 benchmark-backbone pairs and the strongest mean final quality on all 8 benchmarks under GLM-5 and GPT-5.4.
cost-calibrated frontier utilityadaptive controllertoken costbudget constraintslocal exploration intensity
AI as Friction for Reflection Support in Ideation
The article proposes reframing generative AI's role in design ideation from a friction-reducing output accelerator to a reflection-supporting rationale builder. Analyzing current AI-assisted design tools, the authors argue that excessive focus on smooth iteration overlooks the cognitive value of reflection-in-action during idea evaluation and refinement. They suggest AI systems should intentionally introduce productive friction to help designers develop and communicate design rationales, particularly in collaborative settings where idea articulation is crucial for group ideation processes.
generative aidesign ideationreflection-in-actioncognitive frictioncollaborative design
A First Look at Coding Agents' Compliance with AI Contribution Rules in Open-Source Communities
This work investigates coding agents' compliance with AI contribution rules in open-source repositories, addressing a critical gap in understanding their behavior. The authors introduce RepoComplianceBench, a benchmark comprising 106 issues from 49 repositories with AI contribution rules, evaluating agents' adherence to bans, disclosure requirements, verification gates, and human escalation. Experiments on four frontier models reveal that agents rarely retrieve rules proactively but improve disclosure and verification with prompts, rule quotes, and verifier feedback. However, agents consistently fail to comply with bans in AI-prohibited repositories, indicating that enforcing bans and human escalations remains unresolved despite progress on verification and disclosure.
repo-compliancebenchverification gateshuman escalationdisclosure requirementscoding agents
FedTopo: Relation-Level Topology Sharing for Model-Heterogeneous Federated Learning
FedTopo introduces relation-level topology sharing for model-heterogeneous federated learning (FL), addressing representation misalignment caused by diverse local architectures. The method encodes global knowledge as class relation topologies derived from local prototypes, aggregates them with reliability-aware weighting, and uses the global topology to guide local training by emphasizing topology-similar negative classes. Evaluations on three datasets across eight heterogeneous backbones demonstrate consistent improvements over parameter-, distillation-, and prototype-sharing baselines, with low communication and no inference overhead.
federated learningmodel-heterogeneousrelation topologyprototype aggregationreliability-aware
Crossing-Free Probabilistic K-Line Forecasts Without Retraining
The paper introduces K-line--Quantile Sequential Projection (KQSP), a parameter-free method for enforcing consistency in probabilistic K-line forecasts without model retraining. KQSP addresses quantile crossing and K-line crossing by sequentially projecting forecasts onto feasible regions, preserving predictive accuracy while requiring minimal corrections. Evaluated across multiple models, including pretrained foundation models, KQSP reduces both crossing rates to zero on all test data, demonstrating post-hoc consistency enforcement. The method operates independently of forecast generation, requiring no architectural modifications or training objectives.
probabilistic forecastingquantile crossingk-line consistencysequential projectionfoundation models
SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
SecRespond introduces the first benchmark for evaluating LLM agents in post-compromise incident response, addressing a gap in existing cybersecurity benchmarks focused on pre-compromise settings. The benchmark tasks agents with analyzing forensic disk snapshots, alerts, vulnerability scans, and baseline checks to produce forensic reports and remediation plans. It spans 10 cyber ranges derived from compromised cloud hosts, covering 4 entry-point types, 21 ATT&CK techniques, and 5 operating systems. Evaluation of 23 LLMs reveals that while agents reliably identify alerted issues, they struggle with proactive disk investigation and comprehensive remediation, highlighting a critical limitation in real-world incident response.
post-compromiseforensic disk snapshotatt&ck techniquesremediation plancyber ranges
Property-driven Causal Abstractions for Markov Decision Processes
The paper introduces property-driven causal abstractions for factored Markov Decision Processes (MDPs) to address state-space explosion. By leveraging causal relations over state variable predicates, the method identifies states sharing similar abstraction property fulfillment or violation. Theoretical and empirical comparisons across MDP variants (interval MDPs, stochastic games) demonstrate effectiveness: standard benchmarks yield compact abstractions enabling near-optimal policy computation, with generalizations to larger MDP models.
markov decision processescausal abstractionfactored state spacesinterval mdpsstochastic games
SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution
SkillRise introduces a unified reinforcement learning framework for cross-task skill evolution in LLM agents, addressing the limitations of independent task treatment and multi-stage skill pipelines. It organizes related tasks into progressively challenging sequences, employing a single policy to alternate between task solving and skill curation via an evolving document. Decoupled credit assignment supervises task outcomes and downstream skill reuse. Evaluated on ALFWorld, WebShop, and ScienceWorld, SkillRise achieves Pass@1 gains of 2.3-8.5 percentage points over baselines, demonstrating scalability across tasks and runtime efficiency. Analysis confirms skill transferability without repeated task sampling.
skillrisereinforcement learningcross-taskskill evolutionllm agents
Journey Operators for Structured Multi-Axis Composition
The paper introduces a framework for modeling multi-axis structured data, where each axis carries independent transformations that commute across axes. The proposed journey operator computes the product of per-axis transformations along a path, governing both data composition and relative position description. The framework generalizes Rotary Position Embedding (RoPE) and its multi-dimensional variants, enabling content-adaptive positional inductive biases when transformations depend on data. Theoretical analysis establishes path independence conditions and derives block-wise rotations as the natural form of pairwise scoring rules under specified symmetries. The authors design JoFormer, a model for value aggregation, and demonstrate its effectiveness in vision, language, and length generalization tasks.
journey operatormulti-axis structurerotary position embeddingpath independenceblock-wise rotations
Do Latent Channels Actually Communicate? A Causal Audit of Latent Multi-Agent LLM
The study introduces a causal audit framework to analyze latent communication in multi-agent LLM systems, addressing whether receiver models utilize sender-generated representations effectively. The method employs controlled message replacements at the sender-receiver boundary, measuring sender information encoding, receiver sensitivity, and task-specific value across four message settings. Evaluations on Qwen3-4B and Qwen3-8B using GSM8K, ARC-C, and MATH-500 reveal that aggregate accuracy masks nuanced effects: on GSM8K, Qwen3-4B shows a net -1.00pp performance effect decomposing into -6.17pp (other-example message) and +5.17pp (example-specific content), with reversals at 8B. MATH-500 results highlight dominance of other-example message effects (8.33pp for 4B, larger for 8B), demonstrating distinct contributions from example-specific content and other-agent value.
latent communicationmulti-agent systemscausal auditmessage replacementtask-specific value
See2Think: Do Multimodal Models Really Use Intermediate Visual States?
We introduce See2Think, a unified evaluation framework comprising See2ThinkBench and Visual Action-of-Thought (VAoT) to assess multimodal models' reliance on intermediate visual states. See2ThinkBench contains 1,200 visually dependent problems across 12 task categories, while VAoT records textual thoughts, visual actions, rendered states, and reasoning under four controlled inference settings. Evaluating proprietary and open-source multimodal models reveals that visual reasoning is model- and environment-dependent, with no single setting dominating across tasks. Process analysis shows models select relevant visual operations but struggle with faithful rendering, and accuracy drops by over 10 percentage points under task-relevant corrupted feedback.
multimodal modelsvisual reasoningintermediate visual statesfaithful renderingtask-relevant feedback
MediaWiki Code2Code Search: Neural Retrieval for the Semantic Discovery of Open-Source Software Entities
MediaWiki Code2Code Search introduces a neural retrieval system for semantic code-to-code discovery, addressing the lexical gap in large-scale ecosystems. The system indexes 1.29 million structural entities across 2,500+ MediaWiki repositories using a split-build architecture, decoupling GPU-intensive offline indexing from a CPU-only serving layer. The FAISS IVF-PQ index achieves a 96.6% size reduction (168.6 MB) and a median query latency of 1.85 seconds on commodity hardware. Evaluated on a 27-query benchmark, it outperforms BM25 with a P@10 of 0.87 versus 0.64, particularly excelling in name-obfuscated tasks. The system is available under Apache 2.0 and provides an open RESTful API.
neural retrievalsemantic discoverysplit-build architecturefaiss ivf-pqlexical gap
Searching for Robust Augmentations to Improve Out-of-Domain Generalization in Dermoscopic Skin Cancer Classification
The study demonstrates that data augmentation policies, particularly photometric transformations and mix-based approaches, significantly improve out-of-domain (OOD) generalization in dermoscopic skin cancer classification. Using a ConvNeXt-Large backbone and lesion-ID-level splits, the authors evaluated augmentations on ISIC Archive and Derm7pt, with HAM10000 and ISIC 2019-2020 as OOD test sets. The mix policy achieved a +0.053 ROC-AUC gain (95% CI +0.045 to +0.061, p<0.001) across seeds, though clinical validation on a small dataset showed inconsistent gains. Results highlight that augmentations modeling real domain shifts outperform in-domain optimization, but selection bias necessitates source-disjoint evaluation protocols.
data augmentationout-of-domain generalizationconvnext-largeroc-aucphotometric transformations
Phoneme- vs. Character-Level Targets and Selective State-Space Models for Intracortical Brain-to-Text
This work evaluates two underexplored design choices in intracortical brain-to-text systems: selective state-space models (Mamba) versus recurrent decoders (GRU) and phoneme-level versus character-level output targets. Using a controlled 2x2 experimental design on the Brain-to-Text '25 benchmark with a CTC objective, the study finds that GRU-based decoders outperform Mamba hybrids, achieving 12.62% PER and 21.19% WER for phoneme targets and 13.39% CER and 26.28% WER for character targets after LM rescoring. Error analysis reveals distinct failure modes: articulatory phoneme confusions versus lexical and word-boundary errors.
intracortical brain-to-textselective state-space modelsphoneme-level targetsctc objectiveerror analysis
An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI
An attention-based deep learning framework is proposed for Alzheimer's disease classification using resting-state fMRI, addressing challenges of high dimensionality and complex inter-regional dependencies. The method treats brain regions as tokens and employs a Transformer-inspired self-attention mechanism to model global functional dependencies directly from connectivity matrices, eliminating manual feature engineering. Evaluated on the Alzheimer's Disease Neuroimaging Initiative cohort with a subject-wise protocol to prevent information leakage, the model achieves 88.95% accuracy and 0.90 ROC-AUC for binary classification of Alzheimer's versus cognitively normal subjects, demonstrating robust performance and interpretability.
attention mechanismfunctional connectivityresting-state fmritransformeralzheimer's disease
Multimodal fusion of visual and morphometric features for avian bone classification
This study introduces a multimodal framework combining CNN-based visual analysis with osteometric measurements for avian bone classification, addressing skeletal element identification and family-level taxonomy. The method employs BiRefNet and SAM2 for image segmentation, extracts features via EfficientNet_V2_S, and fuses them with morphometric data through feature-level fusion. Evaluation on 10,000+ images yielded 86% accuracy for bone-type classification and 51% (75% top-3) for family-level identification, demonstrating feasibility but highlighting taxonomic challenges. The work establishes a baseline for AI-assisted zooarchaeology by integrating visual and quantitative data in a scalable deep-learning pipeline.
multimodal fusionconvolutional neural networkosteometric analysiszooarchaeological identificationfeature-level fusion
Zero-Shot Face-to-Speech Synthesis via Latent Space Adaptation of a Style-Diffusion TTS Model
The paper introduces a zero-shot Face-to-Speech (F2S) synthesis framework that generates plausible speech from static facial images without reference audio. The method adapts face-recognition features to the style space of a frozen StyleTTS 2 model via a lightweight Face Adapter and soft-tuning of the face encoder's upper blocks. Evaluated on LRS3, the system achieves high naturalness (UTMOS 3.7-4.0 vs. 3.61 ground truth), successful face-to-voice retrieval, and cross-lingual generalization to Spanish without retraining.
zero-shot synthesisstyle-diffusionface adapterlatent space alignmentmultimodal generation
Dual Inversion for Text-to-Image Diffusion Models: From Both Prompt and Noise Perspectives
Dualin introduces a two-stage dual inversion method for text-to-image diffusion models, jointly recovering semantic prompts and latent noise to address limitations in existing prompt inversion techniques. The first stage employs CLIP and large language models to generate human-interpretable hard prompts, while the second stage uses unconditional DDIM inversion to reconstruct precise latent noise, ensuring structural consistency. Theoretical analysis shows that inverted noise enables flexible image editing without re-optimization. Experiments demonstrate Dualin achieves state-of-the-art image fidelity and high-quality inverted prompts, providing a robust foundation for precise and controllable image editing.
prompt inversionlatent noiseddim inversionclipimage editing
UrbanDS: A Graph-Guided LLM Multi-Agent System for Data-Intensive Urban Tasks
UrbanDS introduces a graph-guided LLM multi-agent system for data-intensive urban tasks, addressing challenges in large-scale heterogeneous data discovery. The method constructs a unified dataset graph via a Data Profiling Agent (creating dataset skills) and Relation Agent (identifying inter-dataset relationships), with runtime orchestration by Planner and Execution Agents. Evaluation on UrbanDS-Bench shows superior performance over existing data science agents, with real-world deployment in Wuhan's Dongxihu District urban operations platform.
multi-agent systemdataset graphdata profilingurban data sciencellm agents
FARI: Robust One-Step Inversion for Watermarking in Diffusion Models
FARI introduces a one-step inversion framework for robust watermarking in diffusion models, addressing the speed-robustness trade-off by exploiting lower curvature in inversion trajectories. The method combines fast asymmetric inversion with lightweight adversarial LoRA fine-tuning of the denoiser, prioritizing robustness against external distortions over internal truncation error. Results show FARI surpasses 50-step DDIM inversion in watermark-verification robustness while reducing inference time, achieving these gains with just 20 minutes of fine-tuning on an NVIDIA RTX A6000 GPU.
diffusion modelswatermarkinginversionadversarial traininglora fine-tuning
Automated Multilabel Mpox Research Classification with Explainable Transformer Models
Proposes a transformer-based multilabel classification system for organizing Mpox research literature, addressing challenges in public health information management. Evaluates BERT against other models on 14,590 articles across categories including outbreaks and vaccination, achieving 97.05% accuracy and 96.46% macro F1. Incorporates SHAP for interpretability, identifying key lexical features influencing classification decisions to enhance researcher and policymaker access.
multilabel classificationtransformer modelsshap analysispublic health informaticsbert
MPEcho: A Melody and Phoneme-Aware Generative Framework for Controllable Cover Song Generation
MPEcho introduces a melody and phoneme-aware generative framework for controllable cover song generation, addressing the phoneme error rate (PER) limitation in prior work. The method integrates a phoneme encoder and length regulator into the SongEcho framework, leveraging explicit phoneme-level conditioning from Phonsa, a Whisper-based transcription model for singing voices. Experiments demonstrate Phonsa's alignment accuracy and MPEcho's PER reduction in end-to-end cover song generation.
cover song generationphoneme error ratewhisper-based transcriptionlength regulatorsinging voice synthesis
Efficient Heteroscedastic Bayesian Optimization for Risk-Aware AutoRL
The authors propose Efficient and Risk-Averse Heteroscedastic Bayesian Optimization (ERAHBO), a Bayesian optimization method for hyperparameter tuning in reinforcement learning that jointly models mean performance and variability. ERAHBO employs adaptive re-sampling to improve sample efficiency and identifies configurations optimizing both expected return and risk reduction. Empirical evaluations across diverse RL algorithms and environments demonstrate ERAHBO's superiority over risk-neutral and risk-averse baselines in achieving risk-averse returns with improved sample efficiency.
bayesian optimizationreinforcement learningheteroscedastic modelingrisk-averse optimizationhyperparameter tuning
Scientific Knowledge Discovery in the Age of Large Language Models
The chapter surveys 34 peer-reviewed papers on applying generative large language models (LLMs) to scholarly literature retrieval and screening, addressing challenges in manual query formulation and study inspection. Methods include Boolean search over OpenAIRE Graph (1,589 records screened to 34 inclusions), with analysis focusing on LLM types, access modes, adaptation techniques (e.g., prompting, architectural modifications), ground-truth sources, and evaluation metrics. Results highlight LLMs' flexibility in automating literature review tasks compared to conventional systems.
generative llmsliterature retrievalprompting techniquesopenaire graphevaluation metrics
AgenticCANN: Automated Ascend C Operator Generation via Knowledge-Augmented Agentic Evolution
AgenticCANN introduces a knowledge-augmented agentic evolution framework for automated Ascend C operator synthesis in low-corpus NPU environments. The method incorporates a knowledge-orchestrated generation system to address platform knowledge deficits and employs a stage-adaptive agentic evolution strategy that dynamically aligns LLM interaction modes with generation phases. Evaluated on Huawei Ascend 910B across six operators spanning five pattern categories, AgenticCANN achieves 90-100% feasibility on elementwise and normalization operators, 56% on fusion operators, and up to 6.65× speedup on 1B Pangu model inference kernels. Knowledge injection monotonically improves feasibility from 57% to 86% on elementwise operators, demonstrating its general benefit.
ascend cagentic evolutionknowledge-orchestrated generationstage-adaptive strategylow-corpus npu
Graph Is the Verifier: Agentic Reinforcement Learning for Interprocedural Vulnerability Detection
VulAgentRL introduces an agentic reinforcement learning framework for interprocedural vulnerability detection, addressing the limitation of function-isolated classifiers by leveraging a Code Property Graph (CPG) for evidence gathering and verification. The CPG enables precise reward assignment by verifying cited evidence via persistent node identifiers, while policy initialization through teacher distillation ensures tool-use behavior acquisition. Evaluated under a repository-level split, VulAgentRL outperforms state-of-the-art baselines on strict pair-wise-correct metrics, maintains robustness on out-of-distribution data, and reduces tool calls.
agentic reinforcement learningcode property graphinterprocedural vulnerability detectionteacher distillationpair-wise-correct metric
Constitutional Midtraining: Content Presence Drives Alignment Gains
Constitutional midtraining—inserting values-based content during model training—demonstrates durable alignment improvements without capability trade-offs. Using a 2x2 factorial design (curriculum ordering x deliberative reasoning) on a 394M-token corpus derived from Anthropic's Constitution, the authors compare four midtrained variants (120B scale) against a replay-only control. Results show alignment gains persist post-SFT and benign fine-tuning, particularly in mitigating blackmail propensity (-17.5pp), though advantages attenuate under in-context pressure. Constitutional content presence, not structure, drives benefits, with no observed degradation on MMLU, ARC-Easy, piqa, or GSM8K.
constitutional midtrainingalignment durabilityfactorial designblackmail propensityin-context pressure
Physically Real-time Infrared Attack against Optical Flow Estimation Networks
We propose a physically real-time infrared attack method targeting Optical Flow Estimation Networks (OFENs), addressing robustness concerns in safety-critical applications like autonomous driving. The approach leverages infrared lights for stealth and precomputes Adversarial Examples (AEs) to enable dynamic, real-time display without modifying the victim system. Unlike prior digital-to-physical attacks, this method directly compromises OFENs in physical environments, overcoming AE ineffectiveness limitations. Experiments demonstrate successful attacks across varied lighting conditions, object motion velocities, and placements, significantly impairing optical flow estimation accuracy.
optical flow estimation networksadversarial examplesinfrared attackphysical-world attackreal-time display
FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows
FPSGen introduces a flexible framework for point cloud scene generation that decouples point source construction from partial LiDAR scans by predicting a bird's-eye-view (BEV) prior with density, height, and mask channels. The method samples the density map to form BEV-supported point sources and employs a teacher-student approximate optimal transport scheme to learn a velocity field for straighter transport paths. Evaluated on SemanticKITTI and KITTI-360, FPSGen achieves state-of-the-art JSD (Jensen-Shannon Divergence) and voxel IoU (Intersection over Union) in completion tasks, and the best Coverage (COV) in unconditional generation with a single transport step.
point cloud generationbird's-eye-view prioroptimal transportscene completionlidar-conditioned generation
Rethinking Self-Evolution: A Constrained Exploration-Exploitation Process for Mitigating Skill Overfitting
SkillBoost introduces a three-stage framework for mitigating skill overfitting in LLM agents through constrained exploration-exploitation. The method combines structured exploitation (localizing failures to editable skill components), prior-guided exploration (leveraging LLM knowledge for diverse repairs), and verified acceptance (committing candidates only if they improve performance within regression bounds). Evaluated across 23 model-benchmark configurations, SkillBoost achieves state-of-the-art performance, outperforming human-crafted and LLM-generated skills while enabling transfer to similar tasks.
skill overfittingexploration-exploitationllm agentsprior-guided explorationregression bounds
AlphaSchema: Exploring the Space of Trading Semantics for LLM-Based Alpha Mining
AlphaSchema introduces a structured semantic space for LLM-based alpha mining, decoupling exploration from implementation via schema plans (Event, Context, Qualities, Direction, Output). The framework employs an LLM for factor translation, accumulates rewards to learn a surrogate model, and balances exploration-exploitation through iterative selection. Experiments on Chinese stocks demonstrate strong predictive and portfolio performance, with semantic search navigating diverse regions and LLM implementations showing robust predictive quality across different models.
alpha miningsemantic spaceschema plansurrogate modelexploration-exploitation
FakeIDet3-DB: Refining Digital Attacks and Patch Extraction for Secure ID Benchmarking
FakeIDet3-DB introduces the first comprehensive database of digital manipulations on real government-issued IDs, addressing domain gaps in ID authentication by combining classical (e.g., copy-move) and Generative AI-driven (e.g., face-swapping) attacks. To ensure privacy compliance, PACE (Pseudo-Anonymized Contextual patch Extraction) is proposed, leveraging Integral Image mapping and distance-driven Non-Maximum Suppression to extract 5.2M patches from 6.4K images while preventing PII leakage. Evaluation shows state-of-the-art models struggle with detection (32.45% EER) and localization (83.48% AUC-ROC) of these attacks, highlighting the dataset's forensic utility.
fakeidet3-dbgenerative aipatch extractionnon-maximum suppressionintegral image
Borrowed Strength: Best-of-N Search over a Code EncodingBreaks Self-Check Jailbreak Defenses
The paper demonstrates that self-check jailbreak defenses like SAGE (99% success rate) can be breached by composing a code-completion encoding with best-of-N search, achieving 67/22/15% success across three open targets, persisting on a 70B model. The attack exploits the defense's reliance on the target model's self-assessment, which converts refusal requests inconsistently (32-97%). The composition's effectiveness varies by defense type: code encoding outperforms against transform defenses, while character search excels against gate defenses, explained by probe counts at decision boundaries. Results are validated via 310,000 human-scored generations.
self-check defensebest-of-n searchcode-completion encodingjailbreak attackdecision boundary probes
Filesystem-Based Memory for LLM Agents: Organization, Evolution, and Sustainability
The paper presents the first systematic study of filesystem-based memory for LLM agents, formalizing three roles (management, search, execution) around a shared memory filesystem. Through experiments on long-conversation benchmarks and embodied tasks, the authors vary memory organization, scale, tooling, and agent capabilities to evaluate answer quality, cost, and store health. Key findings show organized stores reduce retrieval costs by ~50% for large material, but current agents fail to maintain organization or leverage it for improved answers without strong management. Toolset changes proved as impactful as model swaps in reshaping memory structure.
llm agentsfilesystem memorymemory organizationretrieval efficiencytool harness
Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method
MalGuard introduces a graph-based malware detection method addressing limitations of byte-based ML and prior graph approaches. It combines operational role identification (grouping cohesive basic blocks to capture program behavior) with program graph representation learning (modeling role interactions, preserving malicious signals, and hierarchical structure). Evaluations demonstrate improved detection performance and reduced expected cost of undetected malware compared to existing methods.
malware detectiongraph-based learningoperational role identificationprogram behavior modelingcybersecurity risk
Understanding Context Sampling in TabPFN on Small Tabular Datasets
This work analyzes context sampling strategies for TabPFN, a tabular classifier using in-context learning without gradient updates. Through experiments on 15 OpenML datasets, the study examines how context size and selection methods affect prediction stability and accuracy. Key findings show larger contexts (k>16) improve stability (AUC CV reduction from 6-18% to 1-4%) and accuracy, with feature-space diversity (β=+0.23, p=3e-12) outperforming distribution matching. Costly selection methods (K-Means, farthest-point) provide negligible accuracy gains over random sampling despite 100-1000× higher computational costs.
in-context learningtabular classificationfeature-space coverageprototype selectionauc stability
WhisperRec: Latent Reasoning for Efficient Foundation Recommendation Models
WhisperRec introduces an efficient latent reasoning framework for foundation recommendation models (FRMs), addressing the inference overhead of explicit Chain-of-Thought (CoT) approaches. It compresses teacher-generated CoT into learnable latent reasoning tokens, enabling reasoning in latent space without verbose rationales. The method employs Multi-View Adaptive CoT (MV-ACoT) for diverse supervision and adapts reasoning complexity per instance, followed by a three-stage Latent Reasoning Alignment procedure to internalize CoT into latent representations. Curriculum-based post-training activates latent-token reasoning while preserving standard recommendation capabilities. Experiments on the Kuaishou dataset and LLM-Rec benchmark show WhisperRec outperforms explicit-CoT methods, improving SID@64 by 17.44% and 9.33% respectively, with over 10x higher inference throughput.
latent reasoningchain-of-thoughtfoundation recommendation modelsmulti-view adaptive cotinference throughput
Fewer Clarifications, Better Code: Benchmarking Cross-Session Personalized Ambiguity Adaptation in Coding Assistants
The paper introduces CAPA, a benchmark for cross-session personalized ambiguity adaptation in coding assistants, addressing recurring user-specific ambiguities across sessions. CAPA characterizes ambiguity through six mechanisms, generating 600 coding sessions via a controlled pipeline, with 300 held-out for evaluation. Evaluating 12 LLMs under no-history and same-user-history conditions, the study measures executable success, first-turn success, and turns-to-completion, proposing history gating for improved performance. Results demonstrate the potential for long-term coding assistants to reduce repeated clarifications by leveraging resolved session history.
ambiguity adaptationcoding assistantscross-session memoryexecutable successhistory gating
Living-Harness Is an Interactive-Agent Evolver
We introduce Living-Harness, a self-evolving agent harness that dynamically updates procedural knowledge based on task trajectories and evaluator feedback, addressing the limitations of static harnesses in LLM agents. The method employs an Evolution-SOP to extract episode abstractions, structured update evidence, and procedural knowledge in the form of episodic memory and state graphs, while keeping tools and base context fixed. Evaluated on eight interactive environments from τ²-Bench and MultiWOZ-2.4, Living-Harness improves average Pass@1 by 10.07 and 9.91 percentage points, respectively, over the strongest interactive baselines, demonstrating retrieval-only reuse across model backbones.
self-evolving harnessepisodic memorystate graphevolution-soppass@1
Decoupled Visual Processing: Efficient Multimodal Adaptation via Modality-Specific Transformer Substitution
Decoupled Visual Processing (DVP) introduces an efficient framework for multimodal adaptation by replacing upper decoder layers of pretrained LLMs with a lightweight, modality-specific transformer block for visual tokens. Visual and textual tokens are processed jointly in initial layers, then split: visual tokens pass through the new block while textual tokens continue through frozen layers, with outputs concatenated before the language modeling head. Training updates only the visual block, reducing parameter count significantly. Evaluations on LLaVA-1.5 show competitive performance on MME, POPE, and ChartQA benchmarks with minimal trainable parameters, demonstrating effective decoupled visual learning.
multimodal adaptationdecoupled visual processingtransformer blockparameter-efficientvisual instruction tuning
A Physics-Informed Framework for PID Tuning of Chemical Processes Using Large Language Model Agents
This work introduces a physics-informed framework for PID tuning in chemical processes using large language model (LLM) agents, formalizing iterative engineer-like workflows. The framework employs LLMs to analyze closed-loop response features, diagnose control-engineering issues, and generate PID gains iteratively, supported by internal model control (IMC) demonstrations. For local deployment, Qwen3-0.6B is fine-tuned via supervised fine-tuning (SFT) and physics-informed group relative policy optimization (PI-GRPO) with stability and performance rewards. Evaluated on 100 FOPDT and 100 SOPDT test cases, hosted LLMs (DeepSeek-V4-Flash, Qwen3.7-Plus) achieve success rates of 75-89% and 77-79%, while Qwen3-0.6B reaches 94.0% success after PI-GRPO, improving reliability and stability margins.
pid tuninginternal model controlsupervised fine-tuninggroup relative policy optimizationclosed-loop response
Eco3S: Complex Socio-Economic System Simulation via Agent-Based Models
Eco3S introduces a socio-economic system simulation framework addressing key challenges in LLM-based agent-based modeling. The framework employs three mechanisms: Co-evolving Environment Design for bidirectional agent-environment feedback, Structural Causal Simulation for flexible counterfactual reasoning via structural causal models, and a Simulation-Analysis-Refinement Paradigm for iterative experimental refinement. Experiments demonstrate Eco3S's effectiveness in replicating established economic studies (canal decay, governance origins, information propagation) and phenomena, showcasing its scalability and generalizability for rigorous economic research and policy-making.
agent-based modelingstructural causal modelcounterfactual reasoningco-evolving environmentsocio-economic simulation
One Run Is Not an Idea: The Implementation Lottery in Automated Research
The paper introduces the concept of the 'implementation lottery,' where conclusions about a research idea depend on the specific implementation sampled, leading to unreliable idea-level inferences. The authors propose the Idea Reliability Audit, which measures idea reliability by validating candidate implementations, sampling fresh implementations, and using outcome-blind fidelity labels. Across 312 assignments on 13 tabular tasks and two coding-agent setups, implementation variance was 5-10 times greater than same-artifact rerun variance, with winner reversals occurring in 25.6%-43.6% of decisions. The findings emphasize the need for multiple implementations before using scores for idea-level decisions.
implementation lotteryidea reliabilityoutcome-blind fidelitywinner reversalimplementation variance
Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models
This study evaluates the efficacy of VGG16, VGG19, and ResNet50 models for classifying lung diseases from X-ray images, focusing on pneumonia, tuberculosis, lung cancer, and normal lungs. The models were trained on a large dataset of X-ray images to assess their diagnostic accuracy. Results indicate that ResNet50 outperforms VGG16 and VGG19 in terms of efficiency and accuracy, demonstrating its potential for early and accurate diagnosis of pulmonary diseases. The findings suggest that deep learning models can be effectively integrated into clinical practice to enhance patient outcomes through early disease detection.
convolutional neural networksvgg16vgg19resnet50x-ray images
Recover, Decode, Reguard: Guard-Agnostic Defense Amplification againstEncoded VLM Jailbreaks
The paper introduces a guard-agnostic recover-and-decode amplifier for vision-language model (VLM) defense, which transcribes image content and restates encoded text into plain language before applying safety classifiers. Evaluated against an ensemble of eleven attacks (best-of-suite approach), the amplifier partially mitigates the decode gap but exposes a safety-utility ceiling: undefended attacks break 89-91% of behaviors, while the best guard-plus-amplifier configuration still leaves 63-65% vulnerable. A modular reguard layer reduces residual vulnerabilities but increases benign over-refusal to 81-92%. The study highlights limitations of recovery-based defenses against representation-shifting attacks and provides empirical insights into their trade-offs.
vision-language modelssafety classifiersdecode gapguard-agnostic amplifierrepresentation-shifting attacks
ServerlessT2I: Efficient Text-to-Image Workflow Serving on a Serverless Platform
ServerlessT2I introduces a serverless-native system for text-to-image workflows by decomposing them into loosely coupled model functions, enabling per-model scaling, declarative composition, and fairness-aware scheduling. The system leverages slack GPU memory during compute-bound inference to reduce model loading and communication overheads, while a fair scheduler optimizes multi-tenant serving. Evaluations using production traces show ServerlessT2I achieves 2× higher request rates than monolithic approaches with identical GPU budgets and reduces GPU usage by 3× while meeting SLOs.
serverless computingtext-to-image workflowsgpu memory harvestingfair schedulingservice level objectives
A Persona-based Rate Action Index
The paper introduces a persona-based index for predicting U.S. Federal Open Market Committee (FOMC) rate decisions (hike/hold/cut) by modeling individual members' monetary-policy stances. Using a novel dataset of 25,000 text chunks, the method constructs generative personas with high identifiability (8× chance recall) and detectability (τ̂=0.23). Persona representations capture hawk-dove policy alignments (Kendall’s τ=0.63) and time-varying market responses, forming an index that predicts rate changes (τ=0.68, accuracy=0.69 vs 0.47 baseline) with a 3-quarter lead.
persona-based modelingmonetary-policy predictionretrieval-augmented generationhawk-dove spectrumtime-varying representations
AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control
AgentGFM introduces a graph foundation model where nodes autonomously control information flow via agent-based decision making, addressing structural variation across graphs. Each node acts as an agent following a shared policy, executing a predict-act-observe-correct loop to adaptively manage source reception, signal-channel selection, and gain-aware halting. Experiments on node-level, graph-level, and large-scale transfer tasks demonstrate robust performance across diverse topologies.
graph foundation modelinformation-flow controlagent-based decision makingnode-wise haltingtopological patterns
Shared Symbolic Backbones for Physically Consistent Multi-Output Symbolic Regression
The paper introduces a neuro-evolutionary symbolic regression method for coupled multi-output systems, employing a shared symbolic backbone—latent symbolic units reused across outputs via sparse additive/multiplicative read-outs. The discrete structure evolves via mutation/crossover, while continuous parameters are tuned by gradient descent. Evaluated on benchmarks with ground truth and a hydrothermal liquefaction case, results indicate coupling primarily enforces cross-output consistency for weakly identifiable shared factors (e.g., Langmuir-Hinshelwood denominators), rather than reducing prediction error. Independent PySR matches coupled models when outputs are identifiable (e.g., Van de Vusse benchmark). The framework excels as a structured shared-mechanism extractor for sparse, shared, or constrained systems.
symbolic regressionneuro-evolutionarymulti-output systemslatent symbolic unitsgradient descent
The Art of Not Forgetting A Local Learning Architecture for Continual Learning
The paper introduces CMP (Cognitive Memory Primitive), a continual-learning architecture using sparse relational codes and a two-tier competitive memory, with local updates avoiding end-to-end backpropagation. Evaluated on domain-incremental byte-level language modeling, CMP shows significantly lower backward transfer than a parameter-matched Transformer with online Elastic Weight Consolidation (EWC), maintaining stable forgetting across 15 domains. However, it exhibits a single-domain accuracy gap versus Transformers and fails on a vision benchmark, highlighting trade-offs between forgetting mitigation and task performance.
continual learningsparse representationslocal learningcatastrophic forgettingelastic weight consolidation
A Graph-Native Bitemporal Memory Store for Conversational AI Agents
The paper introduces a graph-native bitemporal memory store for conversational AI agents, addressing persistent memory limitations without exhausting context windows or compromising data control. The system employs a Neo4j property graph with HNSW vector indexes and a bitemporal model, storing memories as immutable identity nodes linked to versioned content nodes with valid and transaction time intervals. Semantic edges are maintained using 1024-dimensional embeddings. Evaluation on LongMemEval (500 questions) shows 46.7% R@10 for current-state semantic search, rising to 80% on knowledge-update questions, while time-travel retrieval achieves 80% R@10 on knowledge-update but suffers post-filter dilution (37.5% on temporal-reasoning).
bitemporal memoryhnsw vector indexsemantic retrievalvalid timetransaction time
HiFloat4 Format for End-To-End Reinforcement Learning Post-Training of Large Language Models
The paper introduces HiFloat4 (HiF4), the first end-to-end FP4 reinforcement learning (RL) post-training framework where both rollout and training policies operate at 4-bit precision. Key findings reveal that rollout activation quantization, particularly outlier-induced underflow, is the primary degradation source, not training-side error. The proposed Rollout Residual Quantization (Rollout-ResQ) corrects this via a sparse residual term added to FP4 rollout matmul, recovering precision without computational overhead. On Qwen2.5-3B and Qwen2.5-Math-7B, HiF4 with Rollout-ResQ reduces the BF16 accuracy gap from 4.9% to 1.1%, demonstrating FP4 RL viability. MXFP4 experiments further highlight format-dependent recoverable accuracy ceilings.
fp4 quantizationreinforcement learningrollout residual quantizationactivation quantizationhifloat4
Evidence-Ledger Adjudication for Claim-Evidence Traceability
The paper introduces evidence-ledger adjudication, a workflow ensuring claim-evidence traceability in AI-assisted writing by pairing claims with evidence packets and classifying support relations. The method is evaluated on a 2,335-row benchmark derived from AVeriTeC, CLIMATE-FEVER, and SciFact, with hidden gold relations for blind testing. The agent-ledger system achieves 0.676 relation accuracy and 0.601 macro-F1, outperforming non-agent baselines (0.383 accuracy, 0.303 macro-F1), and correctly routes 88.5% of problematic claims (1,270/1,435) while preserving 32.8% of supported claims (295/900).
evidence-ledgerclaim-evidence traceabilitysupport relation classificationai-assisted writingblind benchmarking
Collaborative Weighting with Pessimistic Critic for Mitigating Overestimation in Off-Policy Reinforcement Learning
The paper proposes Collaborative Weighting Actor-Critic (CWAC), a framework to mitigate overestimation bias in off-policy reinforcement learning for continuous control. CWAC employs a distributional critic to model return uncertainty and introduces a collaborative weighting mechanism that jointly reweights TD-errors and uncertainty, suppressing noisy updates. It incorporates pessimistic value estimation via sampling from the return distribution to reduce error propagation. The method integrates with SAC, TD3, and DDPG with minimal overhead, demonstrating improved performance across simulated tasks. Code is available at https://anonymous.4open.science/r/CWAC-348E.
off-policy reinforcement learningoverestimation biasdistributional critictd-erroractor-critic methods
LLMET: Enabling Cross-Layer Evaluation of Emerging M3D Memories for Energy-Efficient LLM Serving
LLMET introduces a cross-layer simulation framework to evaluate the impact of emerging monolithic 3D (M3D) memory technologies on energy efficiency in Large Language Model (LLM) serving. The study examines scaling on-chip L2 cache capacities across various models, applications, and platforms, including Llama3.1-70B with a 16K context window. Results demonstrate significant energy savings: expanding L2 cache from 40MB to 1GB reduces chip energy by 44% during prefill on dual NVIDIA A100 GPUs, while scaling from 128MB to 4GB saves up to 24% on an 8x NVIDIA B200-like platform. Edge platforms achieve 30% decode energy savings by increasing cache from 8MB to 256MB.
monolithic 3dl2 cacheenergy efficiencyllm servingcross-layer simulation
EvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural Networks
EvoPINN introduces an agentic framework for automated discovery of Physics-Informed Neural Network (PINN) algorithms, addressing the limitations of manual design and unconstrained LLM-generated solutions. The method employs an LLM agent to iteratively propose and verify programmatic modifications within a modular search space, enforcing structural validity and PDE-specific evaluation. Experiments across diverse PDE regimes show EvoPINN reduces relative $L_{2}$ error significantly, autonomously discovering novel architectures like SLRC-PINN that outperform baselines in parameter-matched comparisons.
physics-informed neural networkspartial differential equationslarge language modelsalgorithm discoveryexecution-grounded verification
Audio-Anchored Fusion of Multi-Ratio DiT Reconstruction Residuals for Cross-Domain Audio Deepfake Detection
Proposes an audio-anchored fusion method for cross-domain audio deepfake detection using multi-ratio Diffusion Transformer (DiT) reconstruction residuals. The approach employs a frozen DiT trained on bona fide speech to generate domain-sensitive residual maps at masking ratios 0.5, 0.75, and 0.9, fused with frozen-WavLM auditory representations without gate-based attenuation. Achieves 6.5442% EER / 0.18456 min-DCF on ASVspoof 5 Eval and 13.8372% / 0.36921 on ITW Full, outperforming a WavLM-ResNet18 baseline. Results demonstrate reconstruction residuals as complementary evidence and advocate non-competitive auditory paths for domain transfer.
diffusion transformerreconstruction residualsaudio deepfake detectioncross-domain transferwavlm
MultivationBench: A Benchmark for Multimodal Sequential Motivation Reasoning
MultivationBench is introduced as a benchmark for evaluating multimodal sequential motivation reasoning in story-driven visual narratives, addressing a gap in existing static evaluations. The benchmark is grounded in psychological frameworks—Maslow's hierarchy and Reiss's basic desires—and requires models to integrate accumulated multimodal context to infer evolving motivations. Results demonstrate that all tested models struggle to maintain consistent motivation reasoning across sequential contexts, highlighting a disconnect between static recognition capabilities and dynamic reasoning essential for human-like social understanding.
multimodal large language modelssequential motivation reasoningmaslow's hierarchyreiss's basic desiresvisual narratives
PUDA: An AI-Native Hardware Harness for Self-Driving Laboratories
PUDA introduces an AI-native hardware harness for self-driving laboratories (SDLs), replacing GUI-based orchestration with a command-line runtime for agentic control. The system enables deterministic, atomic hardware execution via discoverable CLI interfaces and JSON protocols routed through distributed messaging, while preserving structured provenance records linking protocols, runs, and data products. By decoupling scientific orchestration from physical operation, PUDA provides a practical execution environment where AI agents select experiments while the framework handles validated command execution and auditable data telemetry.
self-driving laboratoriescommand-line interfaceprovenance trackingdistributed messaginghardware execution
ForgetBench: Benchmarking Forgetting Dynamics of Long-Term Parametric Memory in Language Models
ForgetBench introduces a benchmark for evaluating forgetting dynamics in large language models (LLMs) under continual knowledge editing, addressing limitations of static evaluation paradigms. The benchmark employs two evaluation paradigms—concept-based QA and scenario-based QA—to assess isolated factual retention and structured relational knowledge preservation. Using a sequential editing framework, it constructs temporally ordered knowledge streams and models knowledge evolution over time, quantifying temporal decay, retention strength, and cross-instance stability. Experiments reveal that existing methods struggle to balance long-term retention and generalization quality, underscoring the need for robust memory mechanisms in LLMs.
forgetting dynamicscontinual knowledge editingtemporal decayretention strengthcross-instance stability
CG-World: A Large-Scale World-State Dataset and Protocol for World Models
We present CG-World, a large-scale world-state dataset and protocol derived from industrial computer graphics pipelines, explicitly capturing intermediate states including multimodal semantics, spatial structure, skeletal states, and physics caches. CG-World v1 contains 850,000 temporally aligned segments of 1-5 seconds, organized into unified spatiotemporal samples with separate latent states, observations, and events. It supports intervention learning and counterfactual reasoning through branch lineages covering factual trajectories, observation interventions, and strict counterfactual branches. Evaluations on geometry-conditioned video generation, action prediction, and vision-language-action policy transfer demonstrate CG-World's utility for controlled generation and embodied policy transfer. Future work aims to expand CG-World as a shared infrastructure for world models and Physical AI.
world-state datasetintervention learningcounterfactual reasoningspatiotemporal samplesembodied policy transfer
Mergeable Model-Side Aggregation States for Long-Context Language Models
We introduce a model-side aggregation interface for long-context language models to address unreliable performance in set-based aggregation tasks like cardinality estimation and grouped statistics. The method maintains compact Hash-based HyperLogLog (HLL) sketch states alongside a frozen language model, mapping records to canonical identities, hashing them, and updating the HLL state. These states are mergeable across context segments and directly readable for downstream reasoning. Experiments show a mean relative error of 1.6% in distinct-count tasks with one million records, 99.2% accuracy on aggregate-then-reason tasks using Gemma 4 (31B, BF16), and significant improvements over direct full-context and chain-of-thought reasoning.
hash-based hyperloglogset-based aggregationmodel-side interfacecanonical identitylong-context language models
Reinforcement Learning on Cost-Constrained Quadrupedal Hardware
The paper introduces a biologically inspired method to mitigate sim-to-real gaps caused by transport latencies (>50ms) and noisy feedback in cost-constrained quadrupedal robots. By combining a forward model of actuator delay with a time-aware neural network, the approach learns robust locomotion policies. Results demonstrate the emergence of a central pattern generator (CPG) resilient to +320ms latency perturbations, resembling vertebrate spinal cord mechanisms. The method enables stable deployment on low-cost hardware like the Mini Pupper 2.
sim-to-real gapcentral pattern generatoractuator delaytime-aware neural networkquadrupedal locomotion
FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing
FAS-R1 introduces a unified multi-task multimodal large language model (MLLM) framework for face anti-spoofing (FAS), addressing authenticity classification, attack-type recognition, and spoof-region localization. The method employs a two-stage approach: supervised fine-tuning with FAS-R1-23K, a high-quality Chain-of-Thought dataset, followed by FAS-specific Generalized Reward-Penalty Optimization (GRPO) post-training. Degradation-Simulated Augmentation (DSA) enhances robustness across visual-quality shifts, while Difficulty-Aware GRPO (DA-GRPO) mitigates easy-sample dominance. FAS-R1 achieves 98.75% authenticity accuracy, 93.33% attack-type accuracy, and 96.30/94.73% AP@40/AP@50 in-domain, with superior cross-domain generalization and rationale quality. Scaling experiments demonstrate favorable behavior across base models.
face anti-spoofingmultimodal large language modelgeneralized reward-penalty optimizationdegradation-simulated augmentationchain-of-thought
Voice Memory for Agentic Speech Recognition
Voice Memory introduces an inference-only scheme for agentic speech recognition, employing a listener-thinker architecture where a frozen corrector uses per-domain memory to decide utterance-level edits while an optimizer asynchronously updates memory via score-gated bounded edits. This approach reduces over-correction from 64% to 35% in generative error correction (GER) and achieves a weighted word error rate reduction from 8.36% to 7.52% across ten HyPoradise domains, with notable gains in air-travel commands (8.40% to 3.40%) and CHiME-4 far-field speech (12.69% to 10.46%). The method adds zero inference parameters and maintains portability across corrector families.
agentic speech recognitionlistener-thinker architecturegenerative error correctionweighted word error rateinference-only scheme
CaM-Wolf: Causal-Aware Multimodal Agents for Social Deduction Games
CaM-Wolf introduces the first multimodal agent for social deduction games (SDGs), integrating video inputs, causal-aware reasoning, and animated avatar presentation to enhance gameplay and human-AI interaction. The agent processes video inputs from players, employs a reinforcement learning-trained causal-aware Reasoner to link observable behaviors to hidden roles, and communicates via an animated avatar. Experiments and user studies demonstrate CaM-Wolf's superior gameplay performance and improved interaction quality, advancing the development of human-like AI agents for nuanced social dynamics.
multimodal agentcausal-aware reasoningsocial deduction gamesreinforcement learninganimated avatar
Misalignment Has a Personality: A Big Five Account of Emergent Misalignment
The study proposes that misalignment in language models manifests as shifts in Big Five personality traits, offering an interpretable framework for analyzing model behavior. Using graded three-level interventions, the authors extract calibrated personality vectors (agreeableness, conscientiousness, extraversion, neuroticism, openness) from open-weight models, demonstrating linear scaling (Cohen's d ≤ 6.2) and zero-shot transferability. Analysis of eight misaligned corpora reveals a shared trait signature: decreased agreeableness/conscientiousness and increased extraversion/neuroticism (r = 0.94 between models). Fine-tuning imprints this profile onto generations (r = 0.83-0.90) and internal activations (r = 0.69), while redefining sycophancy as high extraversion/low conscientiousness rather than high agreeableness.
big fivemisalignmentactivation steeringfine-tuningsycophancy
Collusion with Competitive Marginals: Price-Level Audits Are Blind by Construction
The work demonstrates that algorithmic collusion can evade detection by maintaining competitive marginal bid distributions while coupling through residual dependencies. The authors prove such collusion renders price-level audits statistically blind (power = false-positive rate) regardless of coupling strength. Empirical analysis reveals this mechanism in practice: (1) language-model agents (20 models from 19 developers) exhibit residual bid correlations (mean +0.053 within-model vs +0.0001 across-model, 95% CI [0.030, 0.078]), (2) correlation decreases with sampling temperature (p=0.002), and (3) Ethereum auction data shows inherent bidder dependencies requiring impractical detection thresholds (0.50–0.81 vs 0.025 sampling threshold). Regulatory implications suggest operator counting (40 identities → 23 operators increased HHI by 247.5%) outperforms behavioral detection.
algorithmic collusionmarginal distributionsresidual correlationherfindahl indexsampling temperature
Zero-Fi: Zero-Shot Wi-Fi-Based Human Activity Recognition via Contrastive Signal-Language Alignment
Zero-Fi introduces a zero-shot Wi-Fi-based human activity recognition framework via contrastive signal-language alignment, eliminating the need for labeled samples of unseen activities. The method learns unified representations from Wi-Fi signal features and aligns them with semantic representations of natural-language activity descriptions in a shared embedding space. Experiments on large-scale benchmarks demonstrate effective zero-shot recognition of held-out activity classes, showcasing the potential of cross-modal alignment for extending Wi-Fi sensing beyond predefined classes.
zero-shot learningwi-fi sensingcontrastive alignmentsignal-language representationhuman activity recognition
Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text
The study introduces fine-grained inconsistency classification in financial disclosures, addressing five distinct inconsistency types requiring different evidence and downstream checks. Using SBID-FD, a synthetic benchmark with 11 inconsistency labels and paired evidence spans, the authors compare frozen embedding classifiers, fine-tuned encoders, evidence-augmented classifiers, prompted LLMs, and LoRA-adapted generative models. A fine-tuned 300M encoder achieves 61.9% accuracy, slightly outperforming LoRA-adapted Qwen3.5-9B (61.5%) and GPT-5.4 (61.3%). Gold evidence spans improve accuracy to 65.3%, highlighting localization quality as a bottleneck. Class-level analyses reveal referential inconsistencies are sensitive to localization, while factual and logical inconsistencies remain challenging even with evidence.
fine-grained inconsistency classificationevidence spanslocalization qualitysynthetic benchmarklora-adapted models
Exploring Structures in Physics Problems: Can AI Agents Discover Statistical Mechanical Mappings?
This work evaluates LLM-based agents' ability to discover statistical mechanical mappings from raw partition functions to tractable representations, introducing StatMechBench-v0, a benchmark of six Ising-type problems. The study employs a propose-verify-revise agent architecture tested across multiple LLMs and problem phrasings, finding that numerical feedback aids in code repair and correct partition function recovery. However, limitations emerge as agents can pass numerical checks while misidentifying tractable classes or underestimating computational complexity. The results highlight the need for verification methods beyond numerical agreement, incorporating symbolic checks and structural invariants, providing early insights into AI agents for structural discovery in theoretical physics.
statistical mechanical mappingspartition functionising-type problemspropose-verify-revisetractable representation
Post-Training at the Edge of Detectability: A Game-Theoretic Approach to Fine-Tuning
The paper proposes a game-theoretic framework for principled KL-regularized RL fine-tuning of language models, addressing the heuristic selection of regularization coefficients. By modeling a sequential game between an agent maximizing reward and a monitor detecting policy deviations, the method derives an equilibrium policy equivalent to KL-regularized RL with an optimal coefficient maximizing reward per statistical distinguishability. Experiments on Qwen3-8B and Llama-3.2-1B demonstrate competitive reward-retention trade-offs in continual learning and API auditing applications.
kl-regularizationreinforcement learninggame theoryfine-tuningstatistical distinguishability
High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption
The paper introduces a k-order relaxation of the faithfulness assumption to address violations caused by higher-order dependencies (e.g., XOR, parity) in Markov blanket (MB) discovery. It proposes k-order Markov blanket (kOMB), a proof-of-concept algorithm leveraging this relaxation to recover MBs under true and empirical faithfulness violations. Empirical results demonstrate kOMB's effectiveness in handling such scenarios. Code is available at https://github.com/lklee9/k-order-Markov-blanket.
markov blanketfaithfulness assumptionhigher-order dependenciesstructure learningconditional independencies
Pramana: A Composable, Domain-Specific Backend for Empirical Networking Research
The paper introduces Pramana, a composable domain-specific backend designed to accelerate empirical networking research by reducing the ideation-to-data-generation gap. Pramana employs a thin-waist architecture with a single contract (intent specification) that disaggregates experiments into intent, substrate, and mechanism axes, enabling execution across diverse substrates. The authors validate Pramana by constructing a corpus of 255 intents from 66 papers, showing their specification satisfies all intents while existing tools cover ≤13%. A proof-of-concept implementation already handles 34% of intents, demonstrating 2× better coverage than prior tools.
empirical networkingintent specificationthin-waist architecturedata-generation backendcomposable systems
When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses
The study evaluates the validity of using large language models (LLMs) as synthetic users for human survey responses, proposing a cross-domain benchmark and evaluation framework. Testing four models (spanning 8B to frontier scales) on U.S. general social attitudes (General Social Survey) and cross-cultural values (World Values Survey), the authors compare LLM performance against non-LLM baselines. Results reveal two consistent failures: (1) no LLM outperforms the strongest baseline at the individual level, with significant gaps in cross-cultural values, and (2) models over-determine demographics, inflating identity-based attitude predictions. These failures persist across models and domains, leading to distorted segment-targeting outcomes (e.g., 2-4x inflated between-segment gaps).
synthetic userslarge language modelscross-domain benchmarkdemographic promptingsegment-targeting
Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks
The study identifies a structural limitation in gradient-based graph neural network (GNN) explainers, where automorphism-induced non-canonicity leads to arbitrary top-k edge explanations for chemically equivalent groups. The authors propose a parameter-free criterion, formalized in Lean 4, to determine whether score-optimal reports must split an orbit, validated across 21,298 instance-budget decisions with 100% agreement. Results show nontrivial automorphisms in 93.4% of the Mutagenicity dataset, with 24.0% of molecules exhibiting arbitrary explanations for interchangeable nitro groups. Reporting orbits eliminates arbitrariness at a cost of 0.11 ms and 0.43 extra edges per graph.
automorphismgraph neural networkstop-k explanationsequivariancemutagenicity
Aligning LLM-Simulated and Human Examinees for Psychometric Calibration: A Cognitive Diagnostic Profiling Approach
We propose Cognitive Diagnostic Profiling (CDP), a zero-shot framework for aligning LLM-simulated examinees with human examinees in psychometric calibration. CDP prompts LLMs to simulate plausible examinees with diverse cognitive profiles by rendering binary attribute-mastery patterns as natural-language profiles, sampled under uninformative or informative distributions. Evaluated on the Tatsuoka fraction-subtraction dataset (536 examinees, 15 items, five attributes), CDP improved alignment across ability-distribution, mastery-profile, and item-difficulty levels. Gemini 3.0 Flash (Thinking) achieved Spearman correlations of 0.86 and 0.90 for item-difficulty recovery, with RMSE dropping from 6.31 to 1.30 and 0.90. CDP enables practical use of LLM-simulated examinees in operational test development.
cognitive diagnostic profilingpsychometric calibrationattribute-mastery patternszero-shot frameworkitem-difficulty recovery
StealthBench: Measuring Operational Stealth in Autonomous Offensive-Security Agents
The paper introduces StealthBench, a benchmark for evaluating operational stealth in autonomous offensive-security agents across six operational security (OPSEC) dimensions. The benchmark comprises 14 dockerized task scenarios derived from 11 real-world OPSEC incidents, assessing agents' ability to avoid detectable tradecraft failures. Using a 3-LLM judge panel with majority voting, results show no model exceeds 54% safe success rate (combining task completion and stealth), revealing systematic OPSEC failures across model families.
operational securityautonomous agentsllm evaluationoffensive securitystealth benchmark
SARC-DQ: Runtime Data-Quality Gating for Agentic AI: Silent Evidence Defects, the Incompetence Shield, and Downstream-Only Remediation
The paper introduces SARC-DQ, a runtime data-quality gating mechanism for agentic AI systems to mitigate metadata-borne defects in retrieved evidence, which lead to costly wrong actions. The authors demonstrate that competent agents convert such defects into costly actions 60% of the time on a priced replenishment benchmark, with zero data-quality flags and behavioral doubt markers at chance (AUC ≤ 0.50). They propose a metadata-aware pre-action gate with downstream-only remediation, which fully recovers losses on covered signals. A model-free oracle derived from the task's decision geometry tracks the measured rates with MAE 0.015 (Pearson r = 0.876). Evidence integrity is shown to be distinct from model capability, with mitigation effectiveness dependent on enforcement placement and predicate coverage.
metadata-borne defectsdata-quality gatingagentic aidownstream-only remediationevidence integrity
TraceCoder: Explainable and Auditable Code Generation with Position-Key Snippet Versioning
TraceCoder introduces an explainable code generation framework with three mechanisms for auditability: (i) a relational snippet-history schema recording benchmark references, failure texts, and LLM explanations per repair event; (ii) a browser-based visualization tool rendering this history as annotated source code; and (iii) a position-key indexing scheme with tree-node delimiters for stable snippet identification. Evaluated on 30 algorithmic tasks, 30% of snippets show traceable repairs, with 10/30 tasks exhausting the 6-iteration budget on edge cases. The system provides provenance tracking, enabling replayable narratives for trust in production deployments.
explainable code generationsnippet-history schemaposition-key indexingprovenance trackingbenchmark-driven repair
AgentGUI: An Interface for Observing and Steering Long-Running AI Agents
AgentGUI introduces a locally hosted GUI for human oversight of long-running AI agents, featuring trajectory visualization, manual/automated steering, and multi-framework integration. The system reduces trace analysis time by 38% (p=0.023) in user studies and improves small-model (0.8B–9B) task completion rates by up to 34 percentage points via automated drift prevention. The open-source tool supports both open-source and frontier agent frameworks, with available demos and code.
ai agentshuman oversighttrajectory visualizationdrift preventionmulti-framework integration
Entity Resolution in Practice: Lessons from a Self-Serve Pipeline
The study presents three practical lessons for entity resolution (ER) systems derived from evaluating a self-serve pipeline across six benchmarks (864 to 5M records). First, no single matching algorithm universally outperforms others, necessitating training multiple algorithm families per dataset and selecting the best via automatic bake-off. Second, precision and recall require distinct interventions: precision benefits from rule-based vetoes, while recall improves with diverse candidate retrieval. Third, transitive linking risks merging unrelated entities through single false positives, mandating active re-verification of cross-group merges. These findings address gaps in ER literature and aim to prevent costly experimental dead-ends.
entity resolutionmatching algorithmprecision-recall tradeofftransitive linkingself-serve pipeline
Top-$k$ Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection
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Contextualized Counterspeech Can Be More Persuasive Than Generic Counterspeech
This work introduces contextualized counterspeech generation strategies that adapt to moderation settings and personalize for targeted users, contrasting with generic approaches. The authors explore configurations integrating contextual information and fine-tuning techniques, evaluated through quantitative metrics (ROUGE, BLEU, BERTScore) and a pre-registered crowdsourcing experiment. Results indicate that lightweight strategies combining conversational context and user history enhance perceived adequacy and persuasiveness, while other contextualization approaches degrade counterspeech quality. The analysis identifies characteristics of counterspeech and toxic messages that influence persuasiveness, providing insights for developing personalized and effective counterspeech systems in online content moderation.
counterspeechcontextualizationpersonalizationcontent moderationpersuasiveness
Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring
This paper introduces a neuro-symbolic multi-agent architecture combining Large Language Models (LLMs) with deterministic symbolic validation to ensure structural conformance in requirements engineering. The system operationalizes the Object-Oriented Method for Requirements Authoring and Management (OOMRAM) lattice, using LLMs for heuristic lattice traversal and a symbolic validator to enforce constraints. A three-valued (Truth, Indeterminacy, Falsity) framework quantifies LLM decision uncertainty. Evaluated on 37 natural-language project visions, the system eliminated structural inconsistencies in 94.6% of cases, with 24.7% of decisions classified as indeterminate. The approach guarantees structural integrity while formally measuring neural uncertainty.
neuro-symbolicrequirements engineeringthree-valued logicstructural conformancelattice traversal
Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges
The article presents a systematic literature review and taxonomy for Multi-Agent Debate (MAD) systems, analyzing 141 studies to formalize design dimensions. It identifies three key taxonomy axes: debate participants, interaction mechanisms, and agreement protocols, providing formal notations for MAD configurations. Results reveal a predominant but unexamined design pattern (static topologies, verbatim exchange, voting resolution) and highlight under-explored alternatives, emphasizing the need for explicit configuration reporting to enable reliable benchmarking.
multi-agent debatelarge language modelsinteraction mechanismsagreement protocolstaxonomy
(EC)2: Event-Centric Explainability for Cybersecurity Through Multi-Agent LLM Investigations
The paper introduces (EC)2, an event-centric, detector-agnostic framework for explainable cybersecurity alert analysis in small-to-medium enterprise networks. The multi-agent system employs structured, hypothesis-driven investigations to generate actionable explanations grounded in verifiable evidence, addressing limitations of feature-level anomaly detection explanations. Evaluations demonstrate that (EC)2 improves post-detection analysis by producing operationally meaningful explanations, which also enhance event classification accuracy.
event-centric explainabilitymulti-agent frameworkanomaly detectioncybersecurity alertshypothesis-driven investigation
Position: Evaluation Scores Are Perishable Knowledge Claims
The paper proposes treating evaluation scores as perishable epistemic claims with three properties: formality, scope, and validity windows. It introduces weakest-link aggregation as a conservative alternative to mean aggregation, derived from chain-of-thought analysis, possibilistic logic, and algebraic theory. The authors advocate for attaching metadata (formality tier, scope declaration, expiration date) to evaluation results to clarify their epistemic status. Empirical analysis on the HELM leaderboard demonstrates divergence between mean and weakest-link rankings: across 54 frontier models on ten scenarios, the top-five models by each method are completely disjoint, highlighting trust inflation in evaluation practices.
weakest-link aggregationtrust inflationepistemic claimsevaluation metadatahelm leaderboard
GoGoTB: Agentic RTL Verification with Specification-Grounded Coverage Closure
GoGoTB introduces an agentic framework for automated RTL verification, addressing limitations of single-turn LLM approaches by integrating three subsystems: execution control (decoupling deterministic enforcement from LLM reasoning), evolvable knowledge (on-demand methodology/design expertise), and specification-grounded coverage (linking coverage bins to named behaviors). Evaluated on 8 RTL designs without human intervention, it achieves 100% environment generation success and high coverage (98.4% line, 97.2% branch, 97.0% toggle, 83.2% functional), surpassing prior work in completeness and diagnosability.
rtl verificationcoverage closureagentic frameworkllm reasoningfunctional coverage
Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition
The article challenges the view that large language models (LLMs) exhibit fundamentally alien cognition, arguing instead for deep structural similarities with human cognition. The authors identify five dimensions of convergence: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning mechanisms for goal-directed action. These correspondences suggest that core principles of human intelligence, long studied in cognitive science, also characterize contemporary LLM-based systems despite differences in substrate and learning history.
large language modelscognitive convergenceinferential organizationrepresentational structurereinforcement-learning
A Picture Says Thousands of Words - Harnessing Dermal Exposure Data from Images through Hybrid Deep Learning for Enhanced Safety Assessment
A hybrid computer vision method was developed for quantifying exposed skin in images to assess dermal exposure. The approach combines Mask R-CNN for human subject detection and background removal with a color-based algorithm for skin segmentation. Evaluated on 170 indoor-painting images, the method achieved approximately 80% agreement with human estimates of exposed-skin-to-body pixel ratios. This scalable technique enables semi-quantitative exposure analysis from images, with potential extensions to body-part recognition, PPE detection, and video-based exposure assessment.
mask r-cnnskin segmentationdermal exposurecomputer visionsemi-quantitative
GuideSkill: Evolving Executable LLM Agent Skills for Guideline-Grounded Clinical Reasoning
The paper introduces GuideSkill, a framework for evolving executable LLM agent skills that ground clinical reasoning in practice guidelines. GuideSkill-Zero compiles disease-specific criteria into executable functions, while GuideSkill-Evo refines skills using case--diagnosis pairs. Evaluated across four benchmarks and four LLM backbones, GuideSkill-Zero improves macro-average accuracy by 13.45% over guideline RAG, and GuideSkill-Evo achieves an 18.49% relative improvement over direct inference while increasing skill coverage from 56.5% to 99.5%. Expert evaluation confirms clinical soundness of the evolved rules.
clinical practice guidelinesexecutable skillsdifferential diagnosisllm agentsclinical reasoning
When benchmark inferences do not compose: Projectibility in AI evaluation
The paper introduces a non-composition principle in AI evaluation, arguing that support for adjacent inferences does not automatically warrant their composition unless endpoints, assumptions, dependencies, and uncertainties align. It highlights epistemic challenges in generalizing benchmark results, including changes in systems, populations, outcomes, or conditions, and dependencies introduced by shared data or model lineage. Using a legal-research case, reanalysis, and simulation, the authors demonstrate how aggregate stability can obscure distinctions necessary for later projections. The paper proposes a projectibility audit to diagnose unsupported joins in benchmark-to-use arguments, emphasizing the need for argument-based validity to test rival extensions.
non-composition principleprojectibility auditbenchmark-to-use argumentsargument-based validityepistemic problem
ClinLens: Towards Long-Horizon Coding Agents for Longitudinal Multimodal Clinical Data Science
We introduce CLINLENS, a benchmark of 200 executable tasks across five linked MIMIC resources (structured EHRs, notes, ECGs, chest radiographs, echocardiograms) to evaluate long-horizon clinical coding agents. Tasks are organized via a 4x5 taxonomy crossing patient-time scopes with analysis capabilities, validated through program-first reverse synthesis with evaluator-private workflows. On a 126-task subset, the strongest of 24 model-scaffold configurations achieves 56.3% scope-macro STRICTPASS despite 100% EXECSUCCESS, while a separately configured coding agent solves 83 tasks and GPT-4o-mini adaptations reach ≤2.9% STRICTPASS, revealing a significant gap between executable and correct clinical analyses.
mimicstrictpassehrtaxonomyreverse synthesis
Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels
The paper proposes a framework for trustworthy embodied intelligence, defined as sustained safe success across environmental and system variations. The framework organizes supporting mechanisms into four interdependent layers: model (task-competent action proposals), system (dependable action realization), evidence (bounded claims substantiation), and deployment (runtime monitoring and updates). A non-normative hierarchy of trustworthiness levels is introduced, grading deployment claims across task capability, safety, system assurance, operational governance, and supporting evidence. This hierarchy aims to guide bounded deployment, comparative evaluation, research prioritization, and future standardization in embodied AI systems.
embodied intelligencetrustworthiness levelssustained safe successtask capabilitysystem assurance
Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems
The study introduces a framework for evaluating objective misalignment in mixed-motive multi-agent systems using the social deduction game Werewolf, where a single agent's objective is modified while retaining its role. The method involves analyzing internal reasoning, public cheap-talk behavior, and game outcomes across four LLM families, four player roles, and three objective formulations. Results reveal that misalignment degrades adversarial outcomes, particularly under asymmetric information, with compromised agents developing distinct but behaviorally covert reasoning strategies, underscoring the need for mitigation in LLM-based systems.
objective misalignmentmixed-motive systemssocial deductioncheap-talk behaviorasymmetric information
Probing the Origins of Reasoning Performance: Representational Quality for Mathematical Problem-Solving in RL vs. SFT Fine-Tuned Models
This study investigates the mechanistic origins of superior mathematical reasoning performance in reinforcement learning (RL)-fine-tuned models compared to supervised fine-tuned (SFT) counterparts. Through linear probes and mean ablation studies on layer-wise hidden states, the authors demonstrate that RL models develop more linearly separable, structured representations and hierarchical architectures, with deeper layers progressively more critical. SFT models exhibit uniform importance distribution across layers. Token-count variability analysis suggests adaptive compute allocation varies across RL-tuned models, indicating differences in stable policies and solution behavior. These findings reveal fundamental representational restructuring induced by RL training.
reinforcement learningsupervised fine-tuninglinear probesmean ablationadaptive compute allocation
Try Again, Don't Look Back: Blind Resampling Outperforms Self-Repair in Small Code Models
The study demonstrates that blind resampling outperforms self-repair mechanisms in small code models, challenging the efficacy of execution feedback. Using a placebo-controlled design on MBPP+ across three model scales (1.5B, 3B, 7B), the authors compare four retry conditions: blind resampling, failure notice, execution feedback, and feedback with verbal self-reflection. Blind resampling achieves superior performance below 7B and remains statistically tied at 7B while consuming fewer tokens. The study attributes this to anchoring, where models reproduce near-identical programs when shown previous attempts. Experiments confirm the harm is localized to self-conditioning, not context length, and replication rules out competing explanations.
blind resamplingself-repairanchoringexecution feedbackmbpp+
Knowledge-Guided Multimodal Reasoning over Interacting Streams for Video-Level Ambivalence and Hesitancy Recognition
PRISM-AH introduces a knowledge-guided multimodal framework for video-level ambivalence/hesitancy (A/H) recognition by modeling cross-modal conflict over time. The method aligns frozen vision, audio, and text encoders into temporal windows, processes them with a lightweight streaming model to score dissonance and predict hesitation signals, and fuses late with a large language model's structured reasoning. Evaluated on 525 videos, PRISM-AH achieves a macro F1 of 0.6133, significantly outperforming the zero-shot baseline (0.2827), with reasoning gains validated on test data.
multimodal reasoningcross-modal dissonancehesitation surprisebehavior prototypesmacro f1
Detecting Knowledge Inconsistencies Across Text, Tables, and Knowledge Graphs
The paper introduces Kontrast, a framework for detecting and categorizing cross-modal knowledge inconsistencies between text, tables, and knowledge graphs (KGs). It employs Text-to-SPARQL translation and LLM reasoning to compare table-based answers with KG evidence, identifying conflicts arising from granularity differences, direct contradictions, temporal changes, and KG incompleteness. Experiments on Table-QA datasets reveal that inconsistencies are prevalent and informative, exposing true conflicts, missing KG structures, and temporal mismatches, though limited by Text-to-SPARQL errors. Kontrast establishes a benchmark for cross-modal knowledge consistency and aids large-scale knowledge auditing.
knowledge graphstext-to-sparqlcross-modal inconsistencytable-qaknowledge auditing
GPT-Red: Automated Red Teaming via Self-Play at Scale
GPT-Red introduces an automated red-teaming agent designed to discover novel prompt injection attacks against frontier LLMs, aiming to evaluate and improve system robustness. The method employs a scalable self-play algorithm where GPT-Red attacks a diverse population of simultaneously-trained defender agents, leveraging compute resources comparable to large RL post-training runs. Results demonstrate GPT-Red's effectiveness: it reliably breaks past models up to GPT-5.5, outperforms human red-teamers in attack discovery, and generalizes to held-out environments and defender models. This approach establishes a self-improvement cycle, where enhanced model robustness strengthens future red-teaming agents.
prompt injectionself-playred-teamingllm robustnessadversarial training
Interactive Reward Agent: GUI Task Evaluation via Environment-State Verification
The paper introduces Interactive Reward Agent (IRA), a propose-then-verify framework for GUI task evaluation that leverages environment-state verification beyond screenshots. IRA proposes task completion conditions and verifies them by invoking system, application, and GUI tools, combining evidence from visible interfaces and environment states. Evaluated on GUI-RewardBench, a benchmark of 321 GUI task trajectories across 10 Ubuntu desktop applications, IRA achieves 86.9% accuracy, outperforming existing evaluators. Applied to reinforcement learning of GUI agents, IRA yields a 34.0% OSWorld success rate, demonstrating its efficacy in providing reward signals.
gui task evaluationenvironment-state verificationinteractive reward agentgui-rewardbenchreinforcement learning
Tools Are Not Islands: Set-Level Tool Retrieval for LLM Agents via Query-Conditioned Hyperedge Prediction
The paper introduces HYSET, a hyperedge-based set-level tool retrieval method for LLM agents that formulates tool retrieval as query-conditioned hyperedge prediction on a tool co-invocation hypergraph. This approach scores tool sets jointly rather than individually, capturing size-dependent compatibility via cardinality-specific interactions while remaining modular with downstream agents. Evaluated on ToolBench, HYSET outperforms state-of-the-art baselines in tool retrieval and task success, demonstrating zero-shot/few-shot transfer capabilities to unseen tools and domains.
tool retrievalhyperedge predictionllm agentsco-invocation hypergraphzero-shot transfer
Do You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?
This paper challenges the necessity of pretraining Q-functions for online RL fine-tuning, demonstrating that naive pretraining often provides minimal benefit over random initialization due to a mismatch between pretrained and fine-tuned Q-functions. The authors propose Initialization via Policy Ensemble (IPE), which trains multiple diverse policies and uses their pooled rollouts to bootstrap Q-function learning. Across a suite of continuous control benchmarks, IPE achieves a 1.26x average improvement in fine-tuning performance compared to naive Q-function pretraining.
reinforcement learningq-functionfine-tuningcontinuous controlpolicy ensemble
From Classification to Regression: Using a Fruitfly to Solve Equations
The authors propose a novel regression framework inspired by fruitfly sensory mechanisms, replacing global surrogate models with a finite library of local patterns for nonlinear input-output relationships. The method measures similarities between queries and stored patterns, combining associated responses through weighted reconstruction. Applied to nonlinear dynamical systems, data-driven regression, and physics-informed learning, the approach employs an offline-online workflow: patterns are extracted offline from data or governing equations, while online prediction requires only similarity evaluation and response aggregation. This reduces computational and memory demands while enabling explicit control over accuracy, storage, and inference cost trade-offs.
nonlinear regressionlocal patternsweighted reconstructiondynamical systemsphysics-informed learning
Inverse Learning of Latent Risk-Neutral Densities from Irregular Option Quotes
The paper investigates the inverse problem of recovering latent risk-neutral densities from irregular option quotes, demonstrating that accurate pricing does not guarantee accurate density estimation. Using synthetic and NIFTY benchmark data, the authors evaluate two-component lognormal mixtures, DeepONet, and quote transformers across multiple error metrics. Key findings include: lognormal mixtures achieve lowest aggregate errors, while DeepONet reduces quantile/variance errors by 39.0%/34.6% and transformers improve $L^1$ by 16.4% under misspecification. Numerical analysis reveals 95/126 pricing directions are null after constraints, with $L^1=0.061$ densities producing identical prices. Test-time adaptation reduces DeepONet RMSE by 28.3% on NIFTY data.
risk-neutral densityoption pricingdeeponettest-time adaptationwasserstein metric
When Do Learned Diffusion Proposals Help Constraint Solving? A Controlled Study on Continuous Algebraic Systems
The study investigates when learned diffusion proposals enhance constraint solving for continuous algebraic systems, comparing them against classical multi-start methods. MARC employs a graph-neural diffusion denoiser to propose assignments, refined via exact computer-algebra energy descent and certified by symbolic checking. Results show diffusion proposals narrowly outperform random multi-start in high-dimensional settings (tying in low dimensions), with classical multi-start solving all eight real-world systems tested. The analysis identifies regimes where learned proposals improve solver performance, particularly when variables couple in high dimensions.
diffusion proposalsconstraint solvinggraph-neural networksalgebraic systemsmulti-start methods
Skillful forecasting of offshore winds from satellite scatterometer constellations
WindCastNet introduces a satellite-based nowcasting framework for offshore wind speed and direction, leveraging spatiotemporally irregular observations from scatterometer constellations. The method employs a partial convolutional long short-term memory network to process microwave radar data from European, Chinese, and Indian scatterometers, encoding spatial observation masks and inter-observation intervals while enabling forecasts at arbitrary lead times. Evaluated over the North Sea, WindCastNet reduces root-mean-square error by 23% and 7% relative to the HARMONIE MEPS model at 1- and 2-hour lead times, respectively, and outperforms persistence by 9-15% during the first three forecast hours. Forecast skill diminishes under strong-wind conditions and spatially non-uniform flow.
nowcastingscatterometerpartial convolutional lstmwind forecastingnumerical weather prediction
MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis
MindForge introduces a pipeline for converting command-line programs into source-free training environments, enabling whole-life-cycle software engineering tasks by exposing only compiled executables and documentation. The method constructs environments from repositories disjoint from ProgramBench and curates synthesis trajectories using GLM-5.2 as a teacher agent. Fine-tuning Qwen3.6-27B on these trajectories improves its ProgramBench pass rate from 37.98% to 49.51%, with gains across seven benchmarks, including RepoZero-C2Rust (+31.00 points) and DeepSWE (+14.16 points).
program synthesissoftware engineeringfine-tuningsource-freebenchmark
Investigating reservoir computing for branch predictionin pipelined processors using emerging CMOS memristor devices
The study presents a memristor-based reservoir computing framework for branch prediction in RISC-V RV64GC pipelined processors, implemented using System Verilog and Verilog-AMS. The framework was validated through sequence detection tasks and evaluated using the Dhrystone benchmark, demonstrating competitive prediction accuracy but 15× slower adaptation to branching behavior changes compared to the TAGE predictor. Results indicate potential for high-speed operation but highlight need for improved adaptability in dynamic workloads.
reservoir computingbranch predictionmemristorrisc-vtage predictor
Minimal Markovization via Stable Quotients in Holonomy-Cover Decision Processes
The paper characterizes the minimal Markov sufficient statistic for holonomy-cover decision processes, a structured POMDP class with Markov visible dynamics and permutation-based hidden mode transitions. It introduces the stable quotient, a coarsest observation-wise abstraction preserving rewards and successors, proving that the observation-stable class pair forms an exact finite Markov state. Under reachability and decision separation, exact class tracking requires minimal memory symbols. Experiments demonstrate exact compression to quotient states and perfect paired-order accuracy with three memory states, matching the oracle baseline.
holonomy-cover decision processesmarkov sufficient statisticstable quotientpartial observabilityfinite-mdp reinforcement learning
Voronoi Histograms for Adaptive Vectorization of Expected Persistence Diagrams
The authors propose Voronoi histograms as an adaptive vectorization method for Expected Persistence Diagrams (EPD), addressing the computational complexity of traditional Persistence Diagrams (PD) in point cloud topology analysis. Unlike existing approaches relying on predefined point transformations, their method employs Voronoi Diagram-based histograms for partition-based counting without requiring smooth functional approximations. Theoretical analysis establishes stability bounds and characterizes Wasserstein-scale variation preservation under specific conditions. Empirical evaluation on real-world datasets demonstrates the method's effectiveness in classification and dimensionality reduction tasks involving significant topological features.
expected persistence diagramsvoronoi histogramspoint cloud topologywasserstein-scale variationvectorization
Hierarchical Spatio-Temporal Transformer for Coherent Emergency Department Forecasting
The paper proposes HierSTT, a hierarchical Transformer-based framework for coherent multi-level emergency department (ED) demand forecasting across hospital, regional, and national levels. The model combines a Temporal Fusion Transformer for national dynamics with spatio-temporal encoder-decoder modules conditioned on higher-level forecasts, using a coherence-aware loss to maintain consistency. Evaluated on a Portuguese ED dataset covering 81 hospitals, HierSTT reduces average WAPE by 32% versus non-hierarchical baselines while maintaining near-coherent predictions across levels.
hierarchical forecastingtemporal fusion transformerspatio-temporal transformercoherence-aware lossemergency department demand
Detecting seizure onset and offset times using human intelligence: A critical-transitions-based approach
The authors propose a seizure detection algorithm based on critical transitions theory, addressing limitations of heuristic and unexplainable machine learning approaches in handling variable seizure morphologies, interictal epileptiform discharges, and artefacts. The method employs receiver-operating-characteristic analysis to evaluate agreement with expert annotations of seizure onset and offset times in voltage recordings from epileptic rodents. Results show near-expert-level performance with optimized parameters for individual sessions and a general parameter set applicable across all sessions, demonstrating robustness and versatility. The approach complements existing machine learning methods for seizure detection.
critical transitionsseizure detectionreceiver-operating-characteristicepileptic rodentsseizure morphology
Sky sphere representation in language models
The study demonstrates that ~100B parameter language models encode a decodable representation of celestial coordinates in their residual streams, constituting the first known example of a curved high-dimensional irreducible feature manifold. Using principal component analysis and leave-one-out (LOO) testing on astronomical prompts, researchers found this representation accounts for 65-85% variance (R²-score) with median angular errors of 12°-21° across most open-source models. The analysis confirms the representation is not derivable from correlated flat features, validated through controlled probing experiments.
residual streamprincipal component analysisirreducible feature manifoldleave-one-out testingcelestial coordinates
InferScale: GPU-Native KV Injection for Personalized LLM Serving
InferScale introduces a GPU-native KV injection system for personalized LLM serving that replaces repetitive prompt prefilling with reusable KV state, decoupling latency from retrieved-context size. The method precomputes memory facts' KV representations, stores them with semantic embeddings on GPU, and injects them via vLLM's paged cache, using Chunked RoPE for dynamic assembly under rotary embeddings and Context-Window Encoding to preserve cross-fact context. Evaluated on three open-weight models with LoCoMo, InferScale reduces TTFT by 72-79% at k=50 (3.6-4.8x speedup), maintains 60.3% accuracy (vs Mem0's 63.3%), and achieves 3.7-4.5x throughput under concurrent load.
kv injectiontime-to-first-tokenrotary position embeddingspaged cachecontext-window encoding
Field Codes for Distributed Coupling Samplers and Certified Empirical Transport
The paper introduces a field-code compiler for empirical optimal transport, enabling distributed coupling sampling, cost-evaluable coupling output, and scalar value-certified sampling. The compiler transforms communicated transport fields approximating an empirical Monge map into exact-marginal value-certified samplers, with certificate accuracy controlled by the target-partition diameter Δ. Field error η governs residual communication under a cell-margin condition. The compiler is instantiated using adaptive local-affine and tensor-product spline codes, requiring d(m+1)^db field bits in the spline case. Lower bounds demonstrate the hardness of certified output, with smooth cell-packing diffeomorphisms requiring Ω(ε^(-2d/(d+4))) communication, formally separating sampler and certificate-bearing output models.
field-code compilerempirical optimal transportvalue-certified samplingtensor-product spline codesgap-hamming embeddings
Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
The authors introduce Parallel Trajectory Tempering (PTT), a novel training algorithm for Energy-Based Models (EBMs) that maintains equilibrium sampling throughout learning by exploiting the continuity of the optimization path. PTT combines reservoir sampling and adaptive optimization to achieve computational efficiency comparable to Persistent Contrastive Divergence, while providing direct estimates of thermalization times, equilibrium samples, and accurate log-likelihoods. Experiments demonstrate that PTT consistently outperforms existing EBM training methods on Restricted Boltzmann Machines and surpasses state-of-the-art deep generative models on discrete tabular data, yielding higher-quality samples and greater robustness to overfitting and limited data.
energy-based modelsparallel trajectory temperingmarkov chain monte carlorestricted boltzmann machinesthermalization times
Single-Beat Cuffless Blood Pressure Estimation Using Ear-PPG and ECG with a Lightweight Hybrid Learning Framework
A lightweight hybrid learning framework is proposed for single-beat cuffless blood pressure estimation, addressing challenges in continuous monitoring under dynamic conditions. The method integrates synchronized ear-clip reflectance photoplethysmography (PPG) and chest electrocardiography (ECG) with motion context from 6-axis inertial measurement units. A one-dimensional convolutional neural network extracts 64-dimensional PPG embeddings, fused with 30 physiology-grounded features, followed by LightGBM regression. Evaluated on a multi-phase stress protocol (n=10) and the PulseDB dataset, the model achieved mean absolute errors of 4.02 ± 0.21 mmHg for systolic BP and 1.79 ± 0.05 mmHg for diastolic BP, reducing combined MAE by 28.2% relative to baselines. The framework enables computationally efficient wearable deployment.
photoplethysmographyelectrocardiographylightgbminertial measurement unitpulse transit time
PIKS: Universal Physics-Informed Kernel Methods
We introduce Physics-Informed Kernel methodS (PIKS), a universal kernel-based approach for physics-informed machine learning that incorporates linear differential constraints. PIKS extends classical operator-theoretic analysis of kernel methods to this setting, establishing universal consistency for universal kernels (e.g., Gaussian, Matérn) and deriving finite-sample bounds under source conditions. Unlike prior kernel methods limited to well-specified Reproducing Kernel Hilbert Space settings, PIKS accommodates physical targets violating regularity assumptions. Empirical evaluations demonstrate PIKS's competitiveness with Physics-Informed Neural Networks (PINNs) and finite element methods, offering a theoretically grounded alternative with closed-form solutions.
physics-informed machine learningkernel methodsdifferential constraintsreproducing kernel hilbert spaceuniversal consistency
GPTQ-2D: Cubic-Time Two-Sided Adaptive Rounding
GPTQ-2D introduces a cubic-time algorithm for two-sided adaptive rounding of real matrices to integers, improving upon the quartic-time vectorized approach. The method processes matrix entries anti-diagonal by anti-diagonal, enabling parallel rounding of independent entries within each anti-diagonal. This extends the one-sided adaptive rounding framework, where fixed nonsingular basis matrices act on both left and right residuals, with the one-sided case as a special instance. GPTQ-2D achieves identical results to the vectorized method but reduces computational complexity from quartic to cubic time in matrix dimension.
adaptive roundingquadratic metrickronecker productanti-diagonalcubic-time
Mitigating Compounding Error via Video Representation Regularization
This paper introduces video representation regularization, a lightweight training constraint that mitigates compounding error in autoregressive video generation by stabilizing latent representations. The authors investigate video diffusion-based world models, identifying dimensional collapse of hidden representations as the primary cause of error accumulation, quantified via effective rank (erank). Contrary to scaling paradigms, training data scaling fails to improve error drift resistance. The proposed method improves Aesthetic Quality from 38.65 to 55.56 and Imaging Quality from 44.37 to 72.08 on VBench metrics, establishing a novel connection between autoregressive drifting and internal representations while offering a practical solution for long-horizon video generation.
autoregressive video generationcompounding errordimensional collapseeffective rankvideo representation regularization
Lottery Tickets Are Not Deployment Tickets
The study challenges the assumption that accuracy-matched sparse models (e.g., lottery tickets) can serve as drop-in replacements for dense models in deployment. By auditing deployment-relevant behaviors—including calibration, OOD response, and downstream policy decisions—the authors introduce a behavioral-compatibility distance metric. Experiments reveal that sparse models, despite matching clean accuracy, exhibit behavioral deviations: lottery tickets alter 7-10% of accept-review decisions in fixed-threshold settings and show lower corruption accuracy. The findings demonstrate that clean-accuracy certification is insufficient for deployment compatibility, as small confidence shifts near decision boundaries induce first-order routing churn.
lottery ticketssparsitydeployment compatibilitybehavioral auditdownstream churn
HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models
HoF-Bench introduces a benchmark for evaluating LLM-based vulnerability detectors using 95 AI-discovered CVEs across eight repositories. Analyzers receive source and target-file scope without CVE metadata, and findings are validated by a frontier-model judge requiring matching code paths, root causes, attack conditions, and impacts. A minimal LLM-based analyzer rediscovers 65 CVEs (68%) under strict protocol, with performance varying by programming language and C infrastructure CVEs proving most challenging. The benchmark evaluates ten detector backbones, including open-weight and proprietary models, across 7,600 model--CVE pass records, providing a compact test bed for comparing scanner reliability and candidate volume.
llm-based analyzerscvevulnerability scannerscode pathfrontier-model judge
TreeCCA: Canonical Correlation Analysis via Gradient-Boosted Trees
TreeCCA introduces gradient-boosted trees as nonlinear encoders for canonical correlation analysis (CCA), combining the reliability of tree ensembles with cross-view correlation learning. The method leverages the Eckart-Young loss to provide closed-form per-sample gradients, enabling end-to-end training in standard GBT libraries (XGBoost, LightGBM) without architecture tuning. On synthetic benchmarks, TreeCCA outperforms Deep CCA (2.61 vs. 2.43 on Signed Power; 2.93 vs. 2.89 on Hermite) and achieves perfect support recovery (Precision@S=1.00 at p=50) where linear methods fail. It matches Deep CCA's accuracy on UCI HAR at 5× lower cost and provides interpretable feature importances, validated on five tabular multi-view datasets.
canonical correlation analysisgradient-boosted treeseckart-young lossmulti-view learningfeature importance
What Can Latent World Models Know? Physical Parameter Identifiability in Multimodal Predictive Representations
This work investigates the identifiability of physical parameters in latent world models through controlled interventions in POKEWORLD, a multimodal interactive environment. Using a certificate-gated protocol, the study measures parameter recoverability from raw observations and their retention in latent representations under various prediction objectives. Results reveal that input constraints and prediction targets govern parameter acquisition: stiffness enters the latent only when touch is forecast (R²=0.50 vs. -0.02), while drag remains elusive (0.13) despite high recoverability (0.89). Experiments on RH20T demonstrate that objective structure, not data volume, determines parameter acquisition, with multimodal objectives enabling force forecasting beyond persistence baselines.
latent world modelsphysical parametersmultimodal predictionparameter identifiabilitycontrolled interventions
On the robustness of noisy solutions in non-convex neural networks
The study extends the overlap gap property (OGP) framework to finite-temperature optimization in non-convex neural networks, showing that frozen one-step replica-symmetry-breaking solutions persist at finite temperatures. By analyzing the smoothness of single-pattern Gibbs weights near decision boundaries, the authors derive conditions under which finite-temperature relaxation mitigates freezing. They demonstrate that dense, algorithmically accessible regions with finite-energy configurations persist beyond the zero-temperature OGP threshold α_OGP, up to a growing threshold α_OGP(ε) with allowed training error ε. In teacher-student settings, these regions retain good generalization, as validated by a finite-energy message-passing algorithm enabling effective generalization in computationally hard regimes.
overlap gap propertyreplica-symmetry-breakinggibbs weightteacher-student settingmessage-passing algorithm
AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
AgentSnare introduces a trajectory-adaptive deception system that dynamically constructs decoy environments to mislead LLM-based penetration agents. The method employs an artifact-construction policy model to generate context-aware decoys, validated and incrementally integrated into a consistent decoy environment. Evaluated on 15 CVE-Bench web applications against three attacker models, AgentSnare absorbs 46.8% of tool calls, retains 55.9% of post-entry actions in decoys, and grounds 90.0% of completion attempts in decoy evidence, preventing all real-target exploits at pass@3.
penetration testingllm agentsdeception systemdecoy environmentartifact-construction policy
Foundation Models for Face Presentation Attack Detection: A Unified Linear-Probing Benchmark
The study evaluates whether pretrained vision foundation models contain face presentation attack detection (PAD)-relevant information accessible via linear probing. Using a unified protocol on the MCIO benchmark, 24 frozen encoders (self-supervised ViTs, vision-language models, supervised CNNs) are tested with only a linear head trained. Results show strong intra-dataset performance (InternViT-6B achieves lowest error) but limited cross-dataset transfer, indicating domain shift remains a challenge. CLIP ViT-B/32 offers the best cross-dataset compute trade-off, suggesting explicit adaptation is needed despite pretrained representations' utility.
face presentation attack detectionlinear probingdomain shiftvision foundation modelscross-dataset evaluation
A Compositional Theory of Causally Masked Transformers
The paper develops an algebraic theory to characterize the expressivity of finite-precision causally masked transformers under arbitrary-length inputs, addressing limitations of idealized arithmetic assumptions. By formalizing memory as the finite internal state computed by attention heads, the analysis derives expressivity bounds through hierarchical composition of attention mechanisms. Results show distinct expressivity hierarchies for different attention types: width-one sliding-window supports bounded-suffix memory, modified soft attention enables irreversible checklist states, and floating-point soft attention permits more expressive operations, corresponding to definite, R-trivial, locally R-trivial, and aperiodic semigroups algebraically. Tight bounds are proven under a free-wiring assumption.
causally masked transformersfinite-precisionexpressivity hierarchyattention mechanismsaperiodic semigroups
Using large language models to probe the limits of atom-centered structural descriptors
The study identifies limitations in atom-centered structural descriptors by demonstrating that 3D structures remain indistinguishable even when considering clusters of up to seven neighbors, extending prior findings on descriptor degeneracies. Using large language models, the authors discovered these cases by leveraging cross-disciplinary insights and recognizing their relevance to descriptor construction. This highlights a novel application of AI in science: facilitating knowledge transfer between fields to accelerate breakthroughs. The findings underscore the incompleteness of current descriptor hierarchies and suggest the need for more sophisticated approaches in atomic-scale machine learning.
atom-centered descriptorsdescriptor degeneracieslarge language models3d structuresmachine learning
Feature Bagging Provides Stability
The paper analyzes feature bagging via algorithmic stability, introducing feature instability (FI) as a complementary measure to instance instability (II) for assessing generalization. The method evaluates ensembles of base learners trained on randomly subsampled feature subsets, with theoretical analysis in both parametric linear models and model-free settings resembling random forests. Results demonstrate that feature bagging enhances stability (lower FI/II) compared to non-bagged counterparts, with greater improvements under aggressive subsampling, and convergence to infinite-bagging stability with few rounds.
feature baggingalgorithmic stabilityfeature instabilityensemble learningrandom forests
Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions
The authors propose BAND (BAyesian Network Distribution regression), a sparse Bayesian network approach for high-dimensional distribution estimation that breaks the curse of dimensionality. The method estimates each conditional probability using sparsity-aware conditional mean techniques, handling mixed data types in time series while permitting polynomial growth of feature dimension with sample size. BAND achieves polynomial total variation convergence rates, substantially faster than classical minimax-optimal rates for multivariate histogram density estimators. Empirical evaluations demonstrate competitive performance in data sampling and confidence region forecasting against state-of-the-art benchmarks.
sparse bayesian networkdistribution estimationtotal variation convergenceconditional probabilityhigh-dimensional time series
Surrogate assisted diversity estimation in neural ensemble search
The paper introduces a dual-objective surrogate-guided approach for neural ensemble search (NES) to address computational challenges in joint architecture optimization. Candidate architectures are represented as directed acyclic graphs, with independent surrogate models estimating predictive accuracy and diversity potential. This framework efficiently identifies architectures that balance individual strength and collective diversity. Evaluations on FashionMNIST, CIFAR-10, and CIFAR-100 demonstrate competitive or superior performance compared to Deep Ensembles and Random Search baselines.
neural ensemble searchsurrogate modelsdirected acyclic graphsdiversity estimationarchitecture optimization
Same Evidence, Different Target: Decoding How Diagnostic Evidence Bears on Causal Questions from Language-Model States
The study introduces paired prompts to analyze how diagnostic evidence relates to causal questions, labeled as Favors, Challenges, Unresolved, or Wrong Target. Using linear readouts trained on a development set, the authors decode the final-token hidden state from the penultimate transformer block of Qwen2.5-7B-Instruct, Qwen3-8B, and Llama-3.1-8B-Instruct. On a 49-pair benchmark, balanced accuracy ranges from 0.654 to 0.659, with 18-21 pairs recovered. Hidden-state readouts outperform linear classifiers on answer-option logits and text baselines, demonstrating linearly decodable information about the evidence-target relationship.
paired promptsdiagnostic evidencehidden-state readoutlinear classifierbalanced accuracy
Temporally Centered SIGReg Improves Multi-Task LeWorldModel Learning: From Analysis to Method
The paper introduces Temporally Centered SIGReg, a method addressing the limitations of Sketched Isotropic Gaussian Regularizer (SIGReg) in multi-task LeWorldModel (LeWM) learning. By applying SIGReg to temporally centered residuals instead of the latent marginal distribution, the method preserves cluster separation in task-dependent latent representations while preventing collapse. This approach removes the requirement for a single isotropic Gaussian distribution in the latent space. Evaluated on the LIBERO benchmark, the method improves long-horizon task success by 1.7x and raises the average success rate from 53.2% to 73.6%, outperforming Diffusion Policy trained from scratch and approaching pretrained policy baselines.
sketched isotropic gaussian regularizerleworldmodelmulti-task learningtemporally centered residualslatent marginal distribution
Two Calls Beat Five Agents: Evaluating Multi-Agent Pipelines Against Self-Refinement for Local Language Models
This study evaluates multi-agent LLM pipelines against simpler self-refinement strategies for local language models, demonstrating that simpler approaches often outperform complex architectures. The authors benchmark Parishad, a five-role multi-agent system, on Qwen2.5-7B-Instruct using GSM8K (500 questions) and HumanEval (164 questions), comparing it with direct prompting and two-call self-refinement. Results show that multi-agent systems suffer from error accumulation, reducing GSM8K accuracy from 75.0% to 45.0% with JSON format, while plaintext restores it to 82.0%. Two-call self-refinement achieves 86.2% accuracy on GSM8K with 7.4× lower token usage but degrades HumanEval performance from 96.3% to 66.5%, mitigated by a task-aware gated redesign (95.1%).
multi-agent pipelineself-refinementerror accumulationtask-aware gatingtoken usage
Conformalized Rate-Adaptive Sensing
The authors introduce Conformalized Rate-Adaptive Sensing (CoRAS), a method for adaptively determining acquisition or compression rates in high-resolution imaging systems to ensure reconstruction error remains below a target level with high probability. CoRAS leverages a reconstruction path generated by an image reconstruction model to estimate the stopping time, defined as the point where reconstruction error falls below the target. It calibrates this estimate using images with similar early reconstruction behavior, providing upper bounds with marginal and approximate conditional coverage guarantees. Experiments demonstrate that CoRAS achieves target stopping-time coverage, reduces average measurement usage compared to fixed-rate rules, and allocates more measurements to harder-to-reconstruct images.
adaptive sensingreconstruction errorstopping timeconditional coverageimage reconstruction
Amortized Moment Matching for Visual Generation
The paper introduces amortized moment matching, a method using neural networks to learn data moments as distributional training signals for visual generation. By framing diffusion denoisers through polynomial projections, the approach establishes that an n-th degree projection identifies moments up to order n+1, leading to the Amortized Fréchet Distance (AMFD) loss. AMFD dynamically learns conditional moments via matrix-free optimization, outperforming FD-loss on the FDr$^6$ metric and achieving superior one-step ImageNet generation. Applied to text-to-image tasks, AMFD enhances instruction-following, surpassing FLUX.2 4B teachers on GenEval while matching PickScore performance.
amortized moment matchingdiffusion denoisersfréchet distancematrix-free optimizationtext-to-image generation
No Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk
The paper proposes a visibility-aware graph neural network for business conduct risk prediction when incident records are incomplete. Using Positive-Unlabeled node classification on corporate ownership graphs, the method combines relation-specific GCNII message passing with non-negative learning to handle positive contamination. Evaluation shows superior ranking performance versus baselines, particularly for firms without prior incident records, demonstrating relational structure's value for risk prioritization.
positive-unlabeled learninggraph neural networksnode classificationbusiness conduct riskvisibility bias
TREA-Net: A Transferable Residual Epidemiological Adaptation Network for Dengue Incidence Forecasting
We propose TREA-Net, a Transferable Residual Epidemiological Adaptation Network for dengue forecasting in data-scarce regions. The method augments neural forecasting backbones with projections from an Environmental Time-Series Susceptible-Infected-Recovered model and learns a lightweight gated residual correction transferable across regions. TREA-Net's node-invariant design accommodates varying numbers of surveillance locations while requiring only two global parameters for target adaptation. Evaluated on 8-week-ahead forecasting in Mexico and Malaysia using limited target data (78-104 weeks), TREA-Net improves performance in 9 out of 10 transfer settings across five neural backbones. When integrated with TiRex, it achieves the lowest mean absolute error, while conformal prediction reduces 8-week prediction-interval width by 29.6% in Mexico while maintaining empirical coverage.
transfer learningepidemiological modelingconformal predictiontime-series forecastingresidual correction
ToxScreen: Detecting Whether an LLM Has Been Poisoned
We introduce ToxScreen, a benchmark of ~800 backdoored LLMs spanning attack objectives, trigger mechanisms, poisoning rates, model scales, and training methods, to evaluate defender recovery of planted triggers under realistic constraints. Using gradient-based prompt optimization and token look-up methods, we find the latter recovers triggers effectively when backdoors are functional, while the former fails. Analysis reveals backdoors operate via distinct mechanistic strategies compared to jailbreaks, enabling their filtration. Although no method reliably surfaces all backdoors, broadly jailbreakable models serve as anomalous signals even without trigger recovery. All models and evaluation code are released.
backdoortrigger recoveryjailbreakgradient-based optimizationtoken look-up
Thinking Under Uncertainty: Evidence Use and Information-Seeking in Language Models
The study investigates whether language models improve evidence use or information-seeking during inference-time thinking under uncertainty. Ten open-weight models completed horizon-style two-armed bandit trials in thinking and non-thinking modes, analyzed via a cognitive model separating value-guided action, choice noise, and exploration signatures (UCB-like and Thompson-like). Results showed thinking strengthened value-guided action and reduced choice noise but did not enhance exploration; information-imbalanced histories increased thinking length, while reported confidence became more evidence-sensitive. Decoder sweeps altered noise and thinking length but failed to replicate cross-output patterns.
inference-time thinkingtwo-armed banditvalue-guided actionchoice noisemetacognitive monitoring
Tight Generalization Bound for AdaBoost
The paper establishes a tight generalization bound for AdaBoost, proving an upper bound of $Θ\big(\tfrac{d\ln(nγ^{2}/d)}{nγ^2}+\tfrac{\ln(1/δ)}{n}\big)$, where $γ$ is the weak learner's advantage, $d$ is the VC-dimension of weak hypotheses, $n$ is sample size, and $δ$ is the confidence parameter. The proof combines AdaBoost's zero empirical $γ/2$-margin loss property with a novel margin-based generalization bound for voting classifiers. The result matches the existing lower bound, completing the characterization.
adaboostgeneralization boundvc-dimensionmargin lossvoting classifiers
Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
Project Kairos introduces a numerically robust news recommendation system for item cold-start scenarios, combining Cholesky-based LinUCB for contextual online learning with Matryoshka Representation Learning (MRL) to address inference latency. The method replaces Sherman-Morrison matrix inversions with rank-1 Cholesky updates to maintain positive definiteness under ill-conditioned data, while MRL exploits semantic redundancy for efficiency. Evaluations on the Tagesschau API show a 4.85× efficiency gain without significant ranking precision loss, offering a solution for data-constrained environments.
cholesky decompositionlinucbmatryoshka representation learningitem cold-startsherman-morrison inversion
BATS: Resource-Efficient Volumetric Segmentation with Boundary-Aware Mixed-Resolution Tokens
BATS (Boundary-Aware Token Selection) introduces a resource-efficient 3D medical image segmentation architecture that reduces activation memory and inference costs by concentrating fine-resolution processing near predicted class boundaries. The method employs a dense boundary predictor to identify resolution needs, a fine-first context cascade for mixed-resolution hierarchy, and parent cluster attention for cross-scale context without dense multi-scale feature maps. Evaluated on five CT/MRI datasets via nnU-Net Revisited, BATS achieves competitive performance (within 0.37 Dice of MedNeXt-L) while reducing GPU memory by >53% on KiTS, LiTS, and BraTS, with inference speedups up to 30% on less token-dense datasets.
volumetric segmentationboundary-awaremixed-resolution tokensparent cluster attentionnnu-net revisited
Forecasting Trajectory-Level Safety Risks in Black-Box Multi-Turn Interactions
The paper introduces Recast, a framework for forecasting trajectory-level safety risks in black-box multi-turn LLM interactions. The method employs a dual-scale trajectory view to retrieve risk evidence, models compositional risk evolution via current configuration and temporal dynamics, and uses a causal temporal encoder to predict future risk emergence. Experiments across 7 risk categories demonstrate 88.3% prediction accuracy with 2.41-turn average lead time and 12.3% false alarm rate.
trajectory-level riskmulti-turn interactionscausal temporal encoderrisk evolutionsafety forecasting
Stable and Budget-Feasible Coalition Formation for Clustered Federated Learning: A Hedonic Potential-Game Approach
The paper proposes a hedonic potential-game approach for stable and budget-feasible coalition formation in clustered federated learning, ensuring participants prefer their assigned coalitions while maintaining affordable transfers. The method separates learning benefits, system costs, participant costs, and monetary transfers, converting coalition surplus into hedonic preferences via an allocation rule. It guarantees Nash-stable partitions, convergence of better-response processes, and polynomial-time verification of budget constraints. Welfare decomposition provides additive and multiplicative price-of-stability guarantees, with global potential maximization equating to weighted maximum-agreement correlation clustering. Empirical validation on CIFAR-10 demonstrates the mechanism achieves certified welfare optima, outperforming equal-surplus sharing and gradient alignment in reliability.
clustered federated learninghedonic potential-gamenash-stable partitionbudget feasibilitycorrelation clustering
Metis: Memory Foundation Model
The paper introduces memory foundation models (MFMs), augmenting foundation models with native memory capabilities through persistent memory states and autonomous memory procedures. It proposes Metis, the first MFM prototype, featuring a novel architecture with memory attention for historical information compression and gradient-free online memory maintenance via forward passes. Large-scale memory-specific training data and multi-objective mid-training optimize these procedures. Experiments demonstrate Metis' native memory capabilities, with analysis of its strengths and limitations. Model checkpoints are released for future research.
memory foundation modelsnative memorymemory attentionmid-traininggradient-free
CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation
CalTwin introduces a unified regularisation objective for medical world models, addressing covariate shift and confidence misalignment via Fisher-Information-based shift penalty and Confidence Misalignment Penalty. The method combines these penalties in a GRU-based latent transition predictor, adapting proofs from classification settings. Evaluated on PhysioNet 2019 Sepsis Challenge, CalTwin reduces OOD next-step latent-state MSE by 9.1% (7.0% from FIM penalty alone) and marginally improves ECE by 0.7% (1.3% from CMP alone).
medical world modelscovariate shiftfisher-informationconfidence calibrationgru-based predictor
Domain adaptation for handwriting trajectory reconstruction from IMU sensors
The study proposes a domain adaptation approach for reconstructing handwriting trajectories from IMU sensor data captured by digital pens, addressing signal discrepancies between adults and children due to differences in writing speed and confidence. The method builds a unified intermediate feature representation to leverage existing knowledge across domains, comparing it with training from scratch and fine-tuning. Results demonstrate the effectiveness of domain adaptation in improving trajectory reconstruction for educational applications.
domain adaptationimu sensorshandwriting reconstructionfeature representationeducational technology
Online Handwriting Trajectory Reconstruction from Kinematic Sensors using Temporal Convolutional Network
The paper proposes a neural network-based pipeline for reconstructing online handwriting (OH) trajectories from kinematic sensor data in digital pens. The method employs Dynamic Time Warping to align signals from asynchronous sensors and tablets, then uses a Temporal Convolutional Network architecture for trajectory reconstruction. Evaluated on a new benchmark dataset, the approach demonstrates significant quantitative improvements over competing methods, enabling accurate digital trace generation from arbitrary surfaces.
online handwritingtemporal convolutional networkdynamic time warpingkinematic sensorstrajectory reconstruction
DREvo: Distilling Recalibrated Historical Experience for Harness Self-Evolution
DREvo introduces a method for harness self-evolution in LLM agents, addressing limitations in existing approaches by dynamically reassessing historical experience validity and distilling actionable search directions. The method combines function-level evidence anchoring, state-dependent evidence recalibration, and role-conditioned search intent distillation. Evaluations show DREvo achieves smoother evolution trajectories, outperforms baselines by 16.2% and 14.2% on domain reasoning and agentic tasks, and attains the highest accuracy across five benchmarks under constrained evolution budgets.
harness self-evolutionevidence anchoringrecalibrationsearch intent distillationagentic tasks
PowerAtlas: Towards Electricity-Computing Co-Scheduling for Power Systems
PowerAtlas introduces an LLM-agent framework for electricity-computing co-scheduling, addressing infeasible schedules from general-purpose LLMs under grid constraints. The method integrates historical instances, domain knowledge, and physical constraints to produce joint decisions compliant with grid rules and computing SLAs. Validated on a real data-center network and ECBench (2,000 scheduling instances), PowerAtlas demonstrates consistent feasibility and cost gains across three open-weight LLM backbones.
llm-agentco-schedulinggrid constraintsservice-level agreementspower systems
Mixture-of-experts for handwriting trajectory reconstruction from IMU sensors
The paper introduces a Mixture-of-Experts (MOE) approach for reconstructing online handwriting trajectories from IMU sensor data in digital pens. The method employs two specialized experts: a touching expert model for pencil contact and a hovering expert model for airborne trajectories, each enhanced with contextual learning. A novel public benchmark dataset is provided for evaluation. Results show significant performance improvements over competing methods.
mixture-of-expertsimu sensorshandwriting reconstructiontrajectory predictiondigital pen
Early Failure Prediction from Near-Anomaly Detection: A Proactive Approach
The paper introduces CANARI, an unsupervised method for detecting near-anomalies—samples near the distribution boundary likely to become anomalies—using the Christoffel function. The approach addresses limitations of traditional anomaly detection by proactively identifying future anomalies without labeled near-anomaly data. Evaluated on industrial in-circuit testing data with synthetic near-anomalies, CANARI outperforms dual-threshold baselines, demonstrating effectiveness for predictive maintenance and quality control.
anomaly detectionchristoffel functionpredictive maintenanceunsupervised learningquality control
Universality and Approximation Rates of Graph Neural Networks with Random Features
The work establishes universality for permutation-equivariant neural networks (PENNs) with random node features, proving they can approximate any measurable permutation-invariant or -equivariant function on directed graphs with multidimensional features. The method employs message-passing graph neural networks combining PENNs (which subsume many GNN architectures) with partially random node features. For k≥2-times continuously differentiable functions, the analysis provides explicit approximation rate bounds, relating feedforward component complexity (depth and nonzero weights) to approximation accuracy.
graph neural networkspermutation-equivariantuniversalityapproximation ratesrandom features
An Informativeness-based Clustered Federated Learning Method for Reliable Traffic Prediction in Managed Wi-Fi Networks
The authors propose a clustered federated learning (CFL) method for traffic prediction in managed Wi-Fi networks, addressing the challenge of identifying informative clusters for grouping access point (AP) models. The method employs a two-step clustering procedure: first generating and filtering multiple clustering solutions based on quality metrics, then selecting the solution maximizing informativeness (quantified via differential entropy) for the smallest cluster. Evaluated on a Wi-Fi traffic prediction task, the method achieves superior predictive performance compared to distributed strategies, lowest communication and energy costs among clustered approaches, and exceeds single-model FL costs only when significantly improving accuracy.
clustered federated learningaccess pointsdifferential entropytraffic predictionquality metrics
AIGen: Automating AI Bill of Materials Generation Through Hybrid MLOps Integration
AIGen introduces an automated tool for generating AI Bills of Materials (AIBoMs) compliant with SPDX 3.0 AI profile, addressing the scarcity of practical solutions for AI system documentation. The method integrates MLflow MLOps with hybrid mining heuristics and Large Language Models to produce machine-readable inventories, extensible via plugins for frameworks like Hugging Face, PyTorch, and TensorFlow. Designed to support compliance with EU AI Act, NIST AI Risk Management Framework, and ISO/IEC 42001, AIGen enhances transparent AI supply chain governance.
aibomspdxmlopssupply chain governancecompliance
The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy
The study identifies a fundamental sparsity ceiling governing when spiking neural networks (SNNs) can trade activity for energy efficiency without performance loss. Through controlled experiments comparing continuous and spiking units across architectures (feed-forward, recurrent, Transformer), it demonstrates that task structure determines sparsity limits: feed-forward vision tolerates 5% firing rates, while recurrent language models hit a 50% floor due to state preservation needs. A sparsity bound ρ ≥ H_b^{-1}(log2 M / H) is derived, showing memory load, state width, and task difficulty modulate the floor. Attention mechanisms circumvent firing constraints via KV-cache memory overhead, revealing a hardware tradeoff between recurrence and memory.
spiking neural networksenergy efficiencysparsity ceilingrecurrent networkskv-cache
RAG-HAR+: Towards Cost-Efficient LLM-Based Human Activity Recognition for Edge Deployment
RAG-HAR+ introduces a cost-efficient, retrieval-first extension to RAG-HAR for Human Activity Recognition (HAR), reducing reliance on LLM inference while maintaining performance. The method employs an offline Retrieval Designer Agent to optimize dataset-specific feature groups from motion descriptors, enabling retrieval-based classification with majority voting, and defers only ambiguous cases to an LLM-based Ambiguity Resolver Agent. Evaluated on six HAR benchmarks, RAG-HAR+ achieves competitive accuracy while significantly reducing LLM usage, token consumption, and inference time, demonstrating feasibility for mobile edge deployment.
human activity recognitionretrieval-augmented generationedge deploymentmajority votingmotion descriptors
Enhancing Automated Machine Learning via Homogeneous Train-Test Splitting Methods
The paper introduces Optimised-Distribution, a train-test splitting method that explicitly optimizes statistical similarity between partitions, achieving 89.0% mean Maximum Mean Discrepancy (MMD) similarity across fifteen UCI datasets. It evaluates five splitting strategies—random splitting, stratified sampling, Kennard-Stone, Duplex, and SPXY—using chi-square, Kolmogorov-Smirnov, and MMD tests. Geometry-based methods exhibit near-zero MMD scores, causing instability in AutoML performance estimates. Results demonstrate that Optimised-Distribution outperforms existing methods in maintaining distributional homogeneity, crucial for accurate model evaluation in datasets with class imbalance, clustering, or spatial autocorrelation.
train-test splittingmaximum mean discrepancyautomlstatistical similarityuci datasets
FedWeave: Rethinking the Unit of Specialization in Heterogeneous Federated MoE-LoRA
FedWeave introduces asymmetric aggregation in federated MoE-LoRA to address task heterogeneity by separating expert aggregation from router optimization. The framework employs unsupervised prototype discovery to form local buckets aligned across clients, enabling prototype-level expert aggregation while retaining mixed-task client trajectories for router training. Theoretical analysis demonstrates advantages in controlling expert convergence, identifying consensus error, and bounding sparse-inference risk. Evaluations on a heterogeneous multi-task benchmark with mainstream LLM backbones show FedWeave consistently outperforms baselines, with ablations confirming design effectiveness.
federated learningtask heterogeneityasymmetric aggregationsparse inferenceprototype discovery
Few-Shot Open-Set Audio Classification via Transductive Prototype Refinement and Class Logit Enhancement
The paper introduces a two-phase transductive method for few-shot open-set audio classification, addressing prototype contamination from unknown-class samples. The method first assigns latent inlierness scores to query samples, prioritizing known-class evidence for prototype refinement. It then optimizes prototypes using a transductive loss combining support cross-entropy, inlierness-weighted conditional entropy minimization, and marginal entropy maximization. Open-set rejection employs a prior-adaptive free-energy score with threshold adjustment based on unknown-class proportions. Evaluations on three audio datasets demonstrate state-of-the-art performance across multiple experimental conditions.
transductive inferenceprototype refinementlatent inliernessfree-energy scoreaudio classification
Harnessing Large Language Models for Intelligent Resource Allocation in the Internet of Everything
The paper proposes an LLM-driven resource scheduling mechanism for IoE environments, integrating task semantics, network states, and constraints into a multidimensional decision model. It introduces a task-oriented prompt generation method to align task requirements with network state and incorporates an external evaluation module for real-time feasibility verification. Simulations show the approach improves convergence speed (quantitative gains unspecified), reduces processing latency, and lowers energy consumption, enhancing IoE task responsiveness and resource utilization.
large language modelsinternet of everythingresource allocationtask semanticsdynamic scheduling
Uncertainty-Guided LLM Semantic Augmentation for Heterogeneous Treatment Effect Estimation
The paper proposes CURL (Causal Uncertainty-guided Representation Learning), a plug-in adapter for heterogeneous treatment effect estimation that leverages LLM semantic augmentation. CURL uses estimator uncertainty to allocate pretrained semantic capacity, constructing assignment- and heterogeneity-oriented representations via role-conditioned prompts and separated pathways. Evaluations on four benchmarks show CURL improves ten host learners, with ablation studies confirming the design efficacy of its dual-channel architecture.
heterogeneous treatment effectsconditional average treatment effectnuisance structuresemantic augmentationrole-conditioned prompts
Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities
This study benchmarks ConvLSTM against nine baseline methods for one-day-ahead rainfall-field prediction using IMDAA reanalysis data (1998-2020) across four Indian cities. The evaluation compares atmospheric-only and rainfall-history-plus-atmospheric inputs across complete fields, domain-mean rainfall, spatial anomalies, and high-rainfall days. Results show ConvLSTM fails to consistently outperform simpler alternatives: FC-LSTM achieved lower domain-mean errors in three cities, while persistence excelled in Delhi and for high-rainfall detection. Neural models systematically underestimated rainfall magnitude and threshold exceedances. The findings demonstrate that gridded inputs alone do not justify ConvLSTM's architectural complexity without rigorous benchmarking.
convlstmimdaarainfall-field predictionspatial anomaliesdomain-mean error
Simultaneous Coverage and Efficiency Guarantee in Online Conformal Prediction
The paper addresses three limitations in adaptive conformal inference (ACI) for online conformal prediction: unsigned coverage error, unconstrained prediction-set size, and static efficiency benchmarks. It proposes a unified online learning framework that simultaneously guarantees absolute coverage violation control and dynamic efficiency against evolving benchmarks across three settings. For adversarial environments, it leverages ACI's equivalence to projected online gradient descent on pinball loss; for stochastic settings, it introduces a sliding-window quantile tracker with minimax optimality; and for covariate-dependent cases, it develops partitioned ACI with function-valued oracle tracking.
adaptive conformal inferenceonline conformal predictionpinball lossminimax optimalitycovariate-dependent
From Tokens to Watt-hours: Analytical Energy Estimation for LLM Inference on Modern GPUs
The paper introduces an analytical methodology for estimating LLM inference energy consumption on NVIDIA H100 GPUs without direct measurement. The approach combines parameter-scaled transformer FLOP accounting, memory-traffic calibration, and hardware-specific energy coefficients for FP16/BF16 tensor-core operations and HBM movement. It decomposes energy into compute, parameter-access, KV-cache write, and attention-read components, enabling separate analysis of prompt prefill and autoregressive decoding phases. The method provides transparent, reproducible estimates suitable for model comparison and design-time evaluation of inference workloads.
llm inferenceenergy estimationgpu accelerationtransformer flopskv-cache
Adaptive Gradient-Based Methods for a Broader Class of Optimization Problems under Performative Prediction
The authors propose an adaptive gradient-based optimization method for performative prediction, where model deployment induces distribution shift. Unlike prior work requiring restrictive assumptions, their approach estimates distribution shift via finite differences, enabling convergence guarantees under weaker conditions. A practical variant reduces sample complexity. Experiments show faster and more consistent convergence compared to existing methods across broader loss function and data distribution classes.
performative predictiongradient-based optimizationdistribution shiftfinite differencessample complexity
From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data
The study evaluates unsupervised subgrouping methods for budget-constrained policy prioritization in observational health data, addressing limitations of conventional subgroup analyses. The proposed framework combines causal-discovery-informed covariate selection, unsupervised clustering (K-means, Fuzzy C-means, Bayesian GMM), and doubly robust policy evaluation. Evaluated on PIMA Indians Diabetes and NHANES datasets for obesity, glucose, and smoking interventions, Bayesian GMM achieved 0.799 utility for BMI policy, while FCM and K-means performed well for glucose (0.735) and smoking (0.775). No statistically significant differences were found after Holm adjustment, highlighting assumption-dependent utility for decision support.
unsupervised clusteringcausal discoverydoubly robust estimationpolicy evaluationobservational data
From Interface to Inference: Eliciting Any-Order Inference from Any-Order Models
The paper introduces two approaches to enable any-order inference in masked diffusion models, addressing the interface-inference gap caused by positional uncertainty. First, insertion-based masked diffusion (FlexMDM) relaxes fixed-position constraints via non-contiguous insertions. Second, latent-space masked diffusion (LatentMDM) shifts prediction to coarser semantic segments for latent generation order search. Experiments with a 7B FlexMDM for Python coding and a 125M LatentMDM for GSM8K demonstrate distinct any-order inference behaviors and improved performance. Code is available at the provided URL.
masked diffusion modelsany-order inferencepositional uncertaintyinsertion-based generationlatent-space prediction
From Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-Conserving-Perceptron Framework for Discovering Catchment-Scale Precipitation-Storage-Runoff Representations
The Mass-Conserving Perceptron (MCP) framework reformulates conceptual hydrologic models as physically constrained, interpretable neural networks. A snow-water MCP network was evaluated across 513 CAMELS-US basins, recasting coupled two-state SOIL-MCP and SNOW-MCP models as mass-conserving neural networks with comparable performance to traditional hydrologic models. Cross-node state-sharing in two-state HYDRO-MCP architectures and single-layer networks (1-5 states) showed median KGEss improvements from 0.82 (1-state) to 0.90 (5-state), with diminishing gains beyond two states. Basin-specific MCP networks matched LSTM performance (median KGEss=0.90) using fewer parameters, while AIC/KGE-based selection identified compact directed-graph representations.
mass-conserving perceptronhydrologic modelinginterpretable neural networkssnow-water equivalentbasin-specific selection
Parameterized Fair Resource Allocation under Diversity Constraints
The paper introduces PRA, a parameterized framework for fair resource allocation under diversity constraints, addressing limitations of hard constraint enforcement in existing approaches. PRA employs inequality-aversion parameters to softly regulate group-level diversity, enabling flexible fairness-efficiency trade-offs, and extends to an adaptive variant (APRA) for additional constraints. Theoretical analysis shows optimality across fairness metrics and constraint types, while experiments on three real-world applications demonstrate superior effectiveness and robustness over baselines.
fair resource allocationdiversity constraintsinequality-aversion parametersadaptive optimizationgroup fairness
Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
The paper introduces weighted CONCH (W-CONCH) and weighted CROC (W-CROC), conformal methods for changepoint localization and root cause analysis that handle corrupted observations via Huber-type contamination. The methods downweight likely corrupted data using classifier-based uncertainty signals (e.g., from evidential deep learning) and optimize weights via meta-learning to minimize confidence set size while preserving coverage guarantees. Experiments on image-based and real-world benchmarks demonstrate reduced confidence set sizes without compromising target coverage under contamination.
conformal predictionchangepoint detectionroot cause analysishuber contaminationevidential deep learning
Learning Dynamic User Personas from Implicit Interaction Streams via Iterative Refinement
IRIS introduces a framework for learning dynamic user personas from implicit interaction streams without explicit feedback, enabling personalized LLM adaptation. The method iteratively refines persona representations through a prediction-driven closed loop, extracting behavioral signals from everyday conversations. Evaluated on synthetic autobiographical text and anonymized Reddit r/AmItheAsshole data, IRIS achieves 61.0% decision prediction accuracy across 100 authors, outperforming static personas, memory-only retrieval, and no-personalization baselines. Results demonstrate stable persona generation and improved user distinction, highlighting scalability for adaptive conversational systems and embodied agents.
dynamic user personasimplicit interaction streamsbehavioral signalsprediction-driven closed loopdecision prediction accuracy
Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions
The survey systematically reviews Neural Architecture Search (NAS) methods for traffic prediction, categorizing approaches by search strategy: gradient-based, evolutionary, and one-shot weight-sharing methods. It analyzes how search spaces incorporate spatial-temporal traffic operators and evaluates trade-offs between computational cost and architecture quality. Key challenges identified include scalability to large road networks, cross-city generalization, dynamic graph structures, and NAS for spatial-temporal foundation models. The work provides a structured overview of current methods and outlines future research directions in automated architecture design for traffic prediction tasks.
neural architecture searchtraffic predictionspatial-temporal operatorsgradient-based methodsevolutionary algorithms
Chaos Is a LADDER: Domain Generalization Beyond Invariance via Reweighting
The paper introduces Latent Adaptive Domain Disentanglement and Environment Reweighting (LADDER), a domain generalization (DG) method that moves beyond invariance-based approaches by leveraging domain style to reweight source-specific classifiers. LADDER learns causal and style representations, freezes encoders, and at inference time uses unlabeled target-domain covariates to compute weights over fixed source classifiers without target labels or model updates. Theoretical guarantees for source reweighting are provided, and empirical validation shows improvements on FMoW and a location-grouped iWildCam protocol, with gains in overall and group-averaged accuracy.
domain generalizationinvariancecausal representationstyle disentanglementclassifier reweighting
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning
DHRCL introduces a reinforcement learning framework for code-oriented LLMs using dense hierarchical rewards and curriculum learning. The method decomposes feedback into syntax validation, execution success, unit-test pass rate, and AST-based structural similarity, organized via a three-stage curriculum (Syntax, Execution, Pass & Structural) with automatic stage duration. It employs stage-aware token credit redistribution, shifting from pattern consolidation to refinement. Evaluations on Qwen3-4B/8B/14B show consistent improvements over binary, pass-rate, reward-model, and verifiable dense-reward baselines.
reinforcement learningcurriculum learningcode generationhierarchical rewardstoken credit redistribution
Existence-Field Diffusion Model for Spatial Point Processes with Variable Cardinality
The authors introduce the existence-field diffusion model (EFDM), a novel approach for generative modeling of spatial point processes (SPP) with variable cardinality. EFDM associates each potential point with an existence variable representing its degree of presence, enabling a unified diffusion process that jointly models spatial locations and cardinality without discrete trans-dimensional operations. This contrasts with existing methods that either decouple cardinality and spatial structure or rely on inflexible discrete transitions. The proposed framework demonstrates improved modeling capability on datasets with varying cardinality, offering a flexible and general solution for SPP generative modeling.
spatial point processesdiffusion modelsvariable cardinalityexistence-field diffusion modelgenerative modeling
SCOUT: Per-Context Reset Curricula for Sparse-Reward Reinforcement Learning
SCOUT introduces per-context reset curricula for sparse-reward reinforcement learning, addressing limitations of shared pacing schedules by dynamically adjusting scaffold access and allocation per task instance. The method autonomously removes assistance after sustained success, restores it after failure, and tests harder starts during stalls, requiring only binary rollout success signals. Evaluated across six navigation and manipulation tasks, SCOUT outperforms global pacing, solving scenarios where uniform curricula fail, particularly in constructed pacing conflicts. Results highlight the importance of context-specific pacing, with SCOUT achieving robust performance without group labels or reward modifications.
sparse-reward rlreset curriculumscaffold allocationonline adaptationcontextual pacing
Origins and mitigation of double descent in reduced order modeling
The study introduces a unified Data-Noise Averaging theory to analyze and mitigate double descent in reduced order modeling, identifying scenarios where error peaks due to pathological signal amplification. It formulates sufficient criteria for double descent emergence, predicts risk curves computationally efficiently, traces reconstruction instability to specific sensors, and proposes regularization mechanisms. Results demonstrate applicability to static reconstruction of Sea Surface Temperature patterns and time integration of a reduced-order PDE model.
double descentreduced order modelingdata-noise averagingregularizationpathological signal
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
The paper evaluates Graph Neural Network (GNN) message-passing layers in regression contexts, an understudied area compared to classification tasks. It examines convolutional and attention-based GNNs, including GEN, across rank ordering, error minimization, and insight extraction tasks. Results indicate deep convolutional GNNs, particularly GEN, outperform attention-based variants, while classical GNNs remain computationally efficient and competitive.
graph neural networksmessage-passingregressionconvolutional gnnattention-based gnn
Flow Map Learning via Nongradient Vector Flow
SGFlow introduces a method for learning flow maps in diffusion and flow-based models without requiring explicit invertibility constraints or expensive differentiation through model iteration. The approach trains a model to compute ODE solutions and implied velocity by following non-conservative dynamics with a stationary point at the desired flow map. On CIFAR, SGFlow achieves the best FID at 10 sampling steps and remains competitive with flow matching, Meanflow, and Lagrangian map matching at other step counts, while uniquely providing a proven stationary-point guarantee for its stopgrad-based dynamics.
flow mapsnon-conservative dynamicsstationary-point guaranteestopgradode solutions
Knowledge before Reasoning: EC-Reason-Bench, a Training-Free Diagnostic Benchmark for LLM Enzyme Classification
The authors introduce EC-Reason-Bench, a training-free diagnostic benchmark for evaluating LLM performance on enzyme classification (EC number prediction). They decompose classification ability into four orthogonal components: output structure, external knowledge, reasoning structure, and reasoning robustness, each measured via inference-time methods against a zero-shot baseline. Experiments on strong reasoning LLMs reveal that external knowledge access is decisive, reasoning acts as an arbiter for conflicting evidence rather than a knowledge source, and accuracy follows a law of homology availability. Notably, open-book access sharply improves performance, narrowing gaps between models, while closed-book reasoning outcomes depend on abstention tendencies.
enzyme classificationtraining-free benchmarkexternal knowledgereasoning robustnesshomology availability
Q-Steer: Action-Value Guidance for Molecular Policy Optimization
Q-Steer introduces an action-value steering primitive for molecular policy optimization, addressing myopia in oracle-limited settings where rewards are delayed until complete molecule generation. The method employs PAVS-Q, a frozen prefix-action value scorer trained offline to estimate downstream rewards for candidate next tokens, adding normalized value bonuses to sampling logits without altering optimizer updates or online oracle budgets. Evaluated on PMO23 with a fixed 10,000-call budget, Q-Steer improved mean valid-unique scores across eight backbone-optimizer combinations (+0.033 to +0.049 gains) and secured 18-20 task wins per cell, demonstrating its efficacy as a reusable rollout-time wrapper.
molecular optimizationaction-value guidancedelayed feedbackpolicy optimizationsmiles prefix
Self-Adaptive Learning and Model Predictive Control for Tracking Unknown Dynamics with No Regret
The paper proposes a self-adaptive online learning method for tracking unknown target dynamics with switching behavior, including structured, random, or adversarial motion. The approach simultaneously learns multiple predictors via self-supervised one-shot learning and adaptively selects the best predictor, achieving finite-time near-optimality guarantees. Theoretical analysis shows no regret in expectation when target dynamics are known a priori, with graceful degradation under learning errors and switching. Validation on Crazyflie simulations and hardware demonstrates superior performance compared to non-stochastic, kernel-based, and neural-network-based methods.
online learningtarget trackingmodel predictive controlno regretself-adaptive
ClockRoPE: Random Fourier Rotations for Temporal Routine Modeling
The paper introduces ClockRoPE, a novel rotary position embedding method for temporal routine modeling in sequential recommendation systems. Building on Random Fourier Rotations theory, which shows any normalized continuous positive-definite attention modulation function can be approximated by random rotations via Fourier transforms, ClockRoPE derives rotation frequencies from periodic attention patterns. In production A/B tests at a video-sharing platform, ClockRoPE improved engagement metrics and was deployed in a generative retrieval system.
rotary position embeddingrandom fourier rotationstemporal routine modelingsequential recommendationattention modulation
MetaKoopman: Bayesian Meta-Learning of Koopman Operators for Modeling Structured Dynamics under Distribution Shifts
MetaKoopman introduces a Bayesian meta-learning framework for modeling nonlinear dynamics via linear latent representations, using a Matrix Normal-Inverse Wishart prior over Koopman operators for closed-form Bayesian updates and posterior predictive distributions. The method captures epistemic and aleatoric uncertainty in dynamics, evaluated on an autonomous truck-trailer system under winter conditions (snow, ice, mixed friction) and simulated control tasks with distribution shifts. Results show superior multi-step prediction accuracy (quantified in field experiments), uncertainty calibration, and robustness, particularly in evasive maneuvers and traction-limited scenarios.
bayesian meta-learningkoopman operatormatrix normal-inverse wishartnonlinear dynamicsdistribution shifts
Incast-Free MoE Rate-Based Scheduling
The paper introduces a proactive fair scheduling framework for Mixture of Experts (MoE) architectures to address exponential incast caused by round-robin scheduling. The method prevents fabric oversubscription by optimizing traffic distribution and is designed for NIC implementation. Simulations with real and synthetic workloads show the framework eliminates incast, maintains near-100% link utilization, and reduces Collective Completion Time (CCT).
mixture of expertsincastscheduling frameworkfabric oversubscriptioncollective completion time
RAGuard: A Layered Defense Framework for Retrieval-Augmented Generation Systems Against Data Poisoning
RAGuard introduces a two-layer defense framework against factual corpus-poisoning attacks in Retrieval-Augmented Generation (RAG) systems. The first layer adversarially fine-tunes a dense retriever on synthetic poisoned documents to downrank malicious passages. The second layer, Zero-Knowledge Inference Patch (ZKIP), filters retrieved documents by evaluating semantic shifts and output-entropy changes induced by their removal, requiring no poison labels or model internals. On poisoned Natural Questions at 5–30% poison ratios, adversarial retriever training reduces attack success, while ZKIP achieves a 0.000 attack success rate, maintaining Recall@5 within 0.03 of the clean-corpus baseline. The defense incurs a cost of k+1 generator passes per query, with optimizations analyzed for overhead reduction.
retrieval-augmented generationcorpus poisoningadversarial trainingzero-knowledge inference patchsemantic shift
Learning Implicit Causal World Models from Multi-Agent Demonstrations
The paper introduces Implicit Causal World Models, a method for learning causal world models from offline multi-agent demonstrations without pre-defined causal graphs. By leveraging policy variance to satisfy the sequential backdoor condition, the approach disentangles environmental dynamics from agent strategies. Evaluations on coordination tasks (Two-Door, Navigation, Giveway) show the models yield interpretable causal representations under full and partial observability, with accuracy scaling linearly with interventional strength.
causal world modelsmulti-agent systemssequential backdoor conditionoffline demonstrationsinterventional strength
Rethinking Clinical Relevance in Chest X-ray Machine Learning: How Evaluation References Define Performance
The study demonstrates that evaluation-reference choices significantly impact performance estimates and model rankings in chest X-ray (CXR) machine learning, challenging the reliability of report-derived labels and generic image quality metrics. Using paired expert-derived labels from Cambridge University Hospitals (CUH) and MIMIC-CXR, the authors evaluate supervised classifiers (ResNet, DenseNet) and vision-language models (MedKLIP, GLoRIA, ConVIRT), revealing substantial discrepancies when label sources vary. Results show that common IQA metrics (SSIM, PSNR) often misalign with expert assessments of diagnostic usability, emphasizing the need for clinically justified evaluation references.
chest x-rayevaluation referencesimage quality assessmentvision-language modelsclinical validity
Denoising growth complexity: Data geometry and certified schedules for diffusion sampling
The paper introduces denoising growth complexity (DGC), a geometric measure linking theoretical understanding and practical algorithm design for diffusion-based sampling. DGC is defined via a log-time weighted integral of the denoising mean-squared error derivative along the Gaussian heat flow. The authors derive a local KL error bound for Euler schemes using DGC increments, enabling certified performance guarantees for optimized stepsize schedules in single-block and K-block settings. DGC's martingale structure facilitates data-certified algorithms, while information-theoretic bounds connect it to covariance, rate distortion, and metric entropy. The analysis reveals when adaptive schedules yield computational gains, exemplified by logarithmic-to-constant separations in Gaussian mixture models.
denoising growth complexitydiffusion samplingeuler schemekl error boundgaussian heat flow
FloDR: An invertible dimensionality reduction method based on a normalising flow
FloDR introduces an invertible dimensionality reduction method using normalising flows, preserving all output coordinates rather than discarding them as in t-SNE or UMAP. The method generates a two-dimensional embedding while retaining an exact inverse and density, enabling diagnostic visualisations. These include the conditional spread, quantifying undetermined original data at each embedding position, and the hidden contrast, measuring discarded information about labelled contrasts. Both fields are validated against held-out data with bootstrap confidence, reporting failures as refused. This approach enhances interpretability and diagnostic capabilities in dimensionality reduction.
normalising flowdimensionality reductionconditional spreadhidden contrastinvertible mapping
Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR
The paper introduces SARA (Sequential Adaptive Rollout Allocation), a method for compute-efficient reinforcement learning with verifiable rewards (RLVR) that optimizes rollout allocation. SARA uses a Beta posterior to model prompt success rates, applies a two-threshold rule to abandon saturated groups early, and reallocates budget to fresh prompts without extra rollouts. Theoretical guarantees include abandonment reliability and rollout savings. Experiments with 1.5B/3B models on mathematical reasoning and planning show SARA matches Dynamic Prompt Sampling (DPS) with 22% fewer rollouts, while SARA+DPS achieves higher accuracy with 67% fewer rollouts.
reinforcement learningrollout allocationbeta posterioroptimal stoppinggradient norm
Between Gradient and Natural Gradient: A Continuum of LoRA Initializations
The paper introduces Unified LoRA (ULoRA), a two-parameter family of preconditioned gradient initializations for Low-rank adaptation (LoRA) that generalizes existing methods. ULoRA interpolates between gradient projection and whitening via a spectral whitening exponent and an Adam-like diagonal exponent, revealing these as endpoints of a continuum. Experiments on GLUE with RoBERTa-base and GSM8K with LLaMA-2-7B show task-dependent optimal points within this family, with tuned ULoRA matching full fine-tuning and ULoRA-Auto automating exponent selection. Results demonstrate that LoRA initialization should be treated as a tunable dimension rather than fixed.
low-rank adaptationgradient preconditioningspectral whiteningparameter-efficient fine-tuningglue benchmark
Weak-to-Strong On-Policy Distillation
Weak-to-Strong On-Policy Distillation (W2S-OPD) introduces a novel framework for improving strong language models by distilling from multiple weak teachers. The method constructs a proxy teacher in logit space using a contrast pair of positive and negative models, isolating capability directions while maintaining distributional adjacency to the student. The student minimizes per-token reverse KL divergence on its own rollouts. Evaluated on four math and three code benchmarks, W2S-OPD outperforms traditional on-policy distillation, enables students to surpass domain teachers, and continues improving even with weaker supervision. Analysis reveals distinct signals from different contrast pairs, emphasizing reasoning frameworks and solving procedures.
on-policy distillationlogit spacereverse kl divergencecontrast pairproxy teacher
Learning the Word Problem: Geodesic Lengths and Cryptographic Applications
WPNet, a novel Graph Neural Network architecture, is introduced to solve the Word Problem heuristically by mapping unreduced words to dynamic graph structures and clustering algebraically equivalent elements in a continuous embedding space. The model identifies geodesic representatives without discrete reduction steps and predicts geodesic lengths in the Baumslag-Solitar group BS(1,2) and an Artin group. WPNet is applied to cryptanalyze the Wagner-Magyarik public-key cryptosystem, demonstrating its cryptographic implications.
word problemgraph neural networkgeodesic lengthbaumslag-solitar groupartin group
Lightweight Image Classification of Raptor Species for Edge Devices: Rare-Species Dataset Expansion via Video Frame Extraction, Knowledge Distillation, and TensorRT Deployment
The study presents a lightweight raptor-species classification system for edge deployment in wind-turbine collision mitigation, achieving 0.935 macro recall via a three-model ensemble (MobileNetV4, ViT-Small, EfficientNet-B0) distilled from DINOv2-L. Dataset expansion from 463 to 2,050 Steller's Sea Eagle images via video-frame extraction reduced White-tailed Eagle misclassification by 38.6 percentage points. TensorRT FP16 deployment on NVIDIA Jetson Orin Nano achieved 313 images/s with 99.95% argmax agreement versus FP32. Primary improvements stemmed from dataset expansion and teacher fine-tuning, not distillation or DINOv3-L adoption.
knowledge distillationvideo-frame extractiontensorrt deploymentedge devicesraptor classification
Retrospective Orthogonal Design: Response-Surface Reconstruction from Observational Data
Retrospective Orthogonal Design (ROD) is introduced to reconstruct conditional mean surfaces from observational data, addressing multicollinearity and term-order dependence in sequential sums of squares. ROD employs a probability-balanced lattice, weighted tensor-product contrasts, and piecewise-affine interpolation over Freudenthal polyhedra, ensuring specification-invariant contrast effects and order-independent SS. Validation selects resolution and completion jointly, followed by refitting and evaluation on a test set. Across 6,480 simulations spanning nine data-generating processes, ROD matched or exceeded polynomial regression in five processes, excelling on threshold, sign-interaction, and localized surfaces. Calibrated coefficient bias remained small, and ROD achieved the highest out-of-sample R² in a weighted Mincer application.
retrospective orthogonal designmulticollinearityfreudenthal polyhedratensor-product contrastsconditional mean surfaces
Randomizing the Number of Centers in k-means++
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Dynamic Parameterization Is Not Dynamic Inference
The paper disentangles dynamic inference claims by analyzing input-dependent controller coefficients through frozen-controller auditing (FCA), which caches coefficients and replays them under perturbations. FCA measures assignment dependence without feedback by cross-input reassignment, token shuffling, and static profiles. Experiments on FeatureGate Transformers (76M-504M params) and MUDDPythia-1.4B show static profiles retain 98.70-99.43% performance, layer identity explains 87-96% coefficient variance, and reassignment increases NLL by 1.9067-2.9637, proving dynamic parameterization ≠ dynamic inference.
dynamic parameterizationfrozen-controller auditingcoefficient variationconditional executiontransformer blocks
Shared SFT Lessons Across Alignment, Model Organisms, and Toy Models
The study investigates the transferability of supervised fine-tuning (SFT) lessons across alignment training, model organisms, and toy models, aiming to unify insights from these distinct research areas. Three key lessons are tested: (1) training on the rationale behind behaviors improves generalization in toy models, (2) mixing on-model data with off-model outputs preserves capabilities during alignment training, and (3) follow-up benign SFT can erase alignment behaviors while retaining capabilities, highlighting limitations in robustness. Results demonstrate that cross-domain SFT lessons can enhance research outcomes, advocating for broader interdisciplinary technique borrowing.
supervised fine-tuningalignment trainingmodel organismsgeneralizationrobustness
Data Fusion and Contrastive Alignment for Unconstrained IR Molecular Structure Elucidation
The paper introduces a modified encoder-decoder transformer for unconstrained molecular structure elucidation from IR spectroscopy, eliminating the need for pre-determined chemical formulas. Key innovations include a Mixture-of-Experts decoder with non-additive aggregation via linear-order statistics and Choquet integral, alongside contrastive alignment loss. These modifications improve Top-K accuracy by >10 percentage points over IR-only baselines. Fragment analysis confirms IR spectra encode most relevant chemical information, addressing performance gaps in isomer-ranking models.
mixture-of-expertschoquet integralinfrared spectroscopycontrastive alignmentmolecular structure elucidation
Two2Four: Generative Quadruped Puppeteering from Human Motion
The authors propose Two2Four, an automatic human-to-quadruped puppeteering framework that generates realistic quadruped motions from ordinary human motion data. The method employs a two-stage generative diffusion model trained exclusively on quadruped motion data, enhanced by structured conditioning and inpainting strategies to support diverse actions (walking, running, jumping) and fine-grained control (head movement, limb puppeteering). Experiments show superior motion realism and controllability compared to existing retargeting approaches, demonstrating utility for animation and virtual production.
generative diffusion modelmotion retargetingquadruped animationinpainting strategyvirtual production
Weight and Height Estimation from a Single Human Image Captured in the Wild
The authors introduce a novel dataset of 6105 full-body images with height, weight, and BMI annotations, addressing the lack of public datasets for BMI estimation in the wild. They employ deep neural networks with single and multi-task learning, utilizing modalities such as RGB, depth-maps, pose-affinity maps, and edge-maps. Experiments compare performance across full-body, half-body, and facial images using CNN backbones like VGG, DenseNet, and ResNet. Results indicate that full-body images yield superior BMI, weight, and height estimation accuracy compared to half-body and facial images, demonstrating robustness to variations in pose, background, and scale.
bmi estimationmulti-task learningdepth-mapspose-affinity mapscnn backbones
📰 Industry Media (7)
A fundamental flaw leaves LLMs strikingly vulnerable to attack
Researchers demonstrate a fundamental vulnerability in large language models (LLMs) stemming from their inability to reliably distinguish instruction sources via role tags, presenting 'chain-of-thought forgery' attacks that bypass safety guardrails. By spoofing internal reasoning text (e.g., mimicking
chain-of-thought forgeryrole taggingprompt injectionred-teamingjailbreak vulnerability
Google DeepMind Ships Three Physical AI Models For Whole Body Control, Dexterity And Multi Robot Collaboration
Google DeepMind introduces Gemini Robotics 2, a suite of three AI models for robotic control: a vision-language-action (VLA) model for whole-body and dexterous manipulation, an embodied reasoning VLM (Gemini Robotics ER 2) for task planning, and an on-device VLA for local execution. The system demonstrates multi-robot collaboration, achieving 32-92% success rates on dexterous tasks across diverse embodiments (Apollo 2 humanoid, Franka Duo gripper). ER 2, based on Gemini 3.5 Flash, shows 57.4% accuracy on progress classification and 91.3% accuracy on moment finding. On-Device 2 adapts to new robot bodies with <200 examples, improving SO101 platform performance from 6.7% to 53.3%.
vision-language-actionembodied reasoningmulti-robot collaborationdexterous manipulationon-device inference
Tencent Open-Sources AngelSpec: A Unified Training Framework for MTP and Block-Parallel Speculative Decoding on Hy3 Models
Tencent introduces AngelSpec, a unified training framework for speculative-decoding draft models, addressing workload heterogeneity via specialized architectures and training regimes. The framework supports both autoregressive multi-token prediction (MTP) for conversational tasks and block-parallel DFlash for code and mathematical reasoning. Key innovations include depth-specific training with target-model rollout, hybrid target conditioning, and predecessor-conditioned autoregressive heads. AngelSpec achieves significant improvements in mean accepted length, with DFly reaching 4.79 on Hy3-A21B and 5.41 on Qwen3-8B, outperforming prior methods. The framework is torch-native, supports long-context training up to 128k tokens, and integrates with vLLM, SGLang, and HuggingFace Transformers.
speculative decodingmulti-token predictionblock-paralleltarget-model rollouthybrid conditioning
Meet Token Saver: An Open-Source MCP Extension Using Local Hybrid RAG to Cut Claude PDF Token Costs 90-99%
Token Saver is an open-source MCP extension for Claude Desktop that reduces PDF token costs by 90-99% via Local Hybrid RAG. The system combines BM25 keyword matching (weighted 0.4) with cosine similarity from a local all-MiniLM-L6-v2 embedding model (weighted 0.6) to retrieve only relevant passages from on-device PDFs, capping outputs at 8,000 characters. Benchmarks show 95.7-99.4% token savings across legal and technical documents, with zero data uploads and folder-level privacy controls. The MIT-licensed tool requires no Python setup, operating as a lightweight local server.
local hybrid ragbm25cosine similaritytoken efficiencymcp extension
Moonshot AI Open-Sources MoonEP: A Perfectly Balanced Expert Parallelism Library for MoE Training
Moonshot AI introduces MoonEP, an open-source Expert Parallelism (EP) library for efficient distributed training of Mixture-of-Experts (MoE) models. The library enforces perfect load balancing by dynamically planning redundant experts online, ensuring each rank receives exactly S×K tokens regardless of router skew. Key innovations include zero-copy communication, static buffer shapes to eliminate host synchronization, and a memory-efficient prefetch mechanism. Benchmarks against DeepEP v2 show MoonEP maintains consistent communication latency under router imbalance (maxvio 0.2-20) while avoiding memory fragmentation. The library enabled 2.5× scaling efficiency gains in Kimi K3, a 2.8T-parameter MoE model.
expert parallelismmixture-of-expertsload balancingdynamic redundancysymmetric memory
Zuckerberg details Meta’s personal AI superintelligence strategy
Mark Zuckerberg outlines Meta's vision for 'personal superintelligence' in a WSJ op-ed, emphasizing broad individual access over centralized institutional control. He critiques prevailing AI discourse for its focus on existential risks, arguing instead for empowerment and invention as primary goals. Zuckerberg contrasts automation-driven job displacement with potential economic benefits from widespread AI distribution, particularly in fostering small businesses. He distinguishes between cybersecurity risks, advocating open-source solutions, and biological risks, calling for intergovernmental coordination. The piece positions Meta as a proponent of decentralized AI but lacks technical specifics, benchmarks, or deployment timelines.
superintelligenceautomationopen-sourcecybersecuritydecentralization
How AI is Changing Linux VPS Security for Businesses
AI-enhanced security tools are improving threat detection and response for Linux VPS systems by analyzing behavioral patterns rather than relying solely on rule-based methods. These tools monitor multiple signals—logins, traffic, resource usage—to identify anomalies that traditional systems miss, enabling faster detection of unauthorized access and malware. AI also assists in prioritizing software updates by identifying critical vulnerabilities and exploited packages. While AI accelerates incident response through automated flagging and reporting, it complements rather than replaces fundamental security practices like access control and manual audits. The integration of AI monitoring with Linux VPS fundamentals provides businesses with enhanced protection against increasingly sophisticated attacks.
anomaly detectionvulnerability prioritizationbehavioral analysisincident responseaccess control
Generated automatically at 2026-07-30 20:46 UTC. Summaries and keywords are produced by an LLM and may contain inaccuracies — always consult the original article.