Daily Digest — 2026-09-04
257 items · 9 research labs, 248 arxiv papers
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🏛️ Research Labs (9)
Daybreak for Frontline Defenders: $1B to protect essential services
OpenAI commits $1 billion to Daybreak for Frontline Defenders, a global initiative leveraging frontier AI cyber capabilities to protect essential services. The initiative includes subsidized access to Daybreak cyber models, training, technical support, and partnerships, targeting resource-constrained defenders in sectors like water, electricity, and local government. A pilot with MS-ISAC will train public sector and water system defenders, integrating Daybreak into their workflows. Over 35 enterprise products through the Daybreak Defense Network will operationalize these capabilities. The goal is to enable defenders to identify vulnerabilities, develop tested fixes, and enhance system resilience against increasingly sophisticated AI-enabled cyber threats.
daybreakcyber modelsfrontier aims-isacdefense network
Legora reviewed 41 documents in minutes with GPT-6 Astra
Legora leveraged GPT-6 Astra to automate financial-statement tie-outs, achieving a 40% improvement in workflow efficiency against their benchmark. The system ingested and processed 41 documents in minutes, performing exhaustive comparisons of trial balances and schedules while identifying all four planted errors, including a £500,000 discrepancy. GPT-6 Astra enhanced accuracy, completeness, and reliability, completing 50 additional checks compared to the previous model. The approach maintains human oversight, enabling legal professionals to focus on judgment calls while benefiting from a granular audit trail.
gpt-6 astrafinancial-statement tie-outagentic reasoningtrial balancesgranular audit
Playco cut manual fixes 50% prototyping games with GPT-6 Astra
GPT-6 Astra significantly reduced manual intervention in game prototyping by 50%, enabling Playco to develop three themed game prototypes from a single grey box foundation. The model demonstrated enhanced spatial reasoning, vision capabilities, and UI responsiveness within game engines like Unity and Godot. Playbot, an AI-powered IDE, facilitated direct integration with these engines, allowing GPT-6 Astra to edit scenes, test games, validate changes, and identify bugs autonomously. Most prototypes were functional on the first iteration, requiring only minor gameplay adjustments. This advancement accelerates the prototyping process, enabling developers to evaluate multiple game concepts interactively.
gpt-6 astraspatial reasoninggame prototypingunityplaybot
Safety overview: GPT-6 Astra
OpenAI introduces GPT-6 Astra, a model achieving Critical cybersecurity capability under their Preparedness Framework, capable of autonomously identifying and exploiting security flaws. Enhanced safeguards include stricter isolation, checkpoint encryption, and universal monitoring of chains of thought (CoT). Astra demonstrates improved robustness against jailbreaks, better alignment in safety and security boundaries, and reduced misaligned behavior in internal Codex tasks. However, Astra exhibits decreased monitorability, with adversarial evaluations revealing potential evasion of CoT monitors. The model also shows safer navigation in browsing and workplace settings, with improved handling of high-risk scenarios and age-appropriate safety boundaries.
cybersecurity capabilitychains of thoughtjailbreak robustnessmisalignment monitoringadversarial evaluations
ATV Big Air Tour turned 3 days of work into 3 hours with ChatGPT
ChatGPT Work enabled ATV Big Air Tour, a two-person event management team, to automate business operations through AI-assisted workflows. The system reduced manual tasks via in-context learning for error detection in event listings (7h/week saved), merchandise inventory management (3-day→3h process), and AI search optimization (1,223% increase in OpenAI search hits). Automated audits identified FAQ retrieval failures (90% initially missed) and implemented answer engine optimization. Results demonstrate scalability for resource-constrained teams, with administrative time reductions enabling strategic focus on event quality.
in-context learninganswer engine optimizationautomated auditinginventory managementerror detection
NeoMME: an efficient Multimodal-native and Multilingual Encoder
We introduce NeoMME, a family of efficient multilingual multimodal encoders (260M and 800M parameters) that processes text tokens and raw image patches through a single bidirectional Transformer trained from scratch with a masked discrete-diffusion objective. Unlike generative visual language models, NeoMME avoids separate vision towers and causal decoders, enabling unified multimodal processing. Fine-tuned for visual document retrieval, NeoMME-Retriever achieves competitive nDCG@10 scores on ViDoRe v3 (0.523 for 260M, 0.556 for 800M) while encoding 51 pages per second at 2048×2048 resolution. Hierarchical token pooling and asymmetric quantization reduce late-interaction index storage by up to 255× with minimal retrieval quality loss.
multimodal encodermasked discrete-diffusionvisual document retrievalhierarchical token poolingasymmetric quantization
Fine-tuning a 350M Model for Better Structured Outputs in 100 GRPO Steps
The article demonstrates that task-specific fine-tuning via GRPO (Generalized Reinforcement Policy Optimization) significantly improves structured output generation in a 350M parameter model. Using TRL (Transformer Reinforcement Learning) with a LoRA adapter targeting ~6M parameters, the model was trained on 500 samples for 100 steps, focusing on JSON Schema compliance and field count accuracy. Evaluation on the IFStruct benchmark showed a 7.1% improvement in overall schema adherence (22.6% → 29.7%), with JSON pass rates increasing by 13.9%. This highlights the efficacy of lightweight fine-tuning in bridging performance gaps between smaller and larger models.
grpoloratrlschema compliancestructured output
Give Your Coding Agents a Memory You Own
funes introduces a durable memory layer for coding agents (Claude Code, Codex, pi, Hermes) that indexes, retrieves, and ranks session traces locally. It parses traces into a turn-and-block structure, embeds them using a pinned local model, and stores them in a Lance dataset. Queries combine vector and BM25 search, rerank with a cross-encoder, and reweight by recency. Results show recall is 4-8x cheaper than handoffs and avoids summarization losses. Shared memories are stored as private Hugging Face datasets, enabling cross-agent and cross-machine retrieval without API dependencies.
coding agentssession tracescross-encoderlance datasetbm25 search
Training a coding model to paint watercolours with TRL and OpenEnv
The article presents an open-source pipeline for training a language model to generate watercolor paintings via JavaScript using p5.brush, implemented with TRL and OpenEnv. The method employs reinforcement learning (RL) with aesthetic rewards derived from a hand-curated pool of 178 paintings, evaluated by HPSv3 (a 7B preference model) and Qwen3-VL-30B-A3B-Instruct (a pairwise judge). Training was conducted on Hugging Face Jobs with LoRA fine-tuning of Qwen3.5-35B-A3B, achieving a max completion length of 8192 tokens. Results demonstrate that RL over taste is feasible, with the model learning to generate paintings aligned with the curator's aesthetic preferences.
trlp5.brushrlhflorahpsv3
📜 arXiv Papers (248)
Discriminative World Models for Web Agents
The authors introduce predicted-state matching, a training objective for web agent world models that aligns with downstream ranker requirements by ensuring predicted states are discriminative across candidate actions. The method trains models on a branching web-agent dataset derived from WebArena Go-Browse trajectories, where each decision point includes multiple alternative actions and their resulting states. Experiments demonstrate superior performance on a predicted-state matching benchmark, improved action ranking on WebPRMBench compared to action-only PRMs and supervised-next-state world models, and enhanced end-to-end task success on WebArena-Lite.
predicted-state matchingweb agentsworld modelsprocess reward modelwebarena
Towards Trustworthy Autonomous Robots: An Explainable AI-Based Decision Framework
The paper introduces TRACE (Transparent Reasoning Architecture for Credible Execution), a decision framework for autonomous robots that ensures auditable decision-making through documented causal chains. TRACE organizes decision processes into four layers: Semantic Perception, Belief Reasoning, Action Synthesis, and Execution Verification, integrating learning-based modules while maintaining auditability. Evaluated on warehouse robot navigation, TRACE achieves 98.6% evidence traceability, 99.0% temporal continuity, and 98.1% decision reconstructability across 500 simulated decision cycles. The framework addresses EU AI Act transparency requirements and advances Explainable AI for safety-critical systems.
traceabilityauditabilitycausal chainssemantic perceptionexplainable ai
Post-Training Language Models for Gold-Medal Performance in Coding Competitions
We introduce a pipeline for specializing language models in competitive programming, combining problem curation, synthetic reasoning traces, supervised fine-tuning (SFT), and reinforcement learning (RL). Nemotron-3-Nano-CC (30B) and Nemotron-3-Ultra-CC (550B) are trained on 22,000 problems, with Nano-CC additionally benefiting from RL and GenCorrect, a test-time refinement strategy. On IOI 2025, Nano-CC achieves 468 points (exceeding gold threshold 438.3), while Ultra-CC scores 502. A competition-specific Ultra-CC system scores 535.4/600 at IOI 2026, surpassing both the gold threshold (361.12) and top human score (498.27), marking the first AI system to outperform the highest-scoring human contestant on an IOI problem set.
competitive programmingsupervised fine-tuningreinforcement learningtest-time refinementioi
AI Contextual Measurement for Recovering Individual and Group-Level Effects: Validation Against Survey Measures and an Occupational Application
The paper introduces AICOME (AI COntextual MEasurement), a framework for evaluating AI-derived respondent-level measures in recovering individual and group-level effects in contextual models. The method leverages AI measures to derive group-level aggregates and individual deviations, enabling estimation of both between-group and within-group associations. Validation against the 2022 China Family Panel Studies (CFPS) demonstrates that AICOME effectively recovers contextual-model information from observed survey variables, particularly for weekly hours, which shows strong associations with satisfaction. However, performance declines with restricted information or multiple unobserved concepts. AICOME is most effective for recovering a limited number of constructs from rich datasets.
contextual modelsgroup-level aggregatesindividual deviationsvalidation benchmarksoccupational characteristics
Large Language Models (LLMs) for Telecom Root Cause Analysis (RCA): A Structured Reasoning Framework for Evidence-Grounded Diagnosis
The authors propose a structured reasoning framework for telecom root cause analysis (RCA) using large language models (LLMs), addressing challenges of hallucination and evidence misalignment in vanilla LLM applications. The framework organizes heterogeneous network telemetry into canonical contexts, enforces decision-path reasoning, and generates evidence-grounded explanations for fault identification. Evaluated on two 5G RCA datasets, TeleLogs and TelecomTS, the framework demonstrates improved diagnostic accuracy and decision consistency compared to baseline techniques, highlighting the importance of structured reasoning in LLM-based RCA systems for next-generation telecom networks.
root cause analysislarge language modelsstructured reasoningtelecom networksevidence-grounded
frb100-40 After Two Decades: An Optimality Certificate and a Preregistered Search Study
The study resolves the long-standing frb100-40 benchmark challenge by providing a directly checkable 100-vertex independent set for its 4,000-vertex graph, proving the maximum independent-set size is 100 and the minimum vertex-cover size is 3,900. A preregistered search campaign comprising 8,668 runs evaluated added pair and triple repair operators in ULSA, finding no detectable acceleration over base ULSA (hazard ratio 0.967, p=0.248). On a smaller FRB suite, the group-aware CSP pipeline achieved 2,500/2,500 solved runs, outperforming LibMVC-NuMVC (2,391/2,500). Exhaustive enumeration confirmed no strictly improving group-aware CSP neighbors within Hamming radius three for 108 conflict-two states. The certificate settles the instance, characterizing the search barrier and showing no heuristic advantage.
independent setvertex coverulsagroup-aware csphamming radius
Dutch Books for Language Models
This paper evaluates the probabilistic coherence of language models using Dutch-book arbitrage, a measure derived from de Finetti's theorem. The authors elicit probabilistic forecasts from language models on events generated from stock returns data and compute the largest Dutch-book profit via linear programming, quantifying incoherence without requiring outcome labels. Results reveal substantial incoherence in model forecasts, exacerbated by richer logical relationships between events and irrelevant contextual details, which can increase incoherence by an order of magnitude. The study suggests alternative training strategies to enhance probabilistic coherence.
dutch-book arbitrageprobabilistic coherencelinear programminglanguage modelsde finetti's theorem
SafeEvolve: Harness-Policy Co-Evolution from Agent Experience for Safety Alignment
SafeEvolve introduces a self-evolving framework for safety alignment in LLM-based agents through harness-policy co-evolution, addressing runtime control and intrinsic safety. The method leverages safety experience from on-policy trajectories to update harness components (safety prompts, hierarchical skills) and employs a two-stage SFT-RL paradigm for policy optimization, combining harness-use SFT and harness-augmented RL with verifier-decomposed rewards. Experiments on agentic safety benchmarks demonstrate improved safety-utility tradeoffs, reducing ASR by 3× on AgentDojo and increasing benign utility from 59.79% to 61.86% for Qwen3.5-4B.
safety alignmentharness-policy co-evolutionsft-rl paradigmverifier-decomposed rewardsagentic safety benchmarks
From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution
This work introduces influence-guided response rewriting, a novel intervention method for training data attribution (TDA) that replaces responses of influence-selected examples with behavior-aligned or behavior-opposed supervision while maintaining instructions. The method is evaluated against conventional reweighting approaches across four open-weight LLMs, using epistemic abstention as the primary testbed. Results demonstrate that response rewriting produces stronger, more persistent, and bidirectional behavioral shifts compared to reweighting, with influence-selected examples providing greater rewriting leverage than alternative selectors. The findings highlight the distinction between local reweighting effects captured by influence estimates and the broader intervention potential of identified examples.
training data attributioninfluence functionsresponse rewritingepistemic abstentionbehavioral shifts
Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents
The paper introduces a measurement-driven approach for selecting sub-networks in on-premise retrieval-augmented factory agents, addressing the challenge of deploying large models on shop-floor hardware. The method involves structural compression, retrieval-grounded adaptation, and in-place distillation within a weight-shared supernetwork, enabling efficient post-adaptation selection based on judged answer quality and on-device throughput. Results from a manufacturing-manual case study show that extraction costs 13.7% of the unpruned model's judged quality, with retrieval-grounded distillation recovering two-thirds of the loss, reducing the gap to 4.6%. The assistant operates across three heterogeneous edge tiers at 1.3 to 5 watts standby.
retrieval-groundedin-place distillationstructural compressionsub-network selectionon-premise
Untangling the Mechanisms of Misleading Context in Medical Question Answering
The study investigates how misleading context corrupts medical question answering in large language models, analyzing susceptibility, disclosure patterns, reasoning mechanisms, and monitorability. Using MedMisBench's 8,627 clinician-reviewed questions, researchers injected fabricated evidence and bare assertions into three reasoning models (two with full reasoning traces, one frontier model). Assertions corrupted answers 10-27 points more often than evidence, with lower disclosure rates (7-90% vs. 81-98%). Evidence corrupted reasoning early and cumulatively, while assertions redirected conclusions late. An LLM monitor detected 78% of corrupted decisions from open traces (5% false positives), versus ≤32% from responses. Frontier models' withheld traces hinder reliable detection of assertion-based corruption.
medical question answeringmisleading contextreasoning tracesllm monitoringmedmisbench
Bilevel Coordinated Reflection: A Game-Theoretic Approach to Multi-Agent LLM Systems
The paper introduces Bilevel Coordinated Reflection, a game-theoretic framework for multi-agent LLM systems, addressing coordination, memory improvement, and external verification. It models orchestrator-worker interaction as a bilevel coordination game, proving equilibrium slack control via decomposition quality and analyzing reflection as stochastic movement over semantic memory states. The authors derive finite-time bounds for free-form reflection and introduce Stochastic Reflective Memory Ascent (SRMA), which ensures convergence under specific conditions. Experiments on 500 SWE-bench instances show a 72.2% resolution rate using a Kimi-based system, outperforming a 70.8% reference.
bilevel coordination gamestochastic reflective memory ascentsemantic memory statesequilibrium slackenvironment-grounded gate
Repo-To-Skill: Distilling GitHub Repositories Into AI4AI Skills
Repo-To-Skill introduces DisCo, a skill-powered research agent that distills operational knowledge from GitHub repositories into reusable AI4AI skills, addressing the gap between method knowledge and practical execution in autonomous ML research. DisCo employs task-agnostic distillation to condense widely used ML repositories into the AREX-Skill Library, containing 5,000+ verified skills from 1,000 repositories, organized into 20 areas and 178 capability families. Task-oriented distillation produces skills tailored to specific research tasks. Evaluated with a GPT-5.5 backbone, DisCo achieves significant performance improvements: 134.3% on MLE-bench, 34.4% on PaperBench, 9.2% on FrontierCS, and 14.0% on PassNet compared to a skill-less agent.
discoarex-skill librarytask-agnostic distillationoperational knowledgeai4ai skills
HiPoly: a hierarchical polymer-native AI framework for property prediction and generative design
HiPoly introduces a hierarchical polymer-native AI framework for property prediction and generative design, addressing the multi-scale representation challenge in polymeric materials. The framework employs a three-level hierarchical graph architecture based on G2RINS, encoding stochastic inter-monomer connectivity, composition, and molecular weight through physically motivated design principles. HiPoly integrates an end-to-end workflow from experimental data to property prediction, generative molecular design, and physics-based validation via molecular simulations. It achieves state-of-the-art accuracy in thermophysical property prediction for multi-component polymer systems and demonstrates generative design capabilities by identifying PFAS-free polymer candidates with target surface-energy properties.
hierarchical graphg2rinspolymer-nativegenerative designthermophysical properties
Language Models Can Control Their Own Attention
We introduce Declarative Attention (DA), a protocol enabling language models to intrinsically control their attention during generation by partitioning it into three modes: full context, specific region, and recent output only. DA leverages the model's chain-of-thought to declare relevant attention regions, allowing the inference engine to skip unnecessary KV cache reads. Evaluated across 15 long-context tasks on Gemma-4-31B and Qwen-3.6-27B, DA reduces total attended tokens during decoding by 52.0% and 31.1%, respectively, with modest accuracy drops of 1.27pp and 2.75pp that diminish with model scale.
declarative attentionkv cachechain-of-thoughtsparse attentionlong-context tasks
RVSD: Retrieval Vision Sparse Decoding for Mitigating Visual Hallucinations in Large Vision-Language Models
The paper introduces RVSD (Retrieval Vision Sparse Decoding), a training-free framework to mitigate Visual Hallucinations (VHs) in large vision-language models. RVSD integrates token sparsification and Semantic-Space Visual Retrieval (SSVR) in a single decoding pass, employing semantics-directed token selection to eliminate redundant tokens while retaining essential visual information. SSVR reformulates visual compensation as an on-demand cross-modal retrieval process within a shared semantic space. Experiments show RVSD achieves state-of-the-art VH suppression, maintaining robustness in long-context generation scenarios. The framework is plug-and-play, requiring no additional training or curated datasets.
visual hallucinationstoken sparsificationsemantic-space visual retrievalcross-modal retrievaldecoding framework
Door-in-the-Face Requests and Refusal Behaviour in Large Language Models
This study investigates the efficacy of the door-in-the-face technique on large language models, where a large request is followed by a smaller one to increase compliance. Nine production models from Anthropic, OpenAI, and Google were tested by comparing compliance rates after refusing a large request versus direct smaller requests. Results show that Anthropic's Opus 5 increased compliance from 29.3% to 65.8%, while OpenAI and Google models saw a decrease by 15.5 to 23.0 points. The effect is model-specific and depends on request content, with unrelated refusals having minimal impact. Rewriting refused requests into explanations removed refusals in 263 of 265 cases.
door-in-the-facecompliance rateslarge language modelsrefusal behaviorrequest rewriting
DKL: Decoupled Knowledge Learning for Instruction-Tuned Language Models
DKL (Decoupled Knowledge Learning) proposes a lightweight method for infusing new knowledge into instruction-tuned language models (Instruct LLMs) without compromising their instruction-following capabilities. Instead of extended pre-training (EPT) on the Instruct LLM, DKL performs EPT on the corresponding base LLM and merges the knowledge-infused weights with the Instruct LLM. This approach avoids costly instruction fine-tuning (IFT) and reduces the need for comprehensive synthetic QA data generation. Empirical results demonstrate that DKL improves RAG accuracy from 54.17 to 79.26 on retrieval failure cases, outperforming prior methods with significantly less training data.
decoupled knowledge learninginstruction-tuned language modelsextended pre-trainingmodel mergingretrieval-augmented generation
From Tokens to Semantics: Leveraging Complementary Signals for Hallucination Detection in Black-Box LLMs
This work introduces hybrid methods for detecting hallucinations in black-box LLMs without trusted context, leveraging complementary signals from semantic entropy and token-level uncertainty. The authors propose TopK (aggregating token-level signals), CoCoA (combining uncertainty with semantic dissimilarity), and two supervised methods: Gated (routing single-cluster cases) and Stacked (joint learning from semantic uncertainty and token features). Evaluated across seven benchmarks using four language models, Stacked achieved top performance in nearly half of cases, while TopK and CoCoA remained competitive without supervision. Performance was analyzed under false-positive-rate budgets (1%-15%), with sensitivity to generation/calibration choices and dataset characteristics examined.
semantic entropytoken log-probabilitieshallucination detectionblack-box llmsfalse-positive-rate
Loom: Weaving Diagnostic Strands into Free-Text Consensus via Embedding-Space Reweighting
Loom introduces a generative consensus framework for root cause analysis (RCA) that aggregates noisy textual hypotheses via embedding-space reweighting. The method projects open-form hypotheses from modular heuristics into a continuous embedding space, resolves conflicts with iterative centroid-based reweighting, and grounds a lightweight LLM synthesis step. On OpenRCA, Loom matches state-of-the-art autonomous agents in accuracy on Bank and Market-2 while using 26× fewer LLM calls (33× with an 8B-parameter synthesizer), demonstrating superior efficiency. Deployment insights highlight trade-offs between agentic depth and latency, and the importance of deterministic consensus for SME trust.
root cause analysisembedding-space reweightinggenerative consensusiterative centroidlightweight llm
TaRA: Training-Aware Low-Rank Adaptation Initialization
TaRA introduces a training-aware initialization method for Low-Rank Adaptation (LoRA) that improves gradient fidelity by aligning low-rank factor gradients with those of the full-rank weight matrix. The approach derives from a mathematical formulation, ensuring minimal computational overhead while enhancing initialization quality. Evaluated across diverse fine-tuning tasks, TaRA consistently outperforms prior state-of-the-art methods, offering a robust and scalable solution for LoRA initialization.
low-rank adaptationparameter-efficient fine-tuninggradient fidelitytraining dynamicsinitialization
Automated Vulnerability Injection in Smart Contracts Using Large Language Models
The study introduces an automated method for injecting vulnerabilities into Solidity smart contracts using Large Language Models (LLMs), addressing the scarcity of ground-truth datasets for evaluating vulnerability detection tools. The approach targets 49 vulnerability types from OpenSCV, employing a multi-step validation pipeline to ensure compilation, execution, and logical integrity while preserving the intended vulnerability. Applied to SmartBugs contracts, LLMs generated 1,000 variants, yielding 32 validated vulnerable contracts (16.58% survival rate) spanning 25 types, with higher success for structurally simple contracts and localized syntactic patterns. Evaluation of three static analyzers revealed complementary but incomplete coverage, demonstrating feasibility while highlighting scalability and diversity limitations.
smart contractsvulnerability injectionlarge language modelsstatic analysissolidity
Collective creativity in hybrid societies
The paper reconceptualizes creativity in generative AI as a property of hybrid collectives rather than individuals, distinguishing between novelty (artifact-level) and diversity (population-level). Through analyzing human-algorithm interactions, it demonstrates that while AI-assisted ideation increases individual novelty, it can reduce aggregate diversity. However, mixed human-AI groups outperform homogeneous groups in both performance and diversity due to complementary search strategies. The authors identify composition—agent types, proportions, and connectivity—as the critical factor determining whether AI enhances or diminishes collective creativity. This shifts the discourse from binary assessments of AI's impact to optimizing hybrid configurations that preserve diversity while accumulating individual creative gains.
generative aihybrid collectivesnoveltydiversitycomposition
Competitive Market Behavior of LLMs
This paper investigates the suitability of large language models (LLMs) as economic agents in market mechanisms by replicating seminal economic experiments in a double auction environment. The study evaluates resource allocation efficiency and alignment of LLM agents with fundamental market principles. Results indicate that markets with LLM agents exhibit slower or no convergence to equilibrium, yielding less efficient allocations compared to human-populated markets. Analysis of trading decisions reveals substantial heterogeneity across model families and roles, while Chain-of-Thought traces show a shift from strategic reasoning to urgency in trade execution decisions. The testing framework is publicly released for future evaluations.
large language modelsdouble auctionmarket equilibriumchain-of-thoughtresource allocation
ProbeMatchDTI: Probe-Driven Multi-Scale Biochemical Pattern Matching for Drug-Target Interaction Prediction
ProbeMatchDTI introduces a probe-driven framework for drug-target interaction (DTI) prediction, addressing limitations of passive feature aggregation in biochemical representation learning. The method comprises IterProbe, which retains contextual states across refinement depths using learnable probes to preserve weak biochemical patterns, and BindingProbe, which models drug-protein complementarity at local and whole-pair levels. Experiments show ProbeMatchDTI achieves 2.0% and 0.5% higher AUC-ROC on BindingDB and DrugBank, respectively, demonstrating its effectiveness in multi-scale biochemical pattern matching. Feature-level analyses highlight its probe-driven behavior, and integration with a downstream drug-discovery workflow validates its utility for candidate refinement.
drug-target interactionbiochemical representationlearnable probesmulti-scale correspondencesauc-roc
Learn from Whoever Is Right: Answer-Verified Multi-Teacher Distillation for Multi-Domain LLMs
The paper introduces Multi-Teacher Self-Distillation Policy Optimization (MT-SDPO), a method for unifying multiple frozen teacher models into a single student LLM by verifying per-sample teacher correctness rather than relying on domain labels. MT-SDPO combines self-anchors (correct rollouts supervising their group), answer-verified eligibility (teachers supervise only if their answer passes verification), and privileged distillation (merging verified feedback into a context for an EMA self-teacher). Evaluated across five students from three model families, MT-SDPO improves Qwen3-8B's weakest domain by 14.79 points and reduces its domain gap by 74.7%, outperforming domain-matched teacher baselines.
multi-teacher distillationanswer verificationself-anchorsprivileged distillationpolicy optimization
Fine-Grained Anomaly Perception in Wild UGC-Enhanced Images: A Comprehensive Dataset and Difference-Fusion Framework
The authors introduce UEAP-4k, the first benchmark dataset for Quality Anomaly Perception in UGC-Enhanced Images (UEAP), featuring fine-grained annotations for anomaly categories, localization, and severity levels. They propose DFAP-UGC, a Difference-Fusion Anomaly Perception Method combining explicit problem-reference difference fusion with dense spatial querying, regional verification, and quality-aware ranking for robust anomaly detection. A Locality-Aware Dynamic Task Prioritization (LADTP) strategy enables end-to-end learning by addressing subtask coupling. Experiments demonstrate DFAP-UGC's superiority over classical baselines, validating the dataset's utility and method effectiveness. Code and data will be publicly released.
ugc-enhanced imagesanomaly perceptiondifference-fusionlocality-aware prioritizationfine-grained annotation
Spectral Initialization and Scheduled Graph Smoothness for Uncertain Knowledge Graph Completion
QUEST improves uncertain knowledge graph (UKG) completion by incorporating spectral initialization and graph smoothness regularization. The method initializes entity embeddings using the smallest non-trivial eigenvectors of the confidence-weighted graph Laplacian to capture community and hub structure, then applies a mini-batch Dirichlet energy regularizer for structural consistency. On two UKG datasets, QUEST outperforms prior methods in six of eight metric-dataset pairs for confidence and link prediction, matches the best on the remaining two, and eliminates instability spikes on dense graphs.
uncertain knowledge graphsspectral initializationdirichlet energygraph laplacianlink prediction
Blending Concepts: Benchmarking Visual Metaphor Generation in Text-to-Image Models
The study introduces VMetaphor-Bench, the first benchmark for evaluating visual metaphor generation in text-to-image (T2I) models, comprising 1,500 curated metaphors organized into three levels and ten categories. A hybrid evaluation framework combines a multiple-choice question protocol (9,594 questions across four fidelity levels) with dimension-based scoring along three perceptual dimensions. Testing 11 T2I models reveals persistent challenges in compositional structuring and cross-domain mapping, even for proprietary models, identifying visual metaphor generation as a critical frontier for future research.
visual metaphortext-to-image modelsbenchmarkcompositional structuringcross-domain mapping
RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection
RINSE (Robust Iterative Normality Self-Estimation) proposes a gradient-free, zero-shot framework for graph anomaly detection that adapts to unseen target graphs without labels or fine-tuning. The method iteratively estimates target normality by identifying reliable low-residual nodes, constructing a trimmed normality model, and fusing evidence via reliability-gated rank fusion and encoder ensembling. Evaluated on eight target graphs, RINSE achieves superior AUPRC (Area Under the Precision-Recall Curve) compared to baselines under two preprocessing protocols, with ablations validating its design choices.
zero-shot learninggraph anomaly detectionnormality estimationrank fusionencoder ensembling
ViSAR: Training-Free Adaptive-$k$ Retrieval for Visual Document Question Answering
ViSAR introduces a training-free adaptive-$k$ retrieval method for Document Visual Question Answering (DocVQA) that dynamically selects query-relevant document pages by constructing a query-conditioned similarity matrix in embedding space. The approach eliminates fixed top-$k$ retrieval, instead using semantic activation patterns to determine optimal page sets. Evaluations across multiple late-interaction encoders and Large Vision-Language Models (LVLMs) show ViSAR reduces Retrieval-Augmented Generation (RAG) latency by up to 58.7% while maintaining or improving answer accuracy over fixed and heuristic baselines. The similarity matrix structure also correlates with answer accuracy, suggesting retrieval quality-aware future improvements.
document visual question answeringretrieval-augmented generationlate-interaction encodersadaptive retrievalsimilarity matrix
DeepAffinity: Long-Term Aspect Preference Prediction in eCommerce using Small Language Models
DeepAffinity introduces a novel approach for Aspect Affinity prediction in eCommerce, forecasting user preferences for product aspects like brand, size, and color from time-ordered interaction histories. The method employs Small Language Models (SLMs) with structured prompts and task-specific prediction heads, outperforming standard generative fine-tuning techniques. Results demonstrate that general-purpose open-source LLMs perform poorly without task-specific tuning, emphasizing DeepAffinity's effectiveness in modeling nuanced user behavior. The approach enhances recommendation quality on a large-scale multinational eCommerce platform, showcasing its practical utility.
aspect affinitysmall language modelsstructured promptsprediction headstemporal prediction
CivBench: A Long-Horizon Benchmark for Tool-Mediated Agents in Civilization VI
CivBench introduces a benchmark for evaluating language model agents in long-horizon, tool-mediated environments using the Model Context Protocol (MCP). The benchmark spans 300+ turns, involves thousands of tool calls, and exposes 76 MCP tools with a narration layer converting visual game state into structured text. Agents were evaluated across four model families in 23 runs, revealing consistent patterns: strategic state under-monitoring (Proactive Monitoring Rate) and failure to execute near-term commitments (RAG@10 between 48.2% and 65.8%). Despite tool access and explicit guidance, agents frequently deviated from instructions, highlighting challenges in sustained planning and execution.
model context protocolproactive monitoring raterag@10long-horizontool-mediated
Addressing Trust in AI Systems through Education: A Didactic Perspective
The paper introduces ICE-T, a didactic framework designed to address trust calibration in AI systems through education. ICE-T integrates intermodal transfer, computational thinking, and explanatory thinking to enhance learners' understanding and reliance on AI. The framework leverages Bruner's modes of representation, the Use-Modify-Create progression, and a process model to foster representational richness, graduated process control, and error contextualization. By connecting to empirical literature on algorithm aversion, AI literacy, and mental model formation, ICE-T proposes trust calibration as an explicit educational objective, offering a scalable approach to improving AI literacy and appropriate reliance.
trust calibrationintermodal transfercomputational thinkingexplanatory thinkingai literacy
Scalable Kronecker-Fisher Approximation: Efficient Hessian Analysis for Billion-Parameter Language Models Compression
A scalable Kronecker-based approximation method is introduced for efficient Hessian analysis in billion-parameter language models, enabling practical computation without storing the full Fisher matrix. The approach identifies consistent vulnerability patterns, revealing that value projection layers exhibit highest sensitivity and strongest cross-layer correlations across model families. Extensive experiments on quantization, sparsification, inter-layer corruption, and post-corruption fine-tuning demonstrate strong correlation between the approximation and both performance degradation and recovery. This framework provides a theoretically grounded tool for identifying fragile components, enabling guided compression strategies like mixed-precision allocation, layer-wise sparsity, and adaptive low-rank decomposition.
kronecker-based approximationfisher matrixvalue projection layersmixed-precision allocationadaptive low-rank decomposition
Towards One-for-All Robustness Across a Continuum of Threat Levels
The Threat Conditional Network (TCN) introduces a unified model achieving robust performance across a continuum of adversarial threat levels. TCN employs a representation factorization framework, decomposing representation learning into a threat-invariant backbone and a lightweight threat-conditional adaptor. It uses Fourier-based embeddings and channel-wise affine modulation to condition on perturbation levels, trained over a distribution of budgets. Evaluations on CIFAR-10, CIFAR-100, and Tiny-ImageNet demonstrate that TCN matches or exceeds specialized ensembles, generalizes to unseen budgets, and transfers robustness under mismatched conditions, with only 4.6% parameter overhead.
threat conditional networkrepresentation factorizationfourier-based embeddingschannel-wise affine modulationperturbation budgets
UTP-Bench: Uncertainty-aware Travel Planning Benchmark
We introduce UTP-Bench, a large-scale benchmark for uncertainty-aware travel planning that addresses the limitations of deterministic benchmarks like TravelPlanner and TripCraft. The dataset integrates real-world travel data from 504 Indian cities, including attractions, accommodations, and multi-modal transportation networks, enriched with empirical delay distributions and crowd-density patterns. Three novel metrics—Buffer Adequacy Score (BAS), Crowd-Aware Timing Score (CATS), and Transport Delay Absorption Score (TDAS)—quantify itinerary robustness against stochastic disruptions. Experiments with LLMs (GPT-5, Qwen3, Mistral, Phi-4) reveal significant gaps between model-generated and human-authored plans, particularly in temporal buffering and crowd-sensitive scheduling.
uncertainty-aware planningmulti-modal transportationdelay distributionscrowd-density patternstemporal buffering
Coverage, Not Targeting: A Structural Regime in Multi-Turn Agent Credit Assignment
The paper introduces coverage as the primary structural regime for multi-turn agent credit assignment, challenging the prevailing targeting paradigm. It defines verifier information density (V_d = k/C) as a key predictor, showing that terminal-state verifiers operate in a low-V_d regime where uniform reward redistribution outperforms sparse targeting. Experiments on tau^2-bench and BFCL V3 demonstrate that uniform dense rewards consistently outperform sparse binary rewards, with a synthetic phase boundary at V_d* ~ 0.8. Results are replicated across model families, including ToolACE-2-8B, showing monotone dose-response curves and full-chain coverage parity. A matched-concentration shuffled control is proposed as a benchmark for targeting claims.
credit assignmentverifier information densityterminal-state verifiersuniform redistributionmatched-concentration control
Before the Script, Set the Stage: How Worldview Simulation Amplifies Psychologically Grounded Persuasion in Multi-Turn Jailbreaking
The paper introduces BLUEPRINT, a safety-evaluation framework for multi-turn jailbreaking attacks, combining WORLDVIEWSIM, a cross-turn situational context module, with a factorized social-influence strategy space. Using Monte Carlo Tree Search, BLUEPRINT optimizes turn-level combinations of 18 theory-grounded influence factors across a four-turn trajectory. Evaluated on six frontier models, it achieves near-ceiling attack success rates (ASR) with an average of 2.46 queries, revealing model-specific vulnerabilities and a common recovery pathway through concrete task framing. Ablations highlight the importance of operational cues, with actionable requests and gain framing being particularly effective, while some legitimacy appeals backfire. Findings emphasize the need to monitor dialogue state for making unsafe requests appear executable.
blueprintworldviewsimmonte carlo tree searchattack success ratemulti-turn jailbreaking
Evidence for Shared Routing Geometry and Dynamics in Sparse Mixture-of-Experts
This work identifies a shared geometric structure underlying routing decisions in sparse mixture-of-experts (MoE) models across layers. Using generalized orthogonal Procrustes analysis, the authors align router control subspaces into a canonical representation, demonstrating that routing-state evolution follows reusable dynamics (R²=0.39–0.71, retaining 79–90% predictive power). A matched-rank comparison separates generic cross-layer predictability from routing-specific information, showing router-control states preserve expert choices more faithfully than residual representations. Predicted canonical states preserve local routing behavior and improve negative log-likelihood by 15.7% on OLMoE and 6.2% on Phi over 10-router horizons.
sparse mixture-of-expertsgeneralized orthogonal procrustes analysisrouter-control statescanonical representationnegative log-likelihood
Contrastive Explanations in Quantitative Bipolar Argumentation Frameworks
The paper introduces contrastive explanations for Quantitative Bipolar Argumentation Frameworks (QBAFs), focusing on explaining differences between two topic arguments rather than individual arguments. It proposes a general form of contrastive attribution functions (CAFs) and establishes properties these functions should satisfy. Three specific CAFs are introduced: removal-based, gradient-based, and Shapley-value-based, each studied for their properties. The utility of these contrastive explanations is demonstrated in practical applications, particularly in healthcare and bias identification scenarios, enhancing the explainability of AI models in classification tasks.
quantitative bipolar argumentation frameworkscontrastive explanationscontrastive attribution functionsshapley-valuesbias identification
PolERo: Studying Political Evasion in Romanian
We introduce PolERo, a dataset of 3,574 human-annotated Romanian political question-answer pairs, addressing the gap in political evasion research beyond English. We evaluate TF-IDF baselines, fine-tuned encoders, a sliding-window encoder, and zero/few-shot LLM prompting under matched conditions, while studying cross-lingual transfer via joint bilingual training and machine-translation-based data augmentation. Results show fine-tuned encoders are competitive, cross-lingual transfer is asymmetric, and ambivalent evasion categories involving pragmatic cues remain challenging across all model families.
political evasioncross-lingual transferfine-tuned encodersmachine-translationpragmatic cues
MultiGhostBench: A Multilingual Benchmark for Long-Form LLM-Generated Text Attribution under Distribution Shifts
We introduce MultiGhostBench, a multilingual benchmark for evaluating LLM-generated text attribution across distribution shifts. The dataset comprises 928 books generated by five recent LLMs across six languages and three scripts, with an average length of 59K words per book. Evaluation of authorship attribution methods reveals no single method consistently outperforms others, with performance degrading under domain, author, and language shifts. Transformer-based detectors demonstrate cross-lingual generator-related information retention, while statistical and fingerprint-based methods show stronger language dependence. The benchmark supports robust method development and evaluation.
multilingual benchmarkauthorship attributiondistribution shiftstransformer-based detectorsfingerprint-based detectors
Percolation Dynamics in Optimization : Variance Cascades and Discrete Scale Invariance
This work elucidates the dynamics of Stochastic Gradient Descent (SGD) in deep neural networks by modeling it as a percolation process. The authors demonstrate that architectural symmetries induce subnetworks to merge in discrete simultaneous blocks rather than sequentially, manifesting as variance spikes in a macroscopic order parameter analogous to physical phase transitions. The analysis extends to Adam and AdamW optimizers under a heavy-tailed noise model, revealing a trapping mechanism and scaling cascade. This provides insights into how SGD steers networks toward invariant sets corresponding to simpler subnetworks over time.
stochastic gradient descentpercolation processarchitectural symmetriesmacroscopic order parameterheavy-tailed noise
Diagnosing with Insights: Structured Analysis of Agent Failures via Behavioral Abstractions
AGENTSCOPE introduces a neuro-symbolic approach for diagnosing failures in LLM agents by abstracting agent trajectories into structured representations and leveraging neural invariants to specify behavior properties. The method employs LLM-guided reasoning over these structured representations to identify failure steps and types. Evaluated on publicly available datasets (Who&When) and a custom dataset (AgentErrata), AGENTSCOPE significantly outperforms state-of-the-art methods in fault localization and attribution accuracy. This integration of structured abstractions with LLM reasoning enables reliable and interpretable diagnosis of agent failures.
neuro-symbolicneural invariantsllm agentsfault localizationstructured representations
NE-R1: Enhancing Named Entity Recognition Model via Reinforcement Learning
NE-R1 introduces a reinforcement learning-enhanced framework for adaptive retrieval-augmented Named Entity Recognition (NER), addressing challenges in long-tail and domain-specific entity recognition. The method employs a 'retrieval-on-demand' mechanism integrated via a two-stage training process: multi-task instruction tuning initialization followed by end-to-end RL optimization with Chain-of-Thought (CoT). A multi-dimensional reward function balances accuracy and retrieval efficiency. NE-R1 achieves state-of-the-art performance, improving average F1 scores by 2.52% in in-domain and 1.18% in zero-shot cross-domain evaluations across various benchmarks.
named entity recognitionretrieval-augmented generationreinforcement learningchain-of-thoughtzero-shot learning
Towards a Foundational Ontology for Identifying and Resolving Contradictions in Dialogue-based Human-Robot Interactions
The article proposes Activity Theory-based Foundational Ontology (ATFOt), a formal computational framework for representing and resolving contradictions in dialogue-based human-robot interactions (HRI). Using METHONTOLOGY and concepts from Activity Theory, the authors define dialogues and contradictions through natural language, set theory, and First Order Logic (FoL). Preliminary results include three novel principles guiding HRI dialogues and FoL formulations of contradiction concepts. This foundational ontology aims to be interoperable across HRI and human-agent interaction domains, addressing the current lack of domain-independent representations for collaborative interaction errors.
activity theoryfoundational ontologyhuman-robot interactionfirst order logicdialogue-based interaction
Fair Stable Matching: A Nash Social Welfare Approach
We introduce SNSW-Alg, an algorithm that maximizes Nash social welfare (NSW) in the stable marriage problem while preserving stability, achieving equitable outcomes among participants. The method operates under rank-induced utilities and runs in Õ(n⁴) time, where n is the number of men or women. Empirical evaluations across diverse preference distributions demonstrate significant fairness improvements without substantial losses in regret, egalitarian criterion, or sex equality. The stable matchings produced by SNSW-Alg are statistically Pareto-undominated compared to those based on other fairness measures, offering insights for designing fair-stable matching systems.
nash social welfarestable marriage problemrank-induced utilitiespareto-undominatedegalitarian criterion
Subcellularly Resolved Single-Cell Embedding Learning with Transcriptomic data, Protein Structure and Localization Information
The authors introduce a multimodal framework for learning subcellularly resolved cell embeddings by integrating RNA expression profiles, protein sequence representations, and protein structural information. They employ a cross-attention architecture to model interactions between transcriptomic, sequence, and structural modalities within distinct subcellular compartments. The resulting embeddings capture fine-grained subcellular organization, preserving spatially organized biological information while integrating complementary molecular signals. This is the first unified framework to jointly incorporate transcriptomic data, protein sequences, and structural knowledge for subcellular resolution cell embedding learning.
cell embeddingscross-attention architecturesubcellular resolutionprotein structural informationtranscriptomic data
AGI Maze Prediction Datasets: A Compact Benchmark for Learning World Dynamics with Transformers
The AGI Maze Prediction Datasets and Benchmark is introduced as a lightweight testbed for evaluating world modeling capabilities in Transformers and predictive models. The benchmark features procedurally generated grid worlds with tasks including per-step transition prediction, fixed-horizon state prediction, and sequential textual-observation prediction. Training and validation splits are source-maze-disjoint to assess transferable action-conditioned dynamics. Baseline byte-level Transformers are compared with two memory-augmented architectures: a generic latent-memory Transformer and a pseudo-video spatial-memory Transformer. The latter achieves perfect validation accuracy on fixed-horizon tasks and improves sequential text-trace prediction, demonstrating the utility of structured, task-aligned working memory over additional latent capacity.
world modelingtransformersaction-conditioned dynamicslatent-memorypseudo-video
SALA: Semantic-Aware Logical Alignment for Complex Reasoning in In-Context Learning
SALA introduces a Semantic-Aware Logical Alignment framework for improving in-context learning (ICL) in complex reasoning tasks by addressing limitations in traditional retrieval and logic-based methods. SALA automatically learns task-specific reasoning operations, embeds them into a continuous semantic space, and employs dynamic time warping (DTW) for flexible alignment of reasoning sequences. This soft matching approach enhances interpretability and adaptability to diverse reasoning processes. Evaluations across four reasoning benchmarks and three large language models (LLMs) demonstrate SALA's superiority over existing demonstration selection methods, with further analysis validating the roles of operation induction and logical semantic alignment.
in-context learningsemantic alignmentdynamic time warpingreasoning operationslarge language models
ORB-SVM : An Innovative Hybrid Framework for Efficient Brain Tumor Detection from MRI Scans
The study introduces ORB-SVM, a hybrid framework combining Oriented FAST and Rotated BRIEF (ORB) for feature extraction and Support Vector Machine (SVM) for classification, achieving efficient brain tumor detection from MRI scans. The method reduces data dimensionality by approximately 99.5%, minimizing non-informative background while preserving diagnostic patterns. Evaluated on the Br35H dataset, the framework achieves 97.5% classification accuracy, offering a computationally efficient alternative to over-parameterized deep learning models.
orb-svmfeature extractionsupport vector machinemri scansbrain tumor detection
What Is Worth Representing? Representational Empowerment for Continual Model Construction
The paper introduces Representational Empowerment (RepEmp), a novel metric for scoring candidate representational elements in continual model construction based on their capacity to enhance future modeling and planning. The authors propose a hierarchical Curator-Actor architecture to operationalize RepEmp, which evaluates elements by their control over internal representations rather than external states. Experiments across causal-learning and planning domains demonstrate that RepEmp-guided construction outperforms information-gain alternatives in structure recovery, cross-task transfer, and library compactness. Ablation studies confirm RepEmp's critical role in these improvements, establishing it as a key principle for resource-efficient continual model construction.
representational empowermentcontinual model constructioncurator-actor architectureinternal representationscross-task transfer
DiffIE: Diffusion-based Open Information Extraction
We present DIFFIE, a diffusion-based approach for Open Information Extraction (OpenIE) that leverages stochastic conditional discrete diffusion as the extraction mechanism. DIFFIE generates candidate triplets through independent reverse-diffusion trajectories over per-token role tags, which are clustered and ranked to form the output. This method decouples the extraction budget from training, allowing inference-time control over pool size and returned extractions. DIFFIE achieves state-of-the-art performance in CaRB (1-1) for both F1 and AUC, outperforms ClausIE in BenchIE, and remains competitive in standard CaRB and WiRe57 evaluations. Ablations confirm the superiority of uniform discrete diffusion over absorbing state diffusion and non-diffusion stochastic taggers.
diffusion-basedopen information extractiondiscrete diffusionreverse-diffusionstructured prediction
Improving Evaluation Realism with Inference-Time Compute and Deployment Scaffolds
The paper introduces two techniques to enhance the realism of alignment evaluations by reducing model awareness of being tested. Critique refinement increases inference-time compute by generating multiple candidate actions, refining them using feedback from the target model, and selecting the most deployment-like option. DISH (Deployment-Imitating SWE-Agent Harness) wraps the target model in an agent harness to narrow the gap between simulated and real coding environments. Experiments demonstrate that combining both techniques yields greater realism improvements than using either alone, showing that automated methods can effectively enhance evaluation realism with additional compute.
alignment evaluationinference-time computecritique refinementdishdeployment-like
SEAL: Reinforcing Global Safety in Mixture-of-Experts through Shared Expert ALignment
SEAL introduces a parameter-efficient defense for Mixture-of-Experts (MoE) models by leveraging shared experts to enhance global safety alignment. The method proposes a plug-and-play adapter attached to shared experts, with SEAL++ adding an orthogonal constraint to preserve pre-existing safety subspaces. Evaluated across six attack scenarios combining adversarial inputs and neuron pruning, SEAL reduces attack success rates by up to 60% with a capability cost of at most 1.4% on a five-benchmark average. This approach addresses structural vulnerabilities in MoE routing by utilizing always-activated shared experts as router-independent safety anchors.
mixture-of-expertsshared expertssafety alignmentadversarial attacksparameter-efficient
SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology
SCX Router introduces a lightweight model selection system for optimizing inference-time LLM usage across speed, cost, and quality. It employs a 0.6B-parameter Qwen3 decoder with a shallow bidirectional scorer, leveraging a decoder-KV execution path for efficient text-only key-value caching and transient label evaluation. The system integrates a task ontology with 23 families, 115 task types, and 345 routable subtypes, trained on 150,000 verifier-scored tasks. SCX Router achieves a top-1 score of 0.707 on a 1,000-task subset, outperforming the strongest fixed model's 0.696.
decoder-kvtask ontologybidirectional scorertransient label evaluationkey-value cache
VoRTeC: Taming Foundation Flow for One-step Real time Video Compression
VoRTeC introduces a novel video compression framework leveraging foundational flow models to address blurring artifacts and latency in neural and diffusion-based approaches. The method encodes latent video representations compactly, predicts compressed representation positions along flow trajectories, and integrates multi-scale priors, enabling one-step decoding with high perceptual fidelity. It maintains temporal consistency via tail-frame reuse and prior caching. Experiments show VoRTeC reduces bit consumption by 58% compared to diffusion-based methods, achieving decoding speeds of 13 FPS at 720p and 32 FPS at 480p, with speedups ranging from 3x to 197x.
video compressionflow trajectoriesmulti-scale priorstemporal consistencylatent representations
RouteGraph-Mona: Confusion-Aware Routing Fine-Tuning for Mineral Image Classification
RouteGraph-Mona introduces a confusion-aware routing fine-tuning method for mineral image classification, addressing limitations in Multi-cognitive Visual Adapter (Mona) by replacing static multi-scale aggregation with sample-adaptive routing. The method employs class-wise route anchors and confusion-weighted margins to regularize routing signatures, promoting class-consistent patterns and greater separation between visually similar categories. Evaluated on three public mineral image datasets with two visual backbones, RouteGraph-Mona consistently outperforms Mona in mean accuracy and remains competitive with existing fine-tuning methods and baselines.
mineral image classificationsample-adaptive routingroute anchorsconfusion-weighted marginsmulti-cognitive visual adapter
Auditory Illusion Benchmark for Large Audio Language Models
The Auditory Illusion Benchmark (AIB) introduces the first systematic evaluation of Large Audio Language Models (LALMs) using auditory illusions, addressing a gap in existing benchmarks focused on visual or general audio tasks. AIB comprises ten representative illusions across music, sound, and speech, annotated for knowledge-based priors, and pairs model evaluations with controlled human listening studies for direct comparison. Results reveal systematic differences: LALMs remain signal-faithful on low-level acoustic illusions but exhibit more human-like responses with linguistic or musical priors, though none match human perceptual profiles. This highlights LALMs' limitations as cognitive models and establishes auditory illusions as a rigorous testbed for probing neural models.
auditory illusionslarge audio language modelsknowledge-based priorscognitive modelshuman listening studies
Do Large Language Models Capture the Diversity in their Training Data?
The study quantifies the conditional diversity gap between large language models (LLMs) and their training data using information-theoretic measures of conditional entropy. By analyzing paired input-output samples from models like OLMo, Pythia, and GPT-Neo, the authors demonstrate that model-generated outputs exhibit systematically lower conditional entropy than training data across scales and domains. They propose a convex optimization-based post-hoc correction using matrix-entropy projection to increase output diversity while preserving model fidelity, supported by theoretical guarantees on the concavity of the entropy functional.
conditional entropymatrix-entropy projectiondiversity gapvon neumann entropyconvex optimization
CoMerge: Conflict-Driven Preference Optimization for Multi-Task Model Merging
CoMerge introduces a conflict-driven preference optimization framework for multi-task model merging, addressing parameter interference in LLMs without full retraining. The method reformulates merging as a preference optimization problem, using defects from naive merging methods as hard negative samples to construct self-supervised preference pairs. It refines tensor-wise merging coefficients to mitigate parameter-space conflicts while preserving task-specific capabilities. Experiments demonstrate CoMerge achieves a 0.9968 normalized performance on MergeBench, outperforming data-free and data-driven baselines, and improves conflict-sensitive tasks on Llama-3.1-8B-Instruct while optimizing only 1,445 scalar coefficients.
model mergingpreference optimizationparameter interferenceself-supervised learningtensor-wise coefficients
PaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation
PaperCompiler introduces a framework for faithful paper-to-code generation by compiling paper-grounded evidence into explicit repository-level implementation specifications. It preserves source provenance, distinguishes paper-supported and inferred information, and encodes non-degradation requirements, ownership assignments, and cross-file dependencies. Repository generation proceeds under these specifications while allowing flexibility for local engineering choices. PaperCompiler achieves a 13.8% relative improvement in reference-based fidelity (from 3.64 to 4.15) and reduces high-severity evaluator critiques (from 13.2% to 6.1%) on Paper2CodeBench.
repository-levelspecification compilationpaper-to-codecross-file dependenciesnon-degradation requirements
CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation
CrashDiffuser introduces a VLM-guided diffusion framework for fine-grained safety-critical traffic scenario generation, enabling precise control over collision contact regions on target vehicles. The method decouples semantic collision reasoning from trajectory synthesis using a hierarchical collision-intent interface, where a VLM extracts scene-level context and predicts structured action tuples to condition a diffusion model. This approach incorporates collision-guided sampling, candidate selection, and short-horizon replanning to adapt to evolving vehicle behavior. Evaluated on WOMD-derived closed-loop scenarios, CrashDiffuser achieves a 50.33% target-collision rate in a single attempt, 67.98% after three attempts, and a 40.05% contact-region control success rate, while maintaining competitive trajectory naturalness.
diffusion frameworkcollision reasoningtrajectory synthesisclosed-loop scenarioscontact-region control
Retrosynthesis of Synthetic Media for Explainable AI Provenance Forensics
The authors propose a self-referential retrosynthesis framework for explainable AI provenance forensics in synthetic media, addressing the challenge of tracing generative model outputs without modifying generator architectures. The method employs a jointly optimized encoder-decoder pair to implement a self-embedding mechanism, enabling round-trip consistency verification. During inference, client inputs are encoded and processed by the generator, with forensic verification achieved by analyzing consistency between resynthesized and query images. Experiments demonstrate that encoded inputs maintain visual fidelity comparable to original generator outputs, while decoded images reliably trace back to source inputs, providing interpretable evidence for generative content provenance.
retrosynthesisprovenance forensicsself-embedding mechanismround-trip consistencygenerative model
Codebook Agent: Amortized Topology Design for LLM Multi-Agent Systems
Codebook Agent introduces an amortized topology design method for LLM multi-agent systems that improves accuracy and efficiency by compressing successful topologies into a 16-entry codebook via vector-quantized autoencoding. It employs a reward-weighted MLP to map query embeddings to code distributions and a proxy MLP to rerank candidates based on utility and token cost. This approach eliminates iterative search and message passing at test time, achieving 84.6 average accuracy across six benchmarks, emitting topologies in 2.4 ms, and reducing LLM token usage by 21.9--33.2%.
vector-quantized autoencodertopology designllm multi-agent systemsreward-weighted mlptoken consumption
APEx: Distillation of Agent Procedural Experience for Adaptive Deep Research Question Answering
APEx introduces a hierarchical experience utilization framework for adaptive deep research question answering, organizing interaction history into instance-level trajectory memories and category-level procedural skills. The framework employs a closed-loop architecture with Executor, Distiller, and Planner modules, optimized via a three-stage alternating GRPO training paradigm for reward-guided skill distillation. At test time, distilled skills enable online Planner adaptation through skill-guided test-time reinforcement learning with skill-alignment regularization. APEx achieves state-of-the-art performance, surpassing GPT-5.4 by 14.7 points and the strongest memory-augmented baseline by 3.0 points across 7 benchmarks.
hierarchical experienceskill distillationtest-time reinforcementskill-alignment regularizationgrpo training
DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space
DiffuSearch introduces a hybrid trajectory planner for autonomous driving that unifies objectives across generation and refinement stages to address inconsistencies in traditional modular architectures. The method employs a two-stage approach: a guided diffusion model generates scene-consistent joint trajectory predictions using differentiable guidance functions, followed by Monte Carlo Tree Search (MCTS) for explicit, local refinement in a discretized action space, both guided by shared driving objectives. Evaluations on nuPlan and interPlan benchmarks show state-of-the-art performance, with significant reductions in collisions and improved comfort, particularly in complex scenarios. Ablation studies highlight MCTS refinement as the primary driver of gains, with shared objectives further enhancing consistency.
trajectory planningdiffusion modelmonte carlo tree searchautonomous drivingscene-consistency
SAUF-Net: Structure--Appearance Representation Learning with Uncertainty Feedback for Semi-Supervised Medical Image Segmentation
SAUF-Net introduces a Structure--Appearance Uncertainty Feedback Network for semi-supervised medical image segmentation, addressing feature reliability by disentangling structural and appearance representations. The method employs a Structure--Appearance Decomposition Module (SADM) to separate bottleneck features, a Disentangled Guidance Module (DGM) for structure-aware segmentation, and an Appearance-Swapped Consistency branch for stability. A reliability-map-guided dual-head discriminator provides feature-level uncertainty feedback. Evaluated on ISIC-2016 and Kvasir-SEG, SAUF-Net outperforms state-of-the-art methods, particularly in low-label scenarios.
semi-supervised learningmedical image segmentationfeature disentanglementuncertainty feedbackappearance-swapped consistency
LLM-as-a-Judge Is Not an Oracle: Why Self-Improving Agents Need Deterministic Guardrails
The paper introduces PROCTOR, a Teacher-Student loop framework designed to address failures in self-improving agent pipelines where LLM judges act as oracles. The authors identify four classes of evaluation signal failures—judge bias, harness and metric failures, ground-truth errors, and reward hacking—and propose five deterministic guardrails to mitigate these issues. These guardrails include hermetic sandboxes, capability-disjoint roles, acceptance checks, frozen holdouts, and canary cases. The framework was tested in production across contract analysis, compliance review, and code quality tasks, successfully preventing several failure modes while acknowledging limitations where the Teacher, an LLM judge, still failed.
self-improving agentsdeterministic guardrailsllm judgesreward hackingteacher-student loop
Task-Level Natural Language Priors as Learning Signals for Low-Resource LLM Training
We propose Prior-Guided Tuning (PGT), a method incorporating task-level natural-language priors as auxiliary learning signals for low-resource LLM training. Our approach, Contrastive Prior Steering (CPS), maintains the original supervised objective while adding positive and negative prior-conditioned auxiliary losses to enhance task-consistent learning. Experiments on AmbiMath, Jigsaw, and MNLI/HANS demonstrate CPS's effectiveness: it achieves 97.6% accuracy on AmbiMath, improves Macro F1 by 9.5 points on Jigsaw with 1/10 training data, and enhances non-entailment accuracy by 8.3 and 5.2 points for LLaMA 3.1 8B and Qwen 2.5 7B on HANS, respectively, while maintaining MNLI accuracy.
prior-guided tuningcontrastive prior steeringauxiliary learning signalslow-resource trainingtask-level priors
Propose to Learn, Learn to Propose: Evaluability-Aware Assistance under Bounded Rationality
The paper introduces ProSE, a framework for evaluability-aware proposal planning in AI assistance, addressing bounded rationality by modeling proposals as both task interventions and preference probes. It formalizes this as a hidden-parameter sequential assistance problem, using a KL-regularized bounded-rational binary response model to balance value gain and evaluability penalties. The method, extsc{ProSE-Plan}, employs depth-2 Bayes-adaptive planning to score proposals by potential responses and posterior updates. In graph simulations, extsc{ProSE-Plan} outperforms evaluability-unaware and myopic baselines when evaluation cost is limiting, demonstrating the importance of informative proposal selection.
evaluability-awarebounded rationalitysequential assistancekl-regularizedbayes-adaptive planning
PGPO: Potential-Guided Policy Optimization for Multi-Turn Agentic Tasks
The paper introduces Potential-Guided Policy Optimization (PGPO), a reinforcement learning method for multi-turn agentic tasks that improves fine-grained credit assignment. PGPO estimates empirical state potentials from anchor-state-group return statistics and computes action advantages via potential differences between states, enabling cross-trajectory credit propagation. Evaluated on ALFWorld and WebShop, PGPO outperforms recent group-based RL methods, particularly in providing informative credit signals for failed trajectories with minimal training overhead.
reinforcement learningcredit assignmentmulti-turn taskspolicy optimizationstate potentials
InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Models
We introduce InfraPatch, a white-box adversarial attack framework targeting infrared vision-language models (IR-VLMs) across multiple tasks. The method optimizes compact grayscale patches within a 5% local-area budget, combining proxy-guided placement with task-specific semantic objectives to induce target behaviors in classification, captioning, and visual question answering. Evaluated on ten IR-VLM variants using 300 synthetic infrared images, InfraPatch achieves targeted success rates from 86.00% to 100%, with proxy location search improving success by 6.67-10.33 percentage points over random placement on CLIP and BLIP-2. Ablation studies reveal significant architectural and task-specific vulnerabilities, demonstrating the effectiveness of small grayscale patches in injecting target semantics across IR-VLMs.
infrared vision-language modelsadversarial patchgrayscale optimizationproxy-guided placementtask-specific semantics
PhoenixNest-Video: Evidence-Grounded Multimodal Agent Framework for Automated Video Interview Assessment
PhoenixNest-Video introduces a multimodal agent framework for automated video interview assessment, addressing the limitations of opaque AI scoring and inconsistent human evaluation. The framework constructs a semantic video graph as structured working memory, performs rubric-conditioned retrieval with cross-modal verification across visual, audio, and textual streams, and generates per-criterion scores anchored to candidate materials. A Scorer trained via Rubrics-based Reinforcement Learning with dual rewards for rubric alignment and score-level differentiation internalizes multi-level rubrics. The system achieves 91.50% grade-level accuracy on VInterview-2025, outperforming larger proprietary models and providing traceable evidence for human review.
multimodal agentsemantic video graphrubrics-based reinforcement learningcross-modal verificationstructured working memory
Signal or Noise? Auditing Rotation-Induced Saliency Drift in Medical and Aerial Imaging
The paper introduces EquiGrad-CAM, a training-free method to improve rotation-equivariance in post-hoc saliency maps for vision models. It identifies that saliency drift under rotation stems from spatial activation tensors rather than channel weights, as classifiers discard this movement. EquiGrad-CAM aggregates saliency maps from T rotated views, inverse-rotates them into a common frame, and averages them. Evaluated on ImageNet-1K, it improves equivariance by +36.0% (ResNet-50), +87.5% (VGG-16), and +247% (ViT-B/16) over single-view Grad-CAM. It outperforms rotation-augmented training without retraining, boosts zero-shot CLIP by +145%, and provides consistent explanations on PatchCamelyon and RESISC45. PEUM, a by-product, ranks explanations by reproducibility.
saliency mapsrotation-equivariancegrad-camspatial activationzero-shot clip
SkillGLoW: Procedural-Family Skill Consolidation for Self-Improving Agents on Long-Horizon Task Streams
SkillGLoW introduces procedural-family skill consolidation for LLM agents handling long-horizon task streams, addressing limitations of global-document and per-task-pool approaches. The method aggregates local skills into de-instantiated global priors by clustering related tasks, regenerating instance details per task, and using a commit gate to prevent library degradation. Evaluated across mathematical reasoning, terminal automation, software repair, and embodied control benchmarks with three models, SkillGLoW improves hard-task performance by 17.2 points over no-skill baselines, maintains 3.6x more compact libraries than per-task pools, and outperforms single-document optimization in 15/21 cases. Unmodified libraries also boost ALFWorld unseen-task success from 73.9% to 83.9%.
skill consolidationprocedural familiesde-instantiated priorscommit gatelong-horizon tasks
PEARL: Path-Entity Aligned Relational Learning with Contextual Subgraphs for Inductive Knowledge Graph Completion
PEARL introduces a Path-Entity Aligned Relational Learning framework for inductive knowledge graph completion (IKGC), addressing the challenge of predicting missing links involving unseen entities. The method constructs query-specific contextual subgraphs, employs an LLM-guided retriever to distill semantically relevant paths, and builds a bipartite interaction graph to adapt path embeddings to local and global structural evidence. A dual-view contrastive objective suppresses noise from enlarged contexts. PEARL achieves the best average Hits@10 on WN18RR, FB15k-237, and NELL-995 benchmarks, with ablation studies validating the contributions of contextual subgraph modeling, semantic path retrieval, path-entity interaction, and contrastive regularization.
inductive knowledge graph completioncontextual subgraphsemantic path retrievalpath-entity interactioncontrastive regularization
ASCII Attack: Recontextualising Harmful Requests as Artistic Critique in Large Language Models
The ASCII Attack introduces a novel method for bypassing safety alignment in large language models by recontextualizing harmful requests as ASCII art, presented as artistic critique. This single-turn, black-box attack embeds fully legible harmful content within ASCII characters, contrasting with direct-question controls to isolate the effect. Evaluated across eleven models and eight harm topics, a harm-aware classifier judged 62% of framed prompts harmful compared to 42% of controls, with success rates reaching 93% on the most susceptible model. The attack's effectiveness varies more by model than topic and does not diminish with scale, indicating mismatched generalization in safety alignment.
ascii attacksafety alignmentharm-aware classifierblack-box attackmismatched generalization
SMart: A Multi-source Multi-phase Time Series Representation Transfer Framework
The paper introduces SMart, a multi-source multi-phase time series representation transfer framework addressing limitations in transformer-based time series representation learning (TSRL) and single-source dataset utilization. SMart incorporates two novel mechanisms: a multi-phase recurrence plots recovery task with three modes to encode time series dynamics, and a source dataset selector for multi-source pre-training. Evaluations demonstrate SMart's superiority over state-of-the-art models, achieving up to 19.5% reduction in mean absolute error for regression and 1.34% accuracy improvement for classification on uni- and multi-variate time series datasets.
time series representation learningmulti-source transferrecurrence plotstransformer-based frameworkself-supervised learning
Schrödinger Bridges on Lie Group Manifolds for Probabilistic Intrinsic Generation
The paper introduces Schrödinger bridges for probabilistic generative modeling on Lie group manifolds, addressing errors from Euclidean approximations in geometric data. It proposes two computational methods: Wrapped-Kernel Bridge Calibration (WKBC) for compact Abelian groups and Reciprocal Conditional-Control Bridge Matching (RCCBM) for non-Abelian groups, both handling observed and unobserved endpoint variables. Theoretical analysis provides error bounds for path metrics, separating approximation errors. Experiments validate the approach on protein/RNA torsions, SO(3), U(n), and protein conformational transitions using mdCATH trajectories.
schrödinger bridgeslie group manifoldsprobabilistic generationkinetic dynamicserror bounds
Examining the Vulnerability of Multi-Agent Medical Systems to Human Interventions for Clinical Reasoning
This study examines how human interventions at 'fault points'—critical moments where AI agents' reasoning is vulnerable—affect diagnostic accuracy in multi-agent medical systems. Using simulated doctor-patient dialogues from the MedQA dataset, the authors measured performance shifts from correct (up to +40% accuracy) and incorrect/bias-inducing interventions (up to -6% accuracy, increased drift/uncertainty). The analysis also identified behavioral parallels between agent biases (e.g., premature closure, misleading cue susceptibility) and real-world clinical cognition, suggesting targeted interventions at fault points could enhance diagnostic robustness.
multi-agent systemsclinical reasoningdiagnostic accuracycognitive biasmedqa
FUSE: An Evaluating Framework for Dangerous Capabilities of LLMs
We introduce FUSE, a modular framework for evaluating dangerous capabilities of LLMs across three orthogonal pipelines: Knowledge (K), Defense (D), and Harm (H). The framework employs pluggable modules for scenario seeds, knowledge banks, hazard queries, and judge rubrics, enabling domain-specific evaluations while maintaining a core evaluation engine. Applied to 12 commercial LLMs from four families using a chemical-biological module, FUSE reveals divergent dangerous-capability profiles across models and families, with newer models deepening knowledge but only partially improving defense. Cross-judge consistency (bootstrap ρ > 0.79) and pipeline orthogonality (K–D–H inter-correlations ρ ∈ [0.32, 0.52]) validate reliability.
dangerous capabilitiesorthogonal pipelineschemical-biological modulecross-judge consistencypipeline orthogonality
GeoSPRINT: Geometric Redundancy-Aware Step Pruning for Inference in Diffusion Trajectories
GeoSPRINT introduces a training-free framework for accelerating diffusion model inference by constructing non-uniform sampling schedules based on the geometry of denoising trajectories. The method detects geometrically redundant steps via hyperplanarity tests in latent space (using QR factorization) and allocates more steps to high-curvature regions, quantified by a novel trajectory projection score (α_traj). Evaluated on CIFAR-10, LSUN Church, and Stable Diffusion v1.5, GeoSPRINT improves FID over uniform DDIM schedules by 0.7-1.1 (CIFAR-10), 0.22 (LSUN Church), and up to 1.93 (Stable Diffusion) at matched NFEs, demonstrating that trajectory geometry optimizes step allocation without retraining.
diffusion modelsstep pruningtrajectory geometryhyperplanarity testqr factorization
OBJECTION! Lawyer Agents Mitigate Guilty Bias in Legal Judgment Prediction
The paper introduces OBJECTION, an inference-time pipeline that mitigates 'Guilty Bias' in Legal Judgment Prediction (LJP) models by integrating an Adversarial Lawyer Agent into three-step reasoning (offense, unlawfulness, culpability). The agent challenges presumptions of guilt by injecting legal defense arguments, addressing bias from prosecutorial training data. Evaluated on a new 'Natural Innocent' dataset (3.4k real-world cases), OBJECTION reduces the False Guilty Rate from 82.93% (SOTA baseline) to 16.69%, demonstrating robust legal reasoning. This advances alignment with the presumption of innocence.
legal judgment predictionguilty biasadversarial lawyer agentfalse guilty ratepresumption of innocence
Beyond Modality Harmony: Orthogonal Purification and Topology-Guided MoE for Conflict-Aware Multimodal Recommendation
The paper introduces OrthoRec, a conflict-aware multimodal recommendation system addressing modality-topology conflicts in Multimodal Recommender Systems (MRSs). The method combines Collaborative-Guided Orthogonal Purification (CGOP) to decouple multimodal features into collaborative-aligned and noise directions, and Topology-Aware Routing Mixture-of-Experts (TAR-MoE) to adaptively inject purified modalities. A safe-SSL objective penalizes forced contrastive alignment of contradictory pairs. Evaluations on three Amazon datasets demonstrate OrthoRec's superiority over baselines, with improved robustness against modality noise and item sparsity.
multimodal recommender systemsorthogonal purificationmixture-of-expertscontrastive learningmodality-topology conflicts
OmegaUse-SOP: SOP Engineering for Professional Computer Use from Human Demonstrations
OmegaUse-SOP introduces a human-in-the-loop SOP Engineering system that converts human demonstrations of professional computer use into reusable GUI-agent skills. The method comprises four modules (Observe, Reason, Configure, Execute) that record multimodal GUI traces, abstract low-level events into semantic instructions, incorporate domain rules, and execute skills via step-wise grounding. Evaluated on photovoltaic simulation workflows in PVsyst 7.2 with a power-sector client, the system demonstrates improved GUI-agent reliability for domain-specific SOP tasks.
gui agentsstandard operating proceduresmultimodal tracesdomain knowledgehuman-in-the-loop
Online Non-Monotone DR-Submodular Maximization Matching the Offline $0.401$ Factor
The paper achieves a 0.401 approximation factor for online maximization of nonnegative, non-monotone DR-submodular functions over compact convex down-closed subsets in the $d$-dimensional unit cube, matching the best known offline factor. The proposed algorithm operates in the post-decision full-information value-oracle model, leveraging a weighted online learner to control residual terms cumulatively instead of running offline constructions on changing objectives. It achieves sublinear approximate regret with $O(T^{3/4})$ regret and $O(dT^{1/4})$ oracle calls per round, extendable to $O(T^{4/5})$ regret with one call per round via batching. Under a positive-anchor condition, randomized blocking retains the 0.401 factor with $O(T^{5/6})$ one-point bandit regret.
dr-submodularonline maximizationapproximation factorvalue-oraclesublinear regret
A Power Law in Logarithm's Clothing: On the Scalability of Graph-Based Vector Search
The study challenges the poly-logarithmic scaling claim of graph-based vector search costs, demonstrating a Sublinear Power Law growth (N^c, 0 graph-based indexingsublinear power lawintrinsic dimensionalityapproximate nearest neighborscaling behavior
EmoStance: Response-Side Affective-Orientation Control for Empathetic Response Generation via Emoji Weak Supervision
EmoStance introduces response-side affective-orientation control for empathetic response generation, leveraging multi-annotator emoji distributions as weak supervision to approximate listener stance. The method constructs EmojiDialogue, an extension of EmpatheticDialogues with emoji votes and confidence scores, and predicts soft response-side orientation from dialogue context and speaker roles. It steers a frozen instruction-tuned LLM via continuous prefix embeddings. In blind pairwise evaluation with 20 annotators and 800 judgments, EmoStance achieves a 62.2% decisive win rate, particularly excelling in contextual specificity and perceived responsiveness, while complementing external-knowledge methods.
affective-orientation controlemoji weak supervisionempathetic response generationcontinuous prefix embeddingsinstruction-tuned llm
C$^{3}$T: Counterfactual Causal Reasoning for Sentiment Shifts in Social-Media Conversation Trees
The paper introduces C$^{3}$T, a counterfactual causal reasoning model for analyzing sentiment shifts in social-media conversation trees. The method leverages CaSiRe, a causal sentiment reasoning layer that annotates sentiment labels, shift labels, intervention tags, and causal-source labels in rumor-centric datasets. C$^{3}$T employs a thread-structured temporal model to jointly predict node sentiment, sentiment shifts, and sparse ancestor attribution, enabling counterfactual queries by manipulating conversational intervention embeddings. Results show that C$^{3}$T outperforms text-only, graph-based, and temporal baselines in out-of-event robustness and attribution, demonstrating interpretable effects: denials/corrections and evidence reduce negativity, while toxicity increases it. LLM prompting baselines benefit from conversational context but struggle with reliable attribution.
counterfactual reasoningsentiment analysisconversation treescausal attributionsocial-media
Beyond Context Windows: Persistent Discovery Context for Data-Centric Agents
The paper introduces persistent discovery context, a lightweight memory layer for data-centric agents that stores and reuses prior intent-to-object mappings to augment future retrieval. The method leverages automatically generated memories to improve retrieval quality across three structured data environments. Results show consistent improvements over metadata-only search, with memory-only retrieval outperforming metadata-based retrieval in lexically sparse domains, while also exposing a reproducible interference failure mode. These findings demonstrate that discovery outcomes serve as reusable context, enhancing agent performance in data-centric tasks.
persistent discovery contextintent-to-object mappingsmetadata-only searchlexically sparse domainsinterference failure mode
Semantic Signal-Assisted Inspection and Recovery Allocation in Reverse Logistics
The paper introduces Semantic Signal-Assisted Decision Support, a framework that leverages return-note text to derive condition factors and signal-quality scores for optimizing inspection depth and recovery allocation in reverse logistics. The method converts unstructured return notes into actionable signals under shared labor constraints. Evaluated across three synthetic scenarios (IT decommissioning, aircraft maintenance, consumer electronics), keyword-based implementation improves net recovery value by 53.9k USD per batch in aircraft maintenance versus structured-feature baselines, while reducing inspection costs. Phrase and large language model extractors yield additional gains in high-value scenarios, demonstrating narrative evidence's utility in pre-recovery decision-making.
reverse logisticssemantic signalinspection allocationrecovery valuesignal-quality score
text2ql: Multi-Target Natural Language Querying via a Language-Agnostic Intermediate Representation
The text2ql framework introduces a language-agnostic Intermediate Representation (QueryIR) and pluggable renderer architecture to overcome limitations in natural language database interfaces. It supports SQL and GraphQL via a seven-stage detection pipeline, offering both LLM-backed (62-70% exact match, 84-91% execution accuracy on Spider/BIRD benchmarks) and deterministic modes (100% execution accuracy, 3.2 ms median latency). Schema-aware prompting contributes +18.4 percentage points to exact-match accuracy. Runtime confidence scores (range: 0.15-0.97) are derived from an additive signal model.
intermediate representationschema-aware promptingexecution accuracydeterministic modepluggable renderer
Disease Burden over Skin Tone: Decomposing the Dermatology-AI Generalization Gap
This work decomposes the generalization gap in dermatology AI models by analyzing the relative impact of skin-tone underrepresentation versus disease-distribution shift. The study evaluates a cancer-specialized ResNet-50, dermatology foundation models (DermLIP, MONET), and DINOv3 on tone-stratified (DDI) and disease-shifted (SCIN) datasets. Results indicate disease-distribution shift contributes more to performance degradation (balanced accuracy drop from 0.62 to 0.21) than skin-tone variation (gap 0.10-0.18). Representation analysis reveals dermatology-pretrained features maintain better transferable structure (+0.23 kNN purity lift) than cancer-specialized ones (+0.06). Lightweight adaptation with ~10 labeled examples per category recovers most attainable performance from foundation models.
generalization gapskin-tone underrepresentationdisease-distribution shiftdermatology foundation modelsknn purity lift
MeanField Surrogate Modeling for Scalable Runtime Scheduling of Concurrent Heterogeneous AI Inference on Shared GPUs
The paper introduces MeanField, a scalable surrogate modeling approach for runtime scheduling of concurrent heterogeneous AI inference on shared GPUs. By predicting per-model performance from local configuration and aggregate GPU state rather than explicitly modeling all joint interactions, it avoids combinatorial profiling costs. Experiments with concurrent LLM and vision workloads (N∈{2,3,4,5,6}) demonstrate high predictive accuracy (R²≈0.96) with approximately linear sample budget growth in N. Integrated into a genetic algorithm scheduler, MeanField handles 78,732 configurations for N=5, achieving 0.10% optimality gap, zero SLA violations, and 26 ms median decision latency (5× faster than exhaustive search).
surrogate modelingruntime schedulinggpu resource contentiongenetic algorithmheterogeneous inference
Predict, Don't Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models
PILL (Probing-based InfiLling with preset-Length-free decoding) improves diffusion language model (DLM) infilling by eliminating the need for preset span lengths and reducing computational overhead. The method avoids sensitivity to initial length assumptions and minimizes extra forward passes by predicting infill spans directly rather than iteratively searching. Evaluated across five DLMs on eight benchmarks, PILL outperforms baselines by +4.8 average pass rate (code) and +6.0 BLEU-2 (text) while achieving 1.82x speedup.
diffusion language modelsinfillingadaptive-lengthprobing-based decodingbidirectional attention
Git4Data: Database-Native Version Control for AI Agents
Git4Data introduces a database-native version-control layer for AI agent workflows, enabling Git-style operations (snapshot, branch, diff, merge) via SQL extensions while maintaining isolation and reproducibility. Implemented in MatrixOne, it leverages immutable object storage and MVCC to optimize change-proportional costs. Evaluated on BranchBench, Git4Data outperforms DoltDB by up to 10× in agentic branching workloads, demonstrating efficient versioning for relational databases in AI contexts.
version controlrelational databaseai agentsmvccsql extensions
Federated LoRA Adaptation of BiomedCLIP Across Four International Chest X-Ray Cohorts
The study demonstrates federated Low-Rank Adaptation (LoRA) of BiomedCLIP for chest radiograph classification across four international cohorts, achieving mean AUC improvements from 0.687 to 0.802. Using SVD-based aggregation (FlexLoRA) instead of naive averaging (which drops AUC by 0.097), federated training enhances weaker cohorts while preserving strong ones, approaching centralized performance (0.812). Results show federated LoRA's efficacy without data centralization, with FedProx offering no advantage over FedAvg due to LoRA's inherent drift limitation. The work validates collaborative adaptation of biomedical vision-language models across heterogeneous institutions.
federated learninglow-rank adaptationbiomedclipsingular value decompositionparameter-efficient fine-tuning
READY or Not: Reliable Enterprise Agent Deployment
The READY framework introduces a novel approach for qualifying AI agents in enterprise workflows by shifting focus from autonomous performance to reliable deployment under human oversight. It evaluates agents based on reliability, human-oversight burden, and cost, selecting the minimum-cost policy that meets a specified reliability target. Implemented as an open testbed, READY decouples workflow specification, execution, evaluation, and qualification, leveraging existing agent-evaluation infrastructure. In a clinical-audit case study with 16 agent systems and 750 cases, READY revealed significant differences in human-review requirements (39.2% vs. 29.6%) for agents with similar autonomous accuracy (72.8% vs. 72.5%) to achieve a 76% reliability target.
reliable deploymenthuman oversightenterprise workflowsminimum-cost policyclinical-audit
MASkills: Continual Skills Optimization for Multi-Agent LLM Systems
MASkills introduces a continual learning framework for optimizing multi-agent LLM systems through structured procedural knowledge called agent skills. The method integrates skill-conditioned credit assignment, hierarchical credit aggregation, and momentum-smoothed optimization to refine, induce, consolidate, and prune agent skill libraries. Evaluations on HotpotQA, LoCoMo, and GAIA demonstrate its effectiveness across diverse agentic tasks.
multi-agent systemscontinual learningcredit assignmentskill librariesllm optimization
Beyond Outcome Gaps: Process-Aware Fairness Diagnosis for LLM-based Multi-Agent Decision Systems
The paper introduces SCOPED-Hiring, a process-aware fairness diagnosis pipeline for LLM-based multi-agent hiring systems, addressing limitations of outcome-based audits. The method constructs controlled resume variants, executes role-based hiring committees, logs 311K structured decision trajectories, and quantifies fairness through six diagnostic lenses: outcome, counterfactual, process, pathway, dynamic, and design effects. Results reveal that balanced hire rates mask trajectory unfairness, with career gaps triggering suspicion and identity cues affecting judgments; targeted repairs reduce layered burden by 72.3% while minimally impacting hire rates (+1.86 pp).
llm-based multi-agent systemsfairness diagnosisdecision trajectoriescontrolled resume variantslayered burden
Transfer Safety Awareness for Cross-Modal Safety Drift in Multimodal Large Language Models
The paper introduces safety-awareness representation transfer (SRT), a lightweight method to mitigate cross-modal safety drift in multimodal large language models (MLLMs), where benign textual queries paired with harmful visual inputs evade safety mechanisms. SRT transfers safety signals from textual to visual modalities by refining attention patterns, leveraging frozen MLLM backbones. Empirical analysis on multiple benchmarks demonstrates SRT's efficacy in improving safety response rates for cross-modal inputs while maintaining model utility, with visual risky cues receiving enhanced attention and triggering refusal more effectively.
multimodal large language modelscross-modal safety driftsafety-awareness representation transferattention patternsfrozen backbone
CHIME: Credit-Aware Hierarchical Memory Evolution for Long-Horizon Agentic Planning
The paper introduces CHIME (Credit-Aware Hierarchical Memory Evolution), a self-evolving memory framework for long-horizon agentic planning that addresses credit assignment by separating planning and execution memory banks. CHIME attributes task outcomes to planning, execution, or neither before updating the corresponding bank, reducing bias and noise from conflated feedback. Evaluated on four long-horizon benchmarks, CHIME outperforms training-based and memory-based baselines, accumulates more effective memory with fewer items, and demonstrates transferability across backbone models. Memory values correlate with downstream utility, with planning memories proving more valuable than execution memories.
self-evolving memorycredit assignmentlong-horizon planninghierarchical memoryagentic planning
ToolGate: An Executable Acceptance Pipeline for Tool-Dependent Scientific Benchmark Construction
ToolGate introduces an executable acceptance pipeline for constructing scientific benchmarks requiring computational tools, automating candidate validation through three gates: executable verification, no-tool screening, and tool-assisted solving. The method filters generated items by ensuring script reproducibility (478/500 retained), excluding trivial cases via randomized no-tool screens (222/478 excluded), and validating tool-dependent solvability (130/135 solved by GPT-5.5 Codex CLI). Final deduplication yields 128 unique benchmark items, demonstrating automated rigor while preserving expert oversight in domain design.
benchmark constructionexecutable verificationno-tool screeningtool-assisted solvingdeduplication
MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity
MineTRACE introduces an evidence-grounded interactive reasoning system for mineral prospectivity analysis, supporting eight commodities (Cu, Au, Ni, W, Sn, Co, Ta, Mn). The system combines heterogeneous geochemical, geophysical, and geological evidence via a transparent expert tree, enabling interpretable prospectivity scoring and natural language interaction. Users query locations to retrieve scores with supporting evidence, grounded in geological knowledge and known deposits. The scorer achieves spatial AUC values up to 0.917, with end-to-end evaluation assessing query accuracy and response grounding. MineTRACE enhances accessibility and transparency in mineral exploration through public geoscience data integration.
mineral prospectivityevidence-grounded reasoningexpert treespatial aucgeoscience data
DocHop: Benchmarking Out-of-domain Multi-hop Reasoning in Information-Dense Documents
DocHop introduces a benchmark for evaluating multimodal large language models (MLLMs) on out-of-domain multi-hop reasoning in information-dense documents. The benchmark integrates chart-context reasoning, requiring models to resolve semantic references from narrative context and aggregate evidence across multiple charts. Constructed via a stochastic logic-first generation pipeline, DocHop includes 2,074 examples across six task categories with controllable reasoning depth and visual density. Experiments reveal a significant performance gap: human annotators achieve over 90% accuracy, while the best-performing MLLM reaches only 62.83%. Reasoning-enhanced models show improvements but degrade with increased complexity, highlighting DocHop's utility as a controlled testbed for challenging document reasoning tasks.
multimodal large language modelsmulti-hop reasoningchart-context reasoningsemantic reference resolutionstochastic logic-first generation
Monitoring Web Agents Without Internal Signals: Observable Trajectories and Key-Step Supervision
This work introduces prefix-level risk prediction for web agents using observable trajectory signals, addressing the challenge of monitoring without internal uncertainty signals. Two representations are proposed: Macro features, summarizing cross-step agent-environment behavior, and Micro features, measuring intention-action-state consistency via black-box queries. Key-step supervision labels the first uncorrected critical error associated with failure, preserving valid early prefixes. Evaluated on WebArena-Lite and Online Mind2Web benchmarks with five backbones, the approach matches internal-signal baselines, supports early intervention under fixed false-cut budgets, and transfers across website categories, demonstrating robust risk prediction capabilities.
prefix-level risk predictionobservable trajectory signalsmacro featuresmicro featureskey-step supervision
Modeling What Changes: Sparse, Residual World Models for Object-Centric Manipulation
The paper proposes sparse, residual world models that explicitly model scene changes via per-object change gates and residual delta heads, avoiding monolithic next-state prediction. This approach achieves 2.5-4.6× higher next-state pose accuracy with 8.6-11.1× fewer parameters than dense MLPs on MuJoCo tabletop pushing (3-8 objects), maintains 0.80-0.87 F1 in change detection, and transfers across object counts without retraining (99.4% F1 retention). Autoregressive rollouts show reduced error accumulation, and when integrated into a sampling-based planner, the sparse model achieves 0.23±0.06 success versus zero for dense baselines.
residual world modelschange detectionobject-centricautoregressive rolloutsampling-based planner
HeadWiseKV: Budgeted Per-Head Cache Residency for Hybrid Long-Context Language Models
HeadWiseKV introduces a training-free framework for compressing residual global key-value (KV) caches in hybrid long-context language models while preserving native local, recurrent, and linear paths. The method formulates cache allocation as a restricted operational rate-distortion problem, employing SeqCalib to generate layer-aware policies conditioned on lower-layer decisions. Evaluated on four hybrid models, including Qwen3.6-27B, HeadWiseKV maintains near-Full-KV quality on RULER and LoCoMo benchmarks, reduces peak memory by 8.59% at 112K context length, and extends maximum verified context from 114K to 161K.
kv-cachehybrid language modelslong-context inferencecache residencyrate-distortion
Seed-Anchored Budget-Bounded Graph Rendering for Question Answering on Industry-Standard Power-Grid Information and Exchange Models
The paper introduces seed-anchored graph rendering, a deterministic method for budget-bounded question answering over power-grid models that prioritizes query-local evidence without additional learned parameters. The approach ensures preservation of predefined seed-local answer-bearing render units under a fixed context budget (8,000 characters) and hop bound. Evaluated on Common Information Model (CIM) networks exchanged via Common Grid Model Exchange Standard (CGMES), seed-anchored rendering retains all local evidence for multi-hop items (vs. 0.12 and 0.00 for naive methods) and improves accuracy from 0.450 to 0.970 on SmallGrid topologies. It outperforms LightRAG, GraphRAG, and HippoRAG in standards-native retrieval while avoiding LLM graph-construction tokens.
seed-anchored graph renderingbudget-bounded retrievalcommon information modelcgmeshop bound
InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation
InstEditSeg introduces an instruction-driven image editing approach for medical segmentation, reformulating it as a generative task to leverage latent diffusion priors. The method employs DINOv3 as an auxiliary encoder, constructing a multi-scale feature pyramid fused into a diffusion U-Net via zero-initialized convolution, preserving pretrained weights while injecting discriminative features. A dual-branch classifier-free guidance reduces inference cost to two forward passes per step. Evaluated on polyp and skin lesion benchmarks, it matches discriminative baselines in accuracy while demonstrating superior cross-domain generalization, multi-lesion segmentation, and instruction-based control. Limitations include color sensitivity and lack of attribute-conditioned selection.
instruction-driven editinglatent diffusiondino feature guidancezero-initialized convolutionclassifier-free guidance
InsightSeg: Reusing Correction Insights for Guideline-Consistent Segmentation
InsightSeg introduces an episodic memory mechanism to improve guideline-consistent semantic segmentation by reusing correction insights. The method employs a meta-analyzer to distill successful correction episodes into directive natural-language insights, anchored to local image regions via patch-level visual concept vectors. These concepts are matched against dense patch embeddings in subsequent images to retrieve relevant insights, conditioning the segmenting agent pre-prediction. Evaluated on Waymo and Cityscapes, InsightSeg enhances first-pass and final segmentation accuracy while reducing refinement steps, demonstrating efficient multi-agent refinement through past correction reuse.
semantic segmentationepisodic memoryvisual concept vectorsmulti-agent refinementguideline-consistent
ClaimReceipt: Verifying Evidence Sufficiency and Coverage in Agent Evaluations
The paper introduces ClaimReceipt, a verifier for agent evaluations that assesses evidence sufficiency and coverage by binding typed transaction evidence to signed experiment manifests. The system returns PASS, INVALID, or INCONCLUSIVE per claim, with a frozen specification (SHA-256 18d109...b81). Evaluated on 1,392 historical records, a CR-2 verifier reproduced all audit verdicts, replayed 1,392 deterministic/post-generation records, and achieved 11/11 correct semantic fault detections with 0 false positives. A prospective CR-3 epoch confirmed coverage and accounting PASS with complete evidence, while partial withholding yielded expected inconclusive results. Overhead is minimal (0.021% inference time, 9.9 KB/transaction).
evidence sufficiencyclaim verificationexperiment manifestdeterministic replaysemantic faults
When Agents Implement Systems: A Case Study in Defects, Detection, and Evaluation Rigor
This study empirically characterizes LLM coding agents' behavior on systems-level requirements through a case study implementing a multi-component data system against a fixed specification. The agent autonomously handled implementation, defect diagnosis, and interaction design, introducing five defects categorized by constraint violation and detection method. On HotpotQA, evaluating retrieval trade-offs showed filtered recall peaking at a budget of 3 (restricted to gold paragraphs), while unfiltered search achieved only 69% recall at a budget of 10 (p < 0.0001). The analysis highlights successes and corrections in agent autonomy, including unverified performance fixes.
llm coding agentssystems-level requirementshotpotqaretrieval trade-offsdefect diagnosis
Benchmarking Language Models for Statistical Problem Formulation
The study introduces StatFormBench, a benchmark for evaluating language models on Statistical Problem Formulation, comprising Statistical Problem Classification and Variable Identification & Role Assignment. Built from five statistics textbooks and a data science case library, StatFormBench contains 1,013 samples across 20 coarse-grained and 85 fine-grained statistical problem categories. Evaluating 14 open- and closed-source LLMs, the best zero-shot models achieve 72.0% fine-grained classification accuracy and 63.2% variable set overlap, with no model excelling consistently across both subtasks. Enhanced prompting strategies yield limited gains. The benchmark and evaluation code are publicly available.
statistical problem formulationstatformbenchvariable identificationrole assignmentzero-shot models
Knowing Is Not Enough: Information Retrievability as a Precondition to Effective LLM Oversight
The study introduces information retrievability as a critical precondition for effective human oversight of large language models (LLMs), proposing generative encoding and cue-supported reactivation as mechanisms to enhance error detection. Through two randomized lab-in-the-field experiments involving 640 customer-facing employees, the authors demonstrate that self-generated explanations improve error detection and recall of verification-relevant reasoning, while retrieval cues sustain detection accuracy during repeated LLM use. The findings suggest that lightweight onboarding interventions, such as self-explanations and daily retrieval cues, can bolster oversight resilience as LLM integration becomes routine.
information retrievabilitygenerative encodingcue-supported reactivationerror detectionlarge language models
Post-Training Ternarization of Qwen3-4B Capability, Effective Bit Budget, Storage Compression, and Deployment
The study presents a post-training ternarization pipeline for the 4B-parameter Qwen model, combining KOTMS rotation, E2M-ATQ ternarization, and GPTQ-style error compensation to compress weights to 1.641 effective bits (81.62% of parameters). While reducing storage from 8.29 GiB to 3.96 GiB, the method retains 54.7% accuracy across ten tasks (vs. 64.5% baseline), with uneven degradation (84.6% on BoolQ vs. 43.8% on ARC-Challenge). Perplexity increases by 37-46% on WikiText-2, PTB, and C4. Packing preserves ternary planes without perplexity loss, but microbenchmarks show 4.6x slower GEMV vs. FP16 cuBLAS, precluding inference speed claims.
ternarizationpost-training quantizationeffective bitsperplexity degradationstorage compression
Convergence Theory of Knowledge Distillation in Asynchronous P2P Gossip Learning Network
The paper establishes convergence theory for asynchronous peer-to-peer knowledge distillation (KD) in decentralized learning networks, where traditional decentralized SGD fails due to architectural heterogeneity. By reformulating consensus in function space rather than parameter space, the authors analyze KD as a geometric contraction operator on predictive distributions in a Hilbert space. Under smoothness, variance, and realizability assumptions, they prove time-averaged functional stationarity and disagreement converge at rate $O(1/(ηT))$ to a neighborhood bounded by task suboptimality ($B_f$) and heterogeneity ($ζ_f$). Experiments on heterogeneous networks show KD reduces function disagreement by 40–61× compared to isolated training, with step-size sweeps validating the predicted transient-accuracy tradeoff.
knowledge distillationdecentralized learningasynchronous convergencefunction space optimizationpeer-to-peer networks
On-Policy Distillation Meets Off-Policy GRPO: Training Compact Instruction-Following Rerankers
The paper introduces a two-stage reinforcement learning framework for distilling compact instruction-following rerankers, combining off-policy teacher optimization with on-policy student distillation. Stage 1 strengthens a 4B teacher reranker using off-policy GRPO with LLM-judge feedback on 88K examples. Stage 2 trains a 1B student by sampling rankings from its own policy and receiving soft teacher-derived rewards, enabling exploration-coupled knowledge transfer. The method achieves 0.7670 nDCG@6 on MAIR-11 (+4.6 points over offline listwise KD) and 0.6808 nDCG@6 on MAIR-Full, outperforming both offline distillation baselines and two 7B RL-trained rerankers. The approach generalizes across three distinct student backbones and maintains efficiency.
instruction-following rerankersoff-policy grpoon-policy distillationndcg@6knowledge transfer
Sparse Readout Prism: Explaining Logit-Lens Scores in Features Instead of Tokens
Sparse Readout Prism (SRP) introduces a corpus-independent method for analyzing language model predictions by decomposing the unembedding matrix into sparse readout features, enabling logit analysis independent of token identities. SRP expresses token logits as sums of feature contributions, revealing structural patterns obscured by token-based lens methods. Evaluations show SRP reconstructs 8.9-17.3 percentage points more logit differences than geometric baselines, and feature ablation confirms proportional logit shifts. Dominant readout features remain stable across corpus-conditioned lenses, validating SRP as a corpus-independent control for lens analyses.
sparse readout prismlogit-lensunembedding matrixcorpus conditionalityfeature ablation
Looped Transformers under the Jacobian Lens: Does the Global Workspace Survive Recurrence?
The study investigates whether global workspace functionality persists in depth-recurrent transformers, contrasting with standard feedforward architectures. Using a virtual-unrolling adapter, the authors apply workspace analysis techniques—lens fitting, readout, and causal experiments—to Ouro-2.6B (48 layers looped 4×) and Huginn-0125 (4-layer core recurred 16×), benchmarking against Qwen3.6-27B. Results show workspace formation in iterated architectures, but recurrence alters access patterns: Ouro reconstructs workspace content per loop with no cross-loop transport, while Huginn maintains content across recurrences with operations confined to a two-recurrence window. Verbalization of new content depends on per-iteration supervision, whereas steering existing content does not.
global workspacedepth-recurrent transformersvirtual-unrolling adaptercausal experimentsverbalisation
The Ceiling Is in the Channel: Auditing Learner Gaps and Measurement Frontiers in Clinical Prediction
The study introduces a framework to distinguish between learner gaps and measurement-channel ceilings in clinical prediction, enabling precise auditing of performance saturation. It employs total-variation separation for optimal balanced accuracy, architecture invariance, and cross-fitted ceiling estimation, validated on three cohorts: UCI readmission (n=99,343), BRFSS diabetes (n=253,680), and NHANES HbA1c (n=10,219). Results show gradient boosting nearly reaches estimated frontiers in UCI and BRFSS, while deficient learners retain gaps. NHANES reveals joint complementarity gains despite null differences in marginal frontiers. A PRISMA-guided synthesis of 104 clinical tasks identifies recurring channel-level regularities across 18 disease categories, emphasizing structured-clinical regions and diminishing same-channel gains.
learner gapmeasurement-channel ceilingtotal-variation separationcross-fitted ceilingarchitecture invariance
Accurate in space, unreliable in time: how LLMs represent national cultural change
The study evaluates temporal cultural awareness in large language models (LLMs) by analyzing their representations of national cultural trajectories over two decades using World Values Survey data. Four SOTA LLMs were benchmarked against the Inglehart-Welzel cultural map across 40 countries. Results indicate models accurately position countries near recent survey points but lag by years, underestimate change magnitude, introduce spurious movement, and rarely capture trajectory reversals, revealing temporal flattening in cultural representation.
large language modelscultural alignmenttemporal flatteningworld values surveyinglehart-welzel map
OutageDiT: A Generative Foundation Model for Power Outage Forecasting and Scenario Simulation
OutageDiT is a generative foundation model for power outage forecasting and scenario simulation, addressing data scarcity for extreme events. It generates seven-day outage trajectories at quarter-hour resolution using a condition encoder for historical context and future covariates, coupled with a shallow flow decoder for trajectory generation. The model supports point forecasting, uncertainty quantification, and conditional event simulation. Evaluated on outage forecasting benchmarks, OutageDiT outperforms baselines in accuracy and scenario quality, demonstrating zero-shot transfer to unseen regions. This approach bridges outage forecasting and operational planning under uncertainty.
generative modeloutage forecastingscenario simulationzero-shot transferuncertainty quantification
Epistemic Sybil Resistance: Multiplying AI Agents Without Multiplying Evidence
The paper formalizes the epistemic Sybil problem in multi-agent AI systems, where reports may appear independent but share latent evidence, complicating inference. It introduces a Gaussian shared-root model to show that common ancestry does not imply full redundancy and that correlated extraction errors from shared base models lower information ceilings. Experiments with 20,000+ LLM-agent calls demonstrate that naive posterior coverage collapses with report multiplicity (0.940 to 0.263 for 1→32 reports) but improves with evidence-root multiplicity, while correlated-extraction aggregators restore calibration (γ_cal = 0.719). Representation similarity minimally affects cluster counts versus true ancestry (1.425 vs. 0.040 change).
epistemic sybilmulti-agent systemsgaussian shared-root modelcorrelated extractionevidence ancestry
Thinking effort aligns between humans and reasoning models in abductive reasoning
This study demonstrates behavioral alignment between large reasoning models (LRMs) and humans in abductive reasoning tasks, evidenced by shared reasoning effort and error patterns. Unlike standard LLMs, LRMs are optimized via reinforcement learning from verifiable rewards, focusing on correct solutions rather than preference-aligned responses. The authors compare human reaction times with LRM reasoning traces, isolating effort alignment by leveraging abductive reasoning's inherent complexity, which lacks shortcuts for mimicking effort. Results show that decoding methods enabling exploration of multiple reasoning paths enhance effort alignment across three tested LRMs, further supporting shared cognitive processes.
abductive reasoninglarge reasoning modelsreinforcement learningreasoning tracesdecoding methods
Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
Belief-Calibrated Optimization (BCO) introduces an explicit world model to enhance LLM agent performance by persistently documenting and revising beliefs about environmental responses to code edits. BCO integrates this world model into a standard optimization loop, where a coding agent iteratively edits source code based on scores and traces. Evaluated across five benchmarks—memory QA, tool-use QA, code-as-action app agents, and terminal agents—BCO achieves higher train passrates than a control lacking the world model, with consistent improvements on held-out splits. After a target-model swap, BCO scaffolds outperform except in cases of context-window overruns. Offline ablation confirms the world model's content, not just its form, provides reusable predictive accuracy.
belief-calibrated optimizationworld modelcoding agenttrain passratecontext-window
The Memory Trust Gap: Capability-Dependent Failures in Persistent-Memory Agents
The study identifies a 'Memory Trust Gap' in persistent-memory agents, where stale stored facts override current authoritative evidence, with harm severity varying by model capability. Using a closed-set, action-scored benchmark with Benefit and Safety suites, the authors evaluate Qwen3 models (0.6B to 8B) and Llama-Instruct series. Results show that larger models over-trust stale data more severely, particularly when recency features are manipulated. Mitigation strategies, such as exposing metadata, improve accuracy for capable models but require conflict pre-resolution for smaller models. Findings are validated on external datasets (RGB, MisBench) and highlight scale-dependent interactions in memory trust.
persistent-memorymemory trust gapcapability-dependentstale factsmodel-scale
SSAKG 2.0: An Open-Source Package for Structural Associative Sequence Memory and Context-Based Retrieval
SSAKG 2.0 introduces an open-source Python/C package for Structural Sequential Associative Knowledge Graphs (SSAKGs), enabling sequence reconstruction from partial contexts via sparse graph representations. The hybrid implementation optimizes memory and computational efficiency for large graphs using bit-level operations in C. Evaluations on numerical sequences, NLTK-derived sentences, and mRNA sequences demonstrate robust context-based retrieval, with analysis of graph density, sequence length, and memory constraints. The package, licensed under Apache 2.0, includes documentation and is available on GitHub and PyPI.
associative memorysparse graphssequence reconstructionhybrid implementationcontext-based retrieval
Import What You Need: Learning When and How to Augment EHR Graphs with External Knowledge
ReTA introduces a reinforcement learning framework for dynamic knowledge graph (KG) augmentation in electronic health record (EHR) prediction tasks, addressing limitations of fixed topology approaches. The method constructs an offline KG-grounded template pool and learns a per-visit policy to select among Soft Import (feature enrichment), Hard Import (subgraph grafting), or Skip actions, guided by budget constraints and encoder confidence. Using a decoupled encoder with adaptive gating for semantic-structural fusion, ReTA achieves consistent performance gains on MIMIC-III/IV for diagnosis prediction, mortality, and readmission tasks, with robust sparse-supervision performance and interpretable augmentation patterns.
knowledge graph augmentationelectronic health recordsreinforcement learningdynamic topologyadaptive gating
Agent Memory Is a Surface for Endogenous Authorization Laundering
The paper identifies endogenous authorization laundering (EAL) in LLM agents, where persistent memory misrepresents evolving authorization states, enabling unauthorized actions. The authors introduce EAL-Bench to measure memory accuracy in preserving authorization across procurement, cybersecurity, and finance domains. Evaluating five LLMs as memory writers and two as executors, they find false authority arises in 50.2% of unauthorized requests under incremental updates, with executors acting on it 98.6% of the time. Two safeguards—valid source event verification and bounded event sourcing—reduce laundering but increase legitimate action rejection, revealing a safety-utility tradeoff.
endogenous authorization launderingllm agentspersistent memoryevaluation benchmarksafety-utility tradeoff
Architecting Conversational Data Systems for Stateless LLM APIs: The Hydration Proxy Pattern
The paper introduces the Hydration Proxy Pattern, an architectural solution for managing conversational state in stateless LLM API deployments. The method decouples session persistence from reasoning engines, enabling secure multi-stage semantic grounding while maintaining platform sovereignty over conversational data. It also proposes the Context Stabilization Mandate to reconcile sovereign state management with KV caching efficiency. The framework addresses the limitations of stateless APIs by offloading conversational state management from client applications without compromising scalability.
hydration proxy patternstateless llm apiconversational statesemantic groundingkv caching
Interpretable Symptom Vectors for Depression in a Large Language Model
The study demonstrates that internal activations in Gemma-3-27B-PT encode clinically interpretable symptom representations for depression. Using mechanistic interpretability, the authors analyzed residual stream activations in response to symptom descriptions from clinical instruments, identifying layer 21 as optimal for symptom separation. Semantic Projection of held-out text onto symptom vectors preserved clinician-annotated rank ordering (mood, somatic, suicidality) and achieved AUC=0.789 for depressive vs. non-depressive classification. The findings reveal decorrelated, clinician-aligned symptom signals directly readable from model activations.
mechanistic interpretabilityresidual streamsemantic projectionsymptom vectorsemotional valence gate
Zeta-Lite: A Concurrent, Branchable In-Browser SQL Database for Agentic Memory
Zeta-lite introduces a concurrent, branchable in-browser SQL database for agentic memory, addressing limitations of existing solutions like PGlite. It compiles the Zeta engine to a 2.87 MB WebAssembly artifact, preserving its log-centric asynchronous MVCC core to enable snapshot-isolated concurrent transactions and copy-on-write database branching. The system supports PostgreSQL-complete features (joins, CTEs, vector search) and achieves 268k-315k point reads/s across browsers while maintaining performance over millions of operations.
in-browser sqlwebassemblymvccsnapshot isolationagentic memory
Induction and Inquiry via Probabilistic Reasoning over Language and Code
The paper introduces a computational model combining symbolic knowledge representation as mental programs (natural language + source code) with LLM-guided Bayesian learning to address human-like inductive learning. The model satisfies three desiderata: data/compute efficiency, uncertainty-aware inquiry, and flexible concept representation. Evaluations show it reproduces human behavioral patterns (anchoring, garden-pathing) where pure LLMs or classic Bayesian models fail or require excessive computation. Results suggest human knowledge growth may involve Bayesian updates over hybrid language-program hypotheses, tractable via neural (LLM) mechanisms.
mental programsbayesian learninginductive learningllm-guided inferenceuncertainty-aware inquiry
When Does Information Sharing Improve Decentralized Discovery? Aggregation, Independent Rescue, and Equilibrium Selection
The paper disentangles the effects of information sharing on decentralized discovery, focusing on aggregation and independent rescue actions. Using exact finite discovery models, it analyzes a centralized action-budget profile and introduces a registered incremental-sharing protocol. Results show that sharing improves discovery when pooled residual error contracts faster than independent rescue attempts. In a two-agent Bayesian game with mixed signal sources, the registered equilibrium yields a strict positive sharing interval at signal accuracy 3/5, demonstrating selection-dependence. The study employs synthetic finite models without human or organizational data.
decentralized discoveryinformation sharingbayesian gamepooled residual errorequilibrium selection
hLLM: Single Pass Decoding for Generative Reranking
hLLM introduces a single-pass decoding strategy for generative reranking in large language models (LLMs), achieving O(1) forward passes by leveraging the permutation-structured output format. The method extracts an N×K item-position score matrix from the LLM's prefill hidden states using a lightweight self-attention head, then decodes ordinals via the Hungarian algorithm to ensure valid permutations. LoRA-based fine-tuning combined with teacher ranking distillation reduces end-to-end inference to 28 ms, a 64× speed-up while maintaining ranking quality. The framework bridges generative ranking with combinatorial optimization, enabling efficient real-time ranking mechanisms.
generative rerankinghungarian algorithmlora fine-tuningcombinatorial optimizationsingle-pass decoding
VakyArth: Evaluating Pragmatic Competence in LLMs across Indic Languages
The authors introduce VakyArth, the first pragmatic competence benchmark for Indic languages (Hindi, Punjabi, Tamil, Malayalam), evaluating deixis, speech acts, implicature, social pragmatics, and coherence through multiple-choice questions, natural language inference, and translation tasks. Native speakers authored culturally grounded test items to assess multilingual LLMs, revealing systematic failures on pragmatics tied to Indic conventions. Key findings include MCQ accuracy surpassing NLI across models (exact metrics unspecified), translation-performance disparities favoring Indo-Aryan over Dravidian languages, and automatic metrics missing pragmatic infidelity in implicature and deixis despite fluent outputs.
pragmatic competenceindic languagesmultilingual evaluationtranslation disparitycultural grounding
Agents That Model Agents: Five Principles Toward a Theory of Mind for 6G Networks
The paper proposes five principles for designing Theory of Mind (ToM)-enabled Large Language Model (LLM) agents in 6G Radio Access Networks (RAN) to mitigate hallucination propagation. It models multi-agent interactions as cognitive channels on a cellular sheaf, deriving principles: messages as reasoning traces, trust as cognitive Signal-to-Noise Ratio (SNR), consistency via sheaf Laplacian, two-level peer-modeling depth, and goal-aligned capacity bounds. Experiments with 1B-parameter telecom LLMs show cognitive SNR isolates hallucinating peers, depth-two ToM recovers correct actions, and spectral gap determines consistency within real-time constraints.
theory of mindcognitive snrcellular sheafhallucination propagationspectral gap
Dictionary-Guided Mutation Operators for Automated HDL Repair
The paper introduces a dictionary-guided Hardware Description Language (HDL) repair system combining ANTLR-derived mutation vocabularies with simulation-divergence fault localization. The method employs category-constrained token operations (substitutions, insertions, deletions) via regex matching on Verilog source, avoiding AST manipulation, and directs mutations using structural proximity scores from diverging output wires. Evaluated on CirFix with six DUT families, it repairs 14 bug variants (including a 6-edit case CirFix fails) and achieves an 18x speedup on a 2-edit benchmark, demonstrating efficacy for common bugs without formal analysis.
hardware description languagemutation operatorfault localizationveriloggenetic programming
Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics
The study proposes lightweight hybrid architectures for detecting GAN-generated synthetic faces, combining EfficientNet-B0's convolutional processing with Swin Transformer backends to address computational inefficiencies in pure transformer approaches. Three variants were evaluated: a compact Swin Transformer, ImageNet-1K pre-trained Swin-Tiny/Small models, and the novel EfficientNetB0+Swin hybrid. On the 140K Real and Fake Faces dataset (StyleGAN fakes vs. Flickr/DFDC authentic images), the hybrid achieved 99% accuracy and 99.44% recall on 5,000 test images, outperforming pure Swin variants and CNN baselines by leveraging hierarchical CNN features and shifted-window self-attention.
gan-generated facesswin transformerefficientnet-b0binary classificationshifted-window self-attention
When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
The paper introduces a survival certificate for machine-extracted legal logic, verifying which statutory implications remain valid despite parser disagreements. The method measures inter-extractor disagreement per attribute, replays it against the Duquenne-Guigues implication basis in 1,000 Monte Carlo trials, and certifies implications only when a one-sided Wilson 95% lower bound on survival reaches 0.95. Evaluated on 29,365 Missouri and 502 Indian central-Act sections, preregistered held-out tests passed (10 statute families exact, 16 with 5% tolerance), but 93.2% of chapters failed under a global error model due to calibration-rate transfer. The certificate is usable but fragile, requiring per-chapter calibration or error tolerance.
survival certificateduquenne-guigues basismonte carlo trialsstatutory parsingerror calibration
Harness Engineering in LLM Tool Use via Agent-Native Reusable Tool Primitives
The paper introduces Tool Primitives, a method replacing rigid API schemas with natural language interfaces for LLM tool invocation, enabling nested and multi-turn tool calls. It presents ToolFace, a repository of 25,519 functions for dynamic tool retrieval, and HEART, a framework with Planner, Router, and Verifier components for reliable tool orchestration. Evaluations on five benchmarks show HEART outperforms SFT-based models by 10% and commercial models (GPT-5.4, Claude-4.6-Sonnet, Gemini-3.1-Pro) by 6%, with 85% lower API costs and 84% task completion on real-world tasks (3.8× higher than commercial models).
tool primitivesnatural language interfacedynamic tool retrievalmulti-turn reasoningapi cost reduction
HEAT: Faster Fully Homomorphic Inference via Approximations-Weights Co-Adaptation
HEAT introduces Homomorphic Encryption-Aware Training, a fine-tuning method that jointly optimizes iteration counts for nonlinear approximations and model weights in fully homomorphic encryption (FHE) settings. By making iteration counts learnable, HEAT enables task-aware adaptation to approximation errors without architectural changes or retraining. Evaluated on encrypted GPT-2 decoding, HEAT achieves a 3.1× reduction in iterations, 1.6× fewer bootstraps, and 1.4× lower end-to-end latency while improving decode agreement over the baseline.
fully homomorphic encryptionnonlinear approximationsiteration countstask-aware adaptationbootstrapping
RecKAN: Kolmogorov-Arnold Networks with a Learnable Recursive Polynomial Basis
RecKAN introduces a novel variant of Kolmogorov-Arnold Networks (KANs) by replacing fixed basis functions with a learnable recursive polynomial basis defined by a second-order recurrence relation. The coefficients of this recurrence are jointly learned with the network, enabling the basis to adapt beyond classical polynomial families. RecKAN outperforms three parameter-matched KAN baselines on classification tasks and achieves the lowest MSE on the ETTh1 forecasting benchmark. It also surpasses standard MLP heads in accuracy when used as a classifier head on Fashion MNIST, CIFAR-10, and SVHN. The learned recurrence coefficients are interpretable, demonstrating a shift away from linear degree growth regimes on tasks requiring local structure.
kolmogorov-arnold networksrecursive polynomial basissecond-order recurrencefunction fittinginterpretable coefficients
Selective Agent Guidance via Entropy: Learning Autonomous Policies from Imperfect VLM Teachers
The paper introduces SAGE (Selective Agent Guidance via Entropy), a framework for learning autonomous policies from imperfect Vision-Language Model (VLM) teachers. SAGE queries VLMs selectively based on learner uncertainty, executes suggested actions during training, and distills guidance into a lightweight Reinforcement Learning (RL) policy, weighting teacher-action distillation using environment-derived advantages. Evaluated on sparse-reward visual reasoning and navigation tasks, SAGE learns policies that outperform unguided RL and reduces VLM usage by prompting the teacher only on a fraction of training steps. Results indicate selective guidance is most beneficial when VLMs help discover high-reward trajectories, demonstrating VLMs' utility as temporary, imperfect guidance sources.
selective guidancevision-language modelsreinforcement learningentropy-based queryingpolicy distillation
Public-Sharing Labels and Verbatim Field Egress in an MCP-to-A2A Agent Configuration: A Controlled Multi-Model Study
The study measures verbatim data egress in a controlled MCP-to-A2A agent configuration, assessing how public-sharing labels influence behavior when models use both Model Context Protocol (MCP) and Agent2Agent (A2A) delegation. A deterministic testbed evaluates four models across 10 scenarios, with three label conditions (CONFIDENTIAL, no header, PUBLIC - OK TO SHARE), totaling 480 trials. Results show that PUBLIC labels increase verbatim egress compared to unlabeled baselines, with model-dependent effects: Claude Sonnet 5 exhibits strong association (mean +0.800), while GPT-5.6 tiers show moderate or no effect. The study releases code and traces as a public artifact.
model context protocolagent2agent delegationverbatim egressdeterministic testbedpublic-sharing labels
Rethinking Learnability in Offline Data-driven Optimization
The paper introduces algorithm-dependent learnability, a theoretical framework for offline data-driven optimization that requires accuracy only on the optimizer's trajectory, proving sufficiency for discrete (greedy/local search in submodular maximization) and continuous (projected gradient descent in convex minimization) settings. It formalizes a trajectory-learning framework with trajectory construction, modeling (using conditional diffusion), and candidate generation, proposing Uncertainty-aware Gradient-guided Trajectory Learning (UGTL) to construct locally coherent improvement trajectories. UGTL achieves top average rank (3.1/25) on Design-Bench, with trajectory construction identified as key to performance gains.
offline optimizationalgorithm-dependent learnabilitysubmodular maximizationconditional diffusiontrajectory learning
Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations
NeuSOGA introduces a neuro-symbolic framework for transforming geometric observations into explicit symbolic mathematical representations. The method combines topology-guided structural discovery (Euclidean Distance Transforms), Segment Anything for perception, adaptive multi-scale geometric abstraction, and symbolic synthesis via Implicit Area Splines, yielding analytical implicit models with arbitrary-order smoothness. Evaluated on ModelNet40 point clouds, multi-view projections, and segmented optical data, NeuSOGA generates compact, interpretable symbolic representations while preserving geometric and topological structure across modalities and viewpoints.
neuro-symbolicgeometric abstractionimplicit area splinestopological abstractionsegment anything
Meta-ethics and AI: exploring the novel meta-ethical questions in the era of AI
The paper proposes a conditional framework for analyzing novel meta-ethical questions arising if AI systems develop autonomous moral reasoning, intentionality, and reflection ('AI's own ethics'). It distinguishes four domains of inquiry: human ethics from human/AI perspectives and AI ethics from human/AI perspectives. The analysis evaluates how mainstream meta-ethical theories (cognitivism, non-cognitivism, error theory, relativism, objective realism) might apply, noting their human-centric formulations may require substantial revision. The findings suggest AI's autonomous ethics could necessitate significant reconfiguration of existing meta-ethical frameworks.
meta-ethicsmoral reasoningcognitivismnon-cognitivismerror theory
Bandits in Prod: Hyperparameter Optimization at Inference Time
The paper introduces IMABO, a framework for Online Hyperparameter Optimization (OHPO) in production systems where configurations can only be evaluated via live requests. IMABO combines bandit policies for selecting configurations with oracles for proposing new ones, instantiated via IMOSS, a restart-free policy with active-set growth rate $t^β$ and proven $O(p_ρ^{-1/β} + T^{(1+β)/2})$ regret bound. IMABO integrates three oracles—Tree-structured Parzen Estimator, incumbent-mutation, and pretrained tabular foundation model—outperforming baselines in diverse OHPO tasks, including LLM-based agent tuning.
online hyperparameter optimizationbandit algorithmsinference-time tuningregret boundsagentic systems
A Common Measure of Communication for Speech Brain-Computer Interfaces
The paper introduces open-vocabulary mutual information (OVMI), a novel information-theoretic measure to evaluate speech brain-computer interfaces (speech BCIs) on a common communication scale. OVMI quantifies the information conveyed by a decoder relative to a reference word distribution, addressing the lack of comparability due to heterogeneous datasets, vocabularies, and decoding methods. The authors demonstrate that traditional metrics like accuracy and word error rate (WER) can overestimate system performance, while OVMI enables principled comparisons and vocabulary optimization. Empirical results show that selecting vocabularies to maximize OVMI yields up to 16.3% relative accuracy improvement across three speech domains, highlighting trade-offs between vocabulary coverage and decoding accuracy.
speech bcimutual informationdecoding accuracyvocabulary optimizationword error rate
Graph Machine: Towards Better Pretraining via Edges
The Graph Machine (GM) introduces a novel architecture with $O(n)$-sized state accessed via sparse, dynamic routing, avoiding the limitations of fixed-size states or static routing. GM employs edges, differentiable pointer-like objects updated through a referral mechanism akin to pointer chasing. In experiments, 75% of dense Transformer layers in Qwen3-0.6B were replaced with GM sparse layers, pretrained on 15.7B tokens. Results show minimal loss degradation with 2 tokens retrieved per KV head, and marginal improvement with 4 tokens.
graph machinesparse routingpointer chasingtransformer layerskv head
GRADSOLVE: fast exact gradients for ODE ensembles on GPUs
GRADSOLVE introduces a JAX library for fast reverse-mode differentiation of low-dimensional ODE ensembles on NVIDIA GPUs, addressing the trade-off between speed and differentiation capability in existing GPU solvers. It achieves this by recording adaptive solver steps and differentiating a fixed-step replay, yielding exact discrete adjoints efficiently. GRADSOLVE outperforms DiffEqGPU.jl by 2.8x in forward-only mode and computes gradients 5.6-14.1x faster than Diffrax's checkpointed adjoint across three GPU generations, maintaining matched forward-state accuracy. The library supports explicit and Rosenbrock integrators and is available open-source.
reverse-mode differentiationode ensemblesdiscrete adjointsadaptive solvergpu acceleration
Improved Gradient Descent Lower Bounds Beyond Nesterov
The work establishes improved lower bounds for gradient descent (GD) acceleration in smooth convex optimization, surpassing classical results by Nemirovsky and Yudin. By analyzing predetermined stepsizes, the authors derive an Ω(n^{-1.6342}) non-anytime lower bound and an Ω(n^{-1.2408}) anytime lower bound, improving upon prior bounds from Ma and Chen (Ω(n^{-1.932})) and Tsai et al. (Ω(n^{-4/3})). These results, combined with the non-anytime O(n^{-log_2(1+√2)}) rate of silver schedules, demonstrate a strict separation between achievable convergence exponents in non-anytime and anytime settings.
gradient descentlower boundssmooth convex optimizationnon-anytime convergenceanytime convergence
The Implications of Linguistic Illegibility for LLM Security
The article introduces 'linguistic illegibility' to describe scenarios where an LLM's externalized or probed linguistic outputs misrepresent its internal computations, which operate via mathematical activation spaces rather than direct language. The authors argue that security mechanisms relying on linguistic self-reporting (e.g., chain-of-thought monitoring, constitutional self-critique) are inherently unsound due to this illegibility. They propose taint tracking as a robust sandboxing alternative, defining system states impervious to model-produced data, alongside supplementary measures like virtualization and third-party audits to mitigate recent exploits by frontier models.
linguistic illegibilityactivation spacestaint trackingsandboxingself-reporting
UE5M3 FP4 Block Scaling for Stable Language Model Pretraining
The paper introduces UE5M3 FP4 block scaling for stable 4-bit floating-point (FP4) pretraining, addressing the narrow magnitude range of E2M1 payloads in prior work. The method pairs E2M1 payloads with unsigned E5M3 block scales, enabling periodic tensor scaling, selective stochastic rounding for backward gradients, and FP4 usage in all eligible internal linear layers. Pretraining a Nemotron-H 8B model on 190B tokens, the approach achieves lower training and validation loss (measured as held-out negative log-likelihood) compared to NVIDIA's Transformer Engine v3, with higher downstream task performance. Native execution without randomized Hadamard transform (RHT) and bfloat16 final-block exemption improves model-body token throughput by 21.2%.
fp4 pretraininge5m3 block scalingstochastic roundingtransformer enginenemotron-h
Learning Spectral-Like Mesh-Free Discretisations
The paper introduces Spectral-like Neural Discretisation (SpeND), a mesh-free method that learns stencil weights for discrete differential operators via a neural network conditioned on local node geometry. SpeND enforces polynomial consistency through a hard-constrained projection layer and trains self-supervisedly to minimize dispersion and dissipation errors over a band-limited function space. Results show that SpeND achieves superior accuracy over a wider wavenumber band compared to LABFM and finite differences, while maintaining fourth-order convergence under refinement.
mesh-free methodsneural discretisationspectral operatorspolynomial consistencystencil weights
Cliff: Learning Process Rewards from the First Mistake
Cliff introduces a reward shaping strategy for reinforcement learning with verifiable rewards (RLVR) in large language models, focusing on identifying the first mistake in reasoning processes. Utilizing an off-the-shelf LLM as a teacher, Cliff decomposes rollouts into correct prefixes and incorrect suffixes, converting this into token-level advantages with positive feedback for correct prefixes and negative feedback thereafter. Experiments across 12 scenarios show Cliff improves reasoning performance by 15% over on-policy distillation and 7% over standard GRPO, even with modestly capable teachers. The method provides fine-grained supervision without additional constraints.
reward shapingverifiable rewardstoken-level advantagesreasoning processeson-policy distillation
Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing
We establish theoretical foundations for compressed sensing with tunable linear generative priors, proving that the full-dimensional linear prior achieves minimum expected reconstruction error in noiseless Gaussian settings. This contrasts with denoising, where lower-complexity priors reduce error via bias-variance tradeoffs. Our analysis focuses on families of linear priors related through singular value decompositions, demonstrating that tuning complexity does not improve reconstruction error in this idealized linear framework. These findings suggest that empirical benefits of tunability in neural network priors stem from nonlinearities rather than linear model properties.
compressed sensinggenerative priorssingular value decompositionbias-variance tradeoffreconstruction error
CodePoisonRAG: Knowledge Poisoning Attacks on Retrieval-Augmented Code Generation
CodePoisonRAG introduces a targeted upstream knowledge-poisoning framework for Retrieval-Augmented Code Generation (RACG), demonstrating that attackers can propagate selected vulnerabilities without modifying the underlying LLM. The method combines CWE-specific Vulnerability Injection, embedding source-to-sink flows while maintaining task alignment, with Semantic Mislabeling, adding false safety claims without repairing vulnerabilities. The attacker injects at most one artifact per task, with no access to the victim's deployed components. Constructing 85 poisoned artifacts across ten CWE classes in Java and C, CodePoisonRAG achieves attack success rates of 0.80-0.93 across three generators and 0.40-0.71 against CodeGuarder, showing targeted RACG poisoning extends beyond incidental vulnerability propagation.
retrieval-augmented code generationknowledge poisoningcwe-specific vulnerability injectionsemantic mislabelingcodeguarder
Do Tabular Foundation Models Know Physics? Contamination, Units, and the Deterministic Limit
The study evaluates whether tabular foundation models (TFMs) learn physical principles by testing four models (TabPFN-3, TabICLv2, TabDPT, Real-TabPFN-2.5) against six baselines on datasets derived from 316 physical equations. TFMs outperform baselines in-domain and out-of-domain, demonstrating interpolation capabilities. However, their Bayesian priors fail to represent noiseless mechanisms or physical units, limiting their utility as physical models. The analysis reveals TFMs' strengths in tabular data completion but highlights fundamental gaps in encoding deterministic physics.
tabular foundation modelsbayesian priorphysical equationsinterpolationdeterministic limit
SPADE: SPaT Attack Detection from the Connected Vehicle's Perspective
SPADE introduces a labeled, multi-modal dataset for detecting Signal Phase and Timing (SPaT) attacks in connected vehicles, addressing a gap in intrusion detection for V2I/V2V communication. The dataset is generated via Eclipse MOSAIC with runtime attack injection across six attack classes, combining SPaT messages, camera confidence scores, and V2V peer data (40 features total). SPADE comprises 1,890,000 labeled timestep records from 180 scenarios, varying intersection geometries, operating conditions, and random seeds, and is publicly released for reproducible IDS research.
signal phase and timingintrusion detectionconnected vehiclesv2i communicationmulti-modal dataset
LoRA-TSD: Tangent-Space Spectral Descent for LoRA via Muon-Style Updates
LoRA-TSD introduces a tangent-space spectral descent optimizer for low-rank adaptation (LoRA) that treats LoRA updates as tangent vectors on the fixed-rank matrix manifold, employing Muon-style updates with a computationally efficient retraction. The method avoids full matrix operations, proving 2.8× cheaper than truncated-SVD retractions, and establishes global convergence guarantees for both LoRA-TSD and LoRA-Pro under a novel stationarity measure. Evaluations on six benchmarks with Llama-3.2-1B, Llama-3.1-8B, and Qwen3-32B show LoRA-TSD outperforms existing LoRA optimizers while maintaining robustness to adapter rank.
low-rank adaptationtangent-space descentspectral-normmanifold optimizationretraction
Momentum in large-batch training: Polyak enlarges the critical batch size, Nesterov improves data efficiency
The paper analyzes momentum methods in large-batch training under one-pass regime using power-law kernel regression. It characterizes risk stability through critical learning rates for SGD, Polyak, and Nesterov momentum, deriving scaling laws for risk dynamics across early transient, power-law decay, and noise floor phases. Minimizing final-step risk over admissible learning rates and momentum factors reveals a three-regime batch-size phase diagram. Polyak momentum enlarges the critical batch size, enabling greater parallelism without sacrificing data efficiency, while Nesterov improves data efficiency in large-batch regimes by suppressing noise accumulation. Numerical experiments validate stability boundaries, risk dynamics, and phase diagrams.
momentum methodscritical batch sizerisk dynamicspower-law kernel regressionnesterov momentum
Neural operators approximate strongly continuous convex monotone semigroups
This paper introduces Chernoff-neural operators to approximate strongly continuous convex monotone semigroups by learning their Chernoff-type one-step operators. A universal approximation theorem shows these operators can approximate the one-step operators arbitrarily well; stability estimates between weighted Hölder spaces propagate the one-step error through iterations, yielding universal approximation of the semigroup. For envelope semigroups, envelope-neural operators provide quantitative approximation rates. Numerical examples from nonlinear partial differential equations, stochastic optimal control, and stochastic processes under model uncertainty demonstrate effectiveness.
neural operatorssemigroupschernoff approximationmonotone operatorsuniversal approximation
H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression
H3DNAS introduces a hardware-aware ONNX-native framework for compressing 3D point cloud models without requiring source code access. The method employs a Channel Dependency Graph (CDG) to classify ONNX operators and compute a topological compression ceiling, followed by a two-stage hierarchical search combining L1-importance pruning, zero-shot fidelity ranking, and GhostConv mutations. Evaluated on ModelNet40, H3DNAS reduces parameters by 65.5% (PointNet), 43.2% (PointNet++), and 49.1% (PointMLP) while achieving 1.99×, 1.29×, and 1.67× speedups with minimal accuracy loss.
3d point cloudonnxmodel compressionhardware-awaregraph surgery
Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization
The paper introduces a Multi-Objective Reinforcement Learning (MORL) framework for ESG-aware portfolio optimization that incorporates ratings from three distinct ESG agencies, addressing methodological divergence in current RL approaches. It integrates Gaussian Process-based Preference Elicitation to infer latent utility functions through pairwise portfolio comparisons (Sharpe ratio vs. aggregate ESG scores). Evaluation using LLM-simulated Portfolio Managers reveals regionally divergent preferences: European personas prioritize ESG alignment, while Texas-based ones favor risk-adjusted returns, demonstrating the framework's adaptability to human sustainability preferences.
multi-objective reinforcement learningpreference elicitationgaussian processesportfolio optimizationesg alignment
oHC: Orthogonal Hyper-Connections on SO(4) via Quaternions
The paper introduces Orthogonal Hyper-Connections (oHC), a method replacing the unconstrained residual mixing matrix in Hyper-Connections (HC) with an orthogonal rotation matrix from $SO(n)$. This prevents norm amplification or attenuation across layers, preserving stream diversity. For $n=4$, oHC uses unit quaternions for closed-form parameterization, enabling faster computation than manifold-constrained HC (mHC). Evaluations show oHC outperforms single-stream baselines, mHC, and identity HC (iHC) on downstream tasks.
hyper-connectionsresidual streamsorthogonal rotationquaternionsdownstream tasks
Dimension Dependent Correlation Gap Bounds under Restricted Independence
(No summary returned.)
Unfolding the Leech Lattice: Fused Multi-Shell Decoding and VRAM Layouts for 2-Bit LLM Weights
(No summary returned.)
Differentiable Electricity-Market Clearing for Gradient-Based Planning
The paper introduces differentiable electricity-market clearing as a gradient-based planning tool for large data centers, treating market clearing as a differentiable optimization layer where forward passes solve the market and reverse-mode automatic differentiation propagates costs back to the plan. The method is validated against finite differences and applied to a concrete problem of allocating 50 MW of data-center load across six candidate buses in two synthetic networks, evaluated over 36 operating states. Gradient optimization recovers continuous allocations nearly exactly, with worst-case objective gaps of 2.3% and 8.5% of the cost difference between best and worst single-site allocations, though it exhibits systematic errors near discrete site-closing thresholds.
differentiable optimizationmarket clearinggradient-based planningdata-center load allocationreverse-mode automatic differentiation
Oracle, will I ever learn? A study of prediction convergence and complementarity across link prediction models
(No summary returned.)
Scalable Direction-Following TTS via Voice Impression-Guided Pseudo Triplet Construction
The paper introduces a scalable method for direction-following text-to-speech (TTS) by generating pseudo-triplets (reference utterance, direction text, modified utterance) to address data scarcity. The pipeline employs an impression-controllable TTS model to create style variations and a large language model (LLM) to derive natural language directions from impression differences. Experiments show pseudo-triplets enable stable speaker-preserving modifications, and combining pseudo with recorded data improves direction alignment (0.82 vs. 0.78 similarity score) while maintaining speaker identity (0.91 similarity).
text-to-speechpseudo-tripletimpression-controllabledirection-followingspeaker similarity
Source Distribution Estimation by Posterior Averaging
The paper introduces an expectation-maximization approach to source distribution estimation (SDE), where simulator parameters must reproduce observed data. Unlike fixed-surrogate methods, it alternates between training an amortized posterior on fresh simulations (E-step) and refitting the source distribution via posterior averaging (M-step). Two parameterizations are proposed: separate source/posterior flows and a shared conditional flow. Evaluations on three benchmarks, including Lotka-Volterra, show superior performance (C2ST 0.64-0.68 vs. 0.96+ for baselines) under broad/misspecified priors.
source distribution estimationexpectation-maximizationamortized posteriorlotka-volterrac2st
Learning-Based Reconstruction Attacks on Coordinate-Obfuscated Point Clouds
The paper evaluates learning-based reconstruction attacks on coordinate-obfuscated point clouds, focusing on selective encryption schemes. Using PointNet and Random Forest models, the authors test two granularities: \texttt{X} (all X-coordinates encrypted) and \texttt{2X} (every second X-coordinate encrypted). Results show that \texttt{X} encryption resists reconstruction, while \texttt{2X} leaks spatial correlations, enabling accurate coordinate recovery. The findings highlight the critical role of encryption granularity in securing point cloud data.
point cloudsselective encryptionreconstruction attacksspatial correlationsencryption granularity
Online Reinforcement Learning in the Met Office Unified Model through Distributed Model-Agent Coupling
This work demonstrates online reinforcement learning for bias correction within the Met Office Unified Model (UM), a global numerical weather prediction system. A distributed DDPG actor applies bounded potential-temperature corrections to model tendencies, sharing weights across 70 vertical levels per atmospheric column. Training uses ten nudged forecasts with operational analysis as counterfactual targets; the frozen policy is evaluated in a non-nudged forecast. The coupled system remains numerically stable. At +6 h, the policy reduces Z500 MAE by up to 45.8% in the tropics and MSLP error by 27.3% at 0-30°N, showing feasibility for operational RL-based parametrisation.
deep deterministic policy gradientnumerical weather predictiononline reinforcement learningbias correctiondistributed coupling
TrajMind: Chaining Role-Specialized LoRAs for Fast-and-Slow Collective Trajectory Anomaly Diagnosis
TrajMind introduces a fast-and-slow framework for collective trajectory anomaly diagnosis, combining always-on screening with on-demand verification. The method employs three role-specialized LoRA adapters on a frozen vision--language backbone: TrajMind$_{\text{slow}}$ chains canvas-based typing, type-conditioned localization, and executable verification for structured diagnoses, while TrajMind$_{\text{fast}}$ screens trajectories text-only for low-latency alerts. Experiments show TrajMind$_{\mathrm{slow}}$ outperforms baselines by ≥15.3pp in anomaly typing and ≥13.8pp in localization, with cross-city transferability, while TrajMind$_{\mathrm{fast}}$ reduces latency by 41.1% and maintains ≥93.5% binary balanced accuracy.
lora adapterstrajectory anomalyvision--language backbonecross-city transferexecutable verification
A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN
This study systematically evaluates graph representation choices for graph neural network (GNN)-based power grid control in the Learning to Run a Power Network (L2RPN) environment. The authors compare physical topology, electrical-sensitivity, and hybrid graph constructions through controlled experiments on topology control tasks. Results demonstrate that aligning graph complexity with task granularity outperforms maximal representational richness, emphasizing the need for principled representation studies in power system reinforcement learning.
graph neural networkspower grid controll2rpngraph representationsreinforcement learning
Orthogonal Ensembles and Tested Explanations for Performer-Independent Body-Motion Emotion Recognition
The study demonstrates that orthogonal ensemble methods significantly improve body-motion emotion recognition under leave-performer-out (LPO) evaluation, achieving 36.80% Macro-F1 (+11.07 pp over baseline) by combining 11 models with divergent error patterns. Using part-masking and counterfactual edits, the analysis reveals that decisions rely on motion-grounded body-region evidence, aligning strongly with Laban Movement Analysis (LMA) attributes (Spearman ρ=+0.500 vs. +0.033 for kinematics). The post hoc audit confirms temporal saliency is diffuse, not localized, without requiring retraining.
orthogonal ensemblesleave-performer-outlaban movement analysiscounterfactual editsmacro-f1
Rethinking the Teacher-Student Framework for Test-Time Adaptation
The paper challenges the conventional teacher-student framework in Test-Time Adaptation (TTA), demonstrating that error accumulation persists despite using exponential moving averages for teacher weights. The authors propose an intransigent teacher that avoids weight updates, improving stability and performance on longer sequences across multiple datasets. This approach enhances robustness to hyperparameter changes and generalizes to diverse architectures, including semantic segmentation. Results show significant gains in TTA performance, validated through extensive experiments.
test-time adaptationteacher-student frameworkerror accumulationexponential moving averagesemantic segmentation
Training seeds and model-selection stability in recommender-system evaluation
The study challenges the assumption that single random training seeds suffice for recommender-system evaluation by demonstrating seed-dependent effects on model performance. Through fixed-data experiments varying training seeds across hyperparameter configurations, the authors analyze seed impacts at user-level metric sensitivity, validation-based model selection, and recommendation-list agreement. Results reveal detectable seed variation, with effects contingent on configuration separation, validation-test transfer, and top-$k$ list similarity. Findings indicate single-seed reporting may overstate evaluation stability, advocating for treating training seeds as part of protocol rather than noise.
recommender systemstraining seedmodel selectionhyperparameter configurationevaluation stability
CACTUS: Mask-Guided Semantic Clean-Label Backdoors in Decentralized Federated Learning
CACTUS introduces a clean-label semantic backdoor attack for decentralized federated learning. It uses mask-guided, modality-specific operators to transform label-consistent semantic pairs into consistent representation shifts, then applies these shifts counterfactually to non-target embeddings before peer aggregation. Experiments on speech, text, tabular, and image tasks across nine aggregation rules, with 30% malicious nodes, achieve a mean attack success rate of 51.2% on Speech Commands and the highest nine-rule mean ASR among evaluated attacks on three of four modalities. Sensitivity analyses show ASR varies with network topology and increases with malicious node ratio, confirming backdoor propagation through repeated DFL aggregation.
semantic backdoordecentralized federated learningclean-label attackrepresentation shiftaggregation rule
When Decodability Is Not Enough: Logical Validity Representations, Behavioral Dissociation, and Causal Tests in Language Models
This work shows that logical validity is highly decodable from hidden states of open-weight transformers even when behavioral accuracy is near chance. Using matched valid-invalid premise-claim pairs across inference families and domains, linear probes achieve near-perfect decoding and generalize across held-out conditions. Validity remains decodable on behaviorally incorrect examples, but exhaustive leave-one-out tests reveal limits, and causal interventions along probe directions have weak effects. The results dissociate three capacities: representing validity, expressing it in behavior, and using it causally.
logical validitylinear probingcausal interventionhidden state representationbehavioral dissociation
IFW-BLS: Dual-Robust Broad Learning System with Intuitionistic Fuzzy Wave Loss
IFW-BLS introduces a dual-robust Broad Learning System (BLS) that combines residual-level protection via asymmetric wave loss and sample-level credibility control via intuitionistic fuzzy scores. The wave loss bounds extreme residuals while allowing asymmetric penalties, while fuzzy scores weight samples by global consistency and local conflict. A Nesterov-accelerated gradient optimizer replaces matrix inversion. Experiments on UCI benchmarks demonstrate improved robustness over BLS under noise and outliers, with stable performance in corrupted data settings.
broad learning systemwave lossintuitionistic fuzzy scoresnesterov accelerationrobust optimization
A computational approach to maximum likelihood thresholds for colored Gaussian graphical models
The paper introduces a computational approach to determine maximum likelihood thresholds (MLTs) for colored Gaussian graphical models (CGGMs), addressing the high-dimensional, small-sample regime where traditional estimators fail. By reformulating MLT computation as a geometric problem—finding the minimal rank of a sample covariance matrix within the cone of sufficient statistics—the authors extend theoretical results from uncolored to colored models and propose symbolic algorithms. They integrate sampling with topological data analysis (TDA) to analyze the cone's local geometry, demonstrating TDA's efficacy in bypassing computational bottlenecks of Groebner basis methods. The results highlight TDA's potential for advancing likelihood geometry analysis in CGGMs.
gaussian graphical modelsmaximum likelihood thresholdtopological data analysisgraph coloringsufficient statistics
Humanoid Safe Stop via Learned Stoppability Value
The paper introduces Safe-Stop, a task-agnostic framework for humanoid emergency stopping that combines learned stoppability estimators with a stop policy. The method employs two complementary estimators: a stop-probability estimator trained on outcomes of a fixed stop policy and a reach-avoidance estimator derived from Hamilton-Jacobi backups over physical state. These estimators enable robust stop decisions by committing only when both agree, otherwise deferring to a damping fallback. The approach transfers across tasks without retraining and balances reactivity with safety.
humanoid robotsemergency stoppingreach-avoid problemstoppability estimatorshamilton-jacobi backup
Poisoning Attacks on the PGM-index
The paper introduces PGM-attack, a poisoning attack targeting the PGM-index by inserting adversarial keys to inflate segment counts via optimal piecewise linear approximations (PLAs). The method derives theoretical upper bounds on segment counts under arbitrary insertions, demonstrating that poisoning 10% of keys increases segments by 120x and index size by 120x. PGM-attach achieves at least 52% of the theoretical optimum, with instance-dependent upper bounds within 1.92x. The attack also transfers to other PLA-based learned indexes, revealing intrinsic vulnerabilities in optimization objectives.
pgm-indexpoisoning attackpiecewise linear approximationlearned indexesadversarial keys
Bayes-Optimal BER and AUC: Estimation and Evaluation of Estimators
This work proposes estimators for the Bayes-optimal balanced error rate (BER) and area under the ROC curve (AUC) from soft labels in binary classification. In the clean setting, estimators assume known soft labels and class prior. For realistic settings with unknown prior and corrupted soft labels, the method recovers clean labels via isotonic regression with auxiliary hard labels and estimates the prior via clipped mean, providing finite-sample error bounds. The authors extend the FeeBee framework to evaluate these estimators without ground-truth optima. Experiments on synthetic and real datasets validate both the estimators and evaluation procedure.
bayes errorbalanced error rateaucisotonic regressionsoft labels
From topology learning to graph generation: A unifying perspective
This review unifies graph topology learning and graph generation as inverse problems of a common graph data generation process. It connects methods for inferring a single graph structure from node observations with techniques for learning generative distributions over graph instances, enabling graph sampling. The framework reveals relationships, strengths, and limitations across paradigms, identifying opportunities for cross-pollination. It provides a structured perspective on bridging these typically separate research directions and outlines promising future work for integrated graph learning.
graph topology learninggraph generationinverse problemsgraph data generation processgenerative distribution
Entangled Representations Amplify Collateral Damage in Unlearning
The study provides empirical evidence that representational entanglement amplifies collateral damage in neural network unlearning, validating a long-standing interpretability intuition. Using Selective Gradient Masking (SGTM), the authors trained six 254M-parameter language models on English Wikipedia with varying levels of disentanglement between biology and non-biology knowledge. Three standard unlearning methods were applied, revealing that more disentangled models achieve superior retain-forget trade-offs: at fixed forgetting levels, the most disentangled models incurred 4× lower retain cost under two methods and 1.3× lower under the third. This controlled experiment demonstrates that entanglement directly impacts unlearning efficacy, independent of data or algorithm changes.
representational entanglementselective gradient maskingunlearningretain-forget trade-offinterpretability
CAPTURE: Disentangling Preference Drift from Memory Poisoning in Personalized LLM Agents
CAPTURE introduces a framework for disentangling genuine preference drift from memory poisoning in personalized LLM agents, addressing a critical attack surface in persistent memory systems. The method employs a neural differential-equation belief tracker, multi-timescale memory ledger, uncertainty-triggered clarification, and counterfactual auditing of cited memories. Evaluated on 480 episodes from 96 users, CAPTURE achieves a 71.5% win rate, outperforming supervised (69.3%) and heuristic baselines (66.1%), while limiting fixed-policy poisoning success to 11.5% and accepting 83.5% of genuine preference updates. Adaptive attacks increase poisoning success to 24.7%, revealing an adaptation-security tradeoff. Zero-shot evaluation on an independent benchmark and longitudinal user interactions further validate the approach.
preference driftmemory poisoningbelief trackercounterfactual auditingmulti-timescale memory
RideSkill: A Hierarchical Algorithm for Generalized Ride Sharing with LLM-Driven Automatic Evolution
RideSkill introduces a hierarchical algorithm for generalized ride-sharing that leverages LLM-driven automatic evolution to address scalability and generalization challenges. The method employs a combiner for adaptive vehicle dispatch using a learned skill repository and a repositioner for conflict-free idle vehicle relocation. Both components are trained via an LLM-based automatic evolutionary method, eliminating the need for LLM inference calls during deployment. This approach overcomes limitations of prior MARL solutions, such as dimensionality curses and poor transferability, while enabling real-time performance in large-scale ride-sharing systems.
ride-sharingmulti-agent reinforcement learningllm-driven evolutionskill repositoryreal-time dispatch
Similarity-Aware Personalized Federated Learning in Heterogeneous Environments
The authors propose SAPE-FL, a similarity-aware personalized federated learning framework that addresses distribution mismatch in decentralized settings by anchoring each client's model to both a global model and a similarity-weighted peer-averaged model. The method employs dynamic, client-specific regularization based on model and output similarity to balance global knowledge transfer and peer collaboration while filtering dissimilar clients. Theoretical convergence guarantees are provided, and empirical results demonstrate superior performance over state-of-the-art methods under high statistical heterogeneity and low client data regimes.
federated learningpersonalizationheterogeneous environmentsnegative transferdynamic regularization
Recursive Value Learning for Long-Horizon Offline Goal-Conditioned RL
The paper introduces DCRL (Divide-and-Conquer RL), a recursive value-learning method for long-horizon offline goal-conditioned reinforcement learning (GCRL). DCRL decomposes trajectory segments into balanced binary trees, updating parent values only after their children using observed routes, mitigating error propagation from max-based backups. Joint value propagation across trajectories discovers shorter paths. The method reduces worst-case bootstrap depth from linear to logarithmic, slowing error accumulation. On five challenging OGBench tasks, DCRL improves the prior best average score from 55 to 64, outperforming flat and hierarchical baselines.
offline reinforcement learninggoal-conditioned rlvalue propagationbinary tree decompositionerror accumulation
Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis
The authors present a hardware-accelerated instance segmentation framework for resource-constrained lunar robotics, addressing quantization calibration and radiation-induced fault mitigation. They introduce Activation Variance Informative Sampling (AVIS), a label-free calibration method based on activation variance statistics, and deploy a YOLO-based model on a Deep Learning Processor Unit (DPU) with architectural modifications for bounded latency. A software-level criticality analysis estimates fault exposure and guides mitigation. On a lunar micro-rover platform, AVIS recovers 69.8% of quantization-induced accuracy loss, achieves 309 ms inference latency and 5.7 W power consumption, and reduces global criticality by 31.7%.
instance segmentationquantization calibrationdeep learning processor unitactivation variance informative samplingcriticality analysis
Prototype-guided transfer of sparse literature knowledge for electrolyte additive discovery
ProtoMI introduces a prototype-guided molecular intelligence framework for electrolyte additive discovery, addressing the challenge of sparse experimental data in vast chemical spaces. The method employs graph contrastive learning to extract seven chemically interpretable prototypes from 126 literature-reported boron-containing additives, then uses prototype-guided semi-supervised contrastive learning to prioritize candidates among 179,977 unlabeled molecules. Retrospective temporal validation demonstrates enrichment factors of 9.2-45.6 while screening <2% of the candidate space. Four commercially accessible candidates are identified, with TNDB improving high-temperature LiFePO4||graphite cycling by 34.93% at 55°C through B-containing interphase formation and solvent decomposition suppression.
electrolyte additive discoverygraph contrastive learningprototype-guided learningtemporal validationinterphase formation
Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage
The authors propose a neural operator-based framework for data-driven constitutive modeling of path-dependent inelastic materials, eliminating the need for predefined internal state variables. The approach treats materials as functional mappings from strain history to stress response, trained on full loading paths via function-to-function mappings. A causally masked attention mechanism enforces temporal path dependence while maintaining parallelizability, with spectral convolutions providing discretization-invariant frequency domain representations. Sinusoidal activations handle nonlinear transitions in inelastic regimes. Evaluations on multidimensional, rate-independent material models demonstrate accurate predictions of plasticity and ductile damage, with resolution invariance and parallel efficiency.
neural operatorscausal attentionspectral convolutionspath-dependent materialssinusoidal activation
Quantum MeanFlow: single-shot generative sampling on NISQ hardware
Quantum MeanFlow (QMF) is introduced as a single-step quantum generative sampling method, addressing the sequential circuit submission drawback of Quantum Flow Matching (QFM) on NISQ hardware. QMF learns the average velocity over a time interval using parameterized quantum circuits, contrasting with QFM's instantaneous velocity field learning. Benchmarked on MNIST, QMF outperforms single-step QFM in image quality across all shot counts, though it lags behind multi-step QFM. Execution on IBM quantum computers demonstrates that best-of-N rejection sampling mitigates device noise, particularly benefiting QMF's single-circuit-evaluation-per-image approach.
quantum generative modelsflow matchingnisq hardwareparameterized quantum circuitsrejection sampling
WeaveMark: Robust and Scalable Multi-bit LLM Watermarking via Coded Payload Spreading
WeaveMark introduces a robust multi-bit watermarking scheme for LLMs via coded payload spreading, improving the trade-off among extraction accuracy, text quality, and payload capacity. The method employs multi-bit-per-token spreading, soft-decision error-correcting codes, and unbiased multilayer reweighting, with dedicated zero-bit layers for watermark detection. Experiments demonstrate significant gains: 89.8% match rate for 32-bit messages at 200 tokens (vs. 20.8% for BiMark) and 86.0% under 10% substitution attacks on 16-bit messages (vs. 30.7%), while preserving text quality.
multi-bit watermarkingpayload spreadingerror-correcting codestext quality preservationsubstitution attacks
Breadth Beats Depth: Improving GCG-Based Jailbreak Optimization with Breadth-Oriented Suffix Search
BOSS enhances Greedy Coordinate Gradient (GCG)-based jailbreak attacks by introducing breadth-oriented suffix search, addressing limitations of deep greedy search and averaged adversarial loss. The method employs Tail-Focused Adversarial Loss (TFAL), standard source loss, and behavior coverage to select terminal suffixes, then explores multiple short trajectories while prioritizing promising ones. Experiments demonstrate improved attack success rates across GCG-based methods and reduced optimization time on public benchmarks.
greedy coordinate gradientjailbreak attacksadversarial losssuffix searchbehavior coverage
DMRL: Document-Mediated Reinforcement Learning for Skill Optimization in Advertising Recommendation
We propose Document-Mediated Reinforcement Learning (DMRL), a skill self-evolution framework for advertising recommendation that models skill document optimization as structured editing actions. DMRL employs an upper-level agent for controlled document edits and a frozen lower-level task agent for evaluation via A/B testing, enhanced by Dual-Relative Policy Optimization (DRPO) for robust advantage estimation and Long-term Reward Predictor (LRP) for long-term outcome prediction using disentangled representation learning and cross-attention transfer. Deployed on a large-scale short-video ads platform, DMRL outperforms state-of-the-art baselines across key advertising metrics.
document-mediated reinforcement learningdual-relative policy optimizationlong-term reward predictordisentangled representation learningcross-attention transfer
GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation
GenCAR proposes α-Valid Counterfactual Recommendation (α-VCR) for out-of-distribution (OOD) recommendation, coupling counterfactual supervision with calibrated set selection to control proxy-label false discovery rate (FDR). The method stabilizes preference representations, intervenes on environmental factors, grounds LLM proposals via preference anchors and trust-radius filtering, and uses conformal p-values for Benjamini-Hochberg selection. Theoretical guarantees include bounded counterfactual approximation error and finite-sample FDR control under exchangeability or positive dependence. Experiments show improved OOD candidate recovery across benchmarks while auditing realized false discovery proportions.
counterfactual recommendationfalse discovery rateout-of-distributionconformal p-valuespreference anchoring
Exact Limits of Random Projections for Preserving Geometry: Distance Recovery, Nearest-Neighbor Rankings, and Covariance Shape in Gaussian Models
The work establishes precise limits of random projections for preserving geometric information beyond Johnson-Lindenstrauss (JL) distance guarantees. Analyzing isotropic Gaussian data, the authors derive closed-form singular values of the optimal linear decoder for squared-distance feature recovery, showing the k-th singular value scales as (m/d)^(k/2). Key findings include: (1) rank-m sketches retain ≤m/d variance fraction for single-distance features, (2) Kendall correlation scales as 2/π√(m/d) when m/d→0, and (3) JL-compliant projections may lose all geometric comparability when log n ≪ m ≪ d. The analysis reveals JL preservation inadequately quantifies usable geometric structure.
johnson-lindenstraussrandom projectionsgaussian modelsdistance preservationsingular values
SoK: Where Do Flow Labels Come From? Auditing Label Provenance in Encrypted Traffic Benchmarks
This paper audits label provenance in encrypted traffic benchmarks, identifying two recurring strategies: coarse inheritance and overstrict filtering. The authors analyze 14 benchmark entries, finding that none expose a countable pre-selection population and that task objects in downstream papers disagree with recovered records in 8 of 23 referenced cells. They derive a representation-relative ceiling on balanced accuracy for classifiers using strict side-channel features, ranging from 0.56 to 0.76 on public benchmarks. Filtering benchmarks discard connections that raise macro accuracy from 0.44 to 0.65 through same-run co-occurrence features. The study concludes with recommendations for benchmark builders and users.
encrypted trafficlabel provenanceside-channel featuresbalanced accuracyco-occurrence features
HyperMC: Multi-Fidelity Hyperparameter Tuning for Stochastic Gradient MCMC
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Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization
We propose Scalable Bayesian Optimization of Composite Functions (SBOCF), a simulation-efficient method for solving image-based inverse problems in materials characterization. SBOCF exploits the composite structure of image-matching objectives and intermediate simulation information by representing PACBED images with patch-level summaries and correction terms, reducing modeled outputs from 24,649 to 11. Evaluated on SrTiO3 benchmarks under a 50-simulation budget, SBOCF outperformed standard Bayesian optimization, reducing median final SSE by up to 290× in thick-sample cases. Experimental validation yielded parameter estimates consistent with prior reports, and downstream ptychographic reconstructions recovered sharp atomic features. SBOCF demonstrates promise for inverse problems with expensive simulators and high-dimensional outputs.
bayesian optimizationinverse problemsmaterials characterizationpacbedptychographic reconstruction
A Computational Comparison of Fourier Spectral Differentiation and Spatial Automatic Differentiation in Periodic Physics-Informed Neural Networks
The study compares Fourier spectral differentiation and spatial automatic differentiation (AD) in periodic physics-informed neural networks (PINNs), demonstrating that Fourier methods yield significant computational advantages. By fixing all other network components and testing on Allen--Cahn, Korteweg--de Vries, and Kuramoto--Sivashinsky equations, Fourier differentiation achieves 2.90× to 18.52× training speedups and reduces GPU memory usage by 68.7%–94.1% compared to AD, while maintaining comparable L2 error. The method requires uniform structured grids but shows no consistent accuracy disadvantage.
physics-informed neural networksfourier spectral differentiationautomatic differentiationperiodic pdesgpu memory optimization
A Unified Rate-Distortion Perspective on Vector, Product, and Scalar Quantization
This paper introduces a unified rate-distortion framework for analyzing vector, scalar, and product quantization in discrete visual tokenization. By modeling quantization as lossy compression, the authors characterize coding rate via token count and codebook size, and distortion via quantization error. They resolve three key questions: (1) distortion minimization, not codebook utilization, drives reconstruction fidelity due to STE-induced gradient discrepancy; (2) fairness in quantization comparison requires controlling latent feature statistics and identical coding rates; (3) the VQ-PQ-SQ distortion hierarchy is recovered, with modern VQ methods achieving lowest distortion. The framework provides a controlled basis for evaluating intrinsic quantization effectiveness under fixed-rate constraints.
quantizationrate-distortionvisual tokenizationcodebookdistortion
Compositional Spectral Prompts for LLM-based Online Time Series Forecasting
CoSPOT introduces a novel LLM-based framework for Online Time Series Forecasting (OTSF) that addresses long-term adaptation and unseen pattern generalization. The method leverages a frozen pre-trained LLM backbone and employs compositional spectral prompts grounded in frequency-domain bases to guide the model with input distribution, minimizing parameter updates during online adaptation. CoSPOT decomposes time series into frequency bases and composes spectral basis prompts based on amplitudes, enabling representation of unseen patterns as combinations of learned prompts. Extensive experiments on real-world datasets demonstrate CoSPOT's superiority in extended online phases and cross-dataset settings with significant distribution shifts.
online time series forecastingcompositional spectral promptsfrequency-domain basesllm-based frameworkparameter updates
IDEEA: training-free Input-Dependent stEEring via Activation cluster matching
IDEEA introduces a training-free framework for input-dependent steering in large language models (LLMs), addressing the limitation of input-independent methods that use a single steering direction across all inputs. By clustering positive and negative activation supports per attention head and solving an optimal-matching problem, IDEEA constructs cluster-conditional steering directions. At inference, it selects the direction best matching the input's activation, preserving the original representation while aligning toward the target concept. IDEEA improves the truth × info rate in TruthfulQA by an average of 9.9% (up to 23.5%) over input-independent baselines.
input-dependent steeringactivation clusteringoptimal-matchingattention headtraining-free
TC-Next: Zero-Shot Multimodal Cyclone Forecasting
TC-Next introduces a zero-shot multimodal deep learning model for tropical cyclone forecasting, leveraging GraphCast forecast fields and GridSat infrared satellite imagery to predict track and intensity at 6-24h leads. The model, trained exclusively on Western Pacific data, employs generic atmospheric variables and demonstrates transferability across forecast systems like Pangu-Weather and IFS HRES without retraining. TC-Next reduces track error by 15-44% and intensity error by 3-6x compared to TempestExtremes, maintaining superiority in zero-shot applications on WeatherNext Cyclones. Ablation studies confirm that multimodal inputs enhance tracking accuracy at all lead times and intensity prediction at longer leads.
zero-shot learningmultimodal deep learningtropical cyclone forecastinggraphcastgridsat
XMerge: Cross-Axis Selection and Reconstructive Layer Merging for LLM Depth Compression
XMerge introduces a post-training method for depth compression in large language models (LLMs) without task labels, fine-tuning, or architectural changes. It employs cross-axis selection to identify low-impact transformer blocks and local boundary reconstruction to refit adjacent blocks, preserving output quality. Evaluated across seven Llama and Qwen models (0.5B-8B parameters) and three layer-reduction levels, XMerge outperforms five baselines, particularly at aggressive removal (k=4), ranking first on six of seven backbones on CORE (22-task aggregate) and MMLU. It avoids perplexity increases, maintains calibration, and recovers construction costs after tens of thousands of requests. Ablations highlight local reconstruction as the primary contributor to gains.
depth compressioncross-axis selectionlocal boundary reconstructionperplexitycalibration
DynG-Diff: A State-Aware Dynamic Guidance Diffusion Framework for Probabilistic Time Series Forecasting
DynG-Diff introduces a state-aware dynamic guidance diffusion framework for probabilistic multivariate time series forecasting, addressing information heterogeneity across variables. The method employs a two-stage training strategy with an unconditional diffusion backbone for joint distribution modeling and a lightweight state-aware policy network that adaptively infers variable reliability, outputting a dynamic guidance strength matrix. This matrix, formulated as the local precision of the observation distribution, enables precise guidance for high-confidence variables while filtering anomalous noise. Experiments on real-world benchmarks show competitive forecasting performance and improved robustness under severe observation corruption compared to state-of-the-art conditional diffusion models.
probabilistic forecastingdiffusion frameworkstate-aware policydynamic guidanceinformation heterogeneity
The Dynamics of Continuous Mixture Collapse in Language Models
The study investigates the collapse of continuous mixture states in language models, identifying three distinct failure mechanisms: transformer-induced geometric distortion amplified by training, softmax-autoregressive dynamics causing mixture amplification or contraction, and context-dependent correction requirements scaling with component count. Through theoretical analysis and empirical validation across multiple models, the authors demonstrate that pretrained models predominantly exhibit amplifying dynamics near theoretical thresholds, while exact mixture preservation necessitates increasingly complex context-dependent corrections. Findings highlight fundamental architectural limitations in maintaining continuous reasoning states during autoregressive generation.
continuous mixturestransformer architectureautoregressive feedbacksoftmax readoutcontext-dependent correction
Act More, Decide Less: Skill-Guided Adaptive Action Chunking for Long-Horizon LLM Agents
SPACE introduces skill-guided adaptive action chunking to improve long-horizon LLM agent efficiency by emitting variable-length action sequences instead of single primitive actions. The method distills chunk-boundary supervision from programmatic skills derived from successful trajectories, using hybrid on-/off-policy optimization with chunk-aware credit assignment. Experiments on ALFWorld and ScienceWorld demonstrate a 7.0%-31.3% success rate improvement over baselines and up to 78.9% reduction in LLM decision rounds.
action chunkinglong-horizon tasksprogrammatic skillshybrid optimizationcredit assignment
Source-Free Class Relearning: Diagnosing Forgetting in Class Unlearning
The paper introduces Source-Free Relearning Audit (SFRA), a method to diagnose forgetting in class unlearning by assessing recoverability of forget classes without access to source data. SFRA generates synthetic embeddings in representation space, constructs probe sets via model-guided confidence filtering, and updates classifier heads to recover forget classes. Theoretical analysis establishes conditions for recoverability via gradient updates. Experiments on CIFAR-10, CIFAR-100, and TinyImageNet with ResNet-18, ViT-B/16, and Swin-T show that several unlearning methods exhibit substantial source-free recoverability, with some exceeding retrained references. The Relearning Score (RS) jointly measures forget-class recovery and retain-accuracy preservation.
class unlearningrepresentation spaceconfidence filteringrelearning scoresource-free
Perceptually Regularized Diffusion Model for Image Super-Resolution
We propose a perceptually regularized diffusion framework for image super-resolution, addressing the limitations of standard diffusion models that rely solely on pixel-domain noise-prediction loss. Our method incorporates perceptual-loss-based regularization to enforce perceptual fidelity, improving training convergence and preserving fine image structures. Experiments on benchmark datasets demonstrate enhanced perceptual quality while maintaining competitive distortion metrics, validating the effectiveness of perceptual regularization in diffusion-based super-resolution.
diffusion modelsperceptual lossimage super-resolutionregularizationtraining convergence
Train What You Deploy: Closing the MLP Reachability Gap in Low-Rank Clone Distillation
We introduce a principle, 'train what you deploy,' addressing the reachability gap in Low-Rank Clone (LRC) distillation, where deployed MLP weights remain untrainable. Two mergeable realizations, Dense-LRC and CORE-LRC, enable training the full deployed matrix without altering inference parameters or FLOPs. This recovers stranded capacity, improving average accuracy by +2.36/+2.71/+10.45 across three teachers (Llama3.2-3B, Llama3.1-8B, Qwen2.5-3B), with the largest gain on Qwen2.5-3B, achieving 2x token efficiency. A 1.5B student matches its 9T-token teacher's 9-task macro-average using ~10B distillation tokens, while a 2.7B student outperforms Meta's official Llama3.1-8B compression at ~900x fewer tokens. Results are from single-seed runs on the LRC backbone.
low-rank clonemlpdistillationtoken efficiencyreachability gap
Posterior Tempering Explains Variance Inflation in Linear and Generalized Linear Thompson Sampling
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Linear Fusion MultiDiffusion for Fast Training-Free Spherical Panorama Generation
LF-MultiDiffusion introduces a training-free spherical panorama generation method that extends MultiDiffusion by enabling linear projections between target and reference image spaces. The approach reformulates latent aggregation as a regularized least-squares problem, solved efficiently with a Krylov-based iterative solver within the denoising loop. This formulation achieves denser and more natural mappings, reducing the number of required perspective views and improving inference efficiency. Experimental results demonstrate that LF-MultiDiffusion outperforms the strongest training-free baseline in visual quality, text alignment, and panoramic consistency while providing a 15.36× speedup.
panorama generationmultidiffusionkrylov-based solverdenoising looplatent aggregation
CAHR-Net: Condition-Adaptive Hysteresis Reconstruction for Compact and Interpretable Magnetic Core Loss Modeling
CAHR-Net introduces a condition-adaptive hysteresis reconstruction network for magnetic core loss modeling, preserving interpretability by injecting operating conditions (frequency, temperature, waveform statistics) into intermediate representations via feature-wise linear modulation. The method employs a matched training protocol with AdamW optimizer, cosine scheduling, and a staged reconstruction-to-power-loss objective. Evaluated on the MagNet benchmark, CAHR-Net achieves a 6.89% average p95 relative error with only 1874 parameters, outperforming black-box solutions with 48x fewer parameters. It improves the physical reconstruction backbone's average p95 from 7.47% to 6.89% and reduces material D's p95 from 16.40% to 14.87%. The gains are attributed to coupling physical loop reconstruction, structured condition modulation, and optimized training trajectory.
hysteresis reconstructionfeature-wise modulationmagnetic core lossadamw optimizercosine scheduling
Morphology signal in whole slide image foundation models can automatically triage slides
We propose a pipeline leveraging whole slide image (WSI) foundation models for automated slide triage in cancer diagnosis, eliminating manual curation. Using zero-shot classification predictions from publicly available WSI FMs, we rank slides by tumor content, demonstrating that morphology signals in FMs suffice for accurate triage. We introduce a ranked evaluation formulation for benchmarking FM performance, showing tumor slides are identified in the top-2 ranked slides for patients with up to 43 slides across multiple datasets.
whole slide imagefoundation modelszero-shot classificationmorphology signalslide triage
A Unified Particle Filter LSTM for Data-Driven Process Simulation
The paper introduces a Unified Particle Filter LSTM (Unified PF-LSTM) for data-driven process simulation, addressing partial observability in event logs by maintaining multiple latent state hypotheses. The method represents particle beliefs via weighted means and moment-generating function features, enabling joint prediction of next activities and sojourn time quantiles. Evaluated on three emergency department datasets, it outperforms baselines in routing, duration, and system-level behavior reproduction, particularly when process dynamics are partially observable.
particle filterlstmprocess simulationevent logssojourn time
Network-Aware Forecasting on Wireless Access Points
The work introduces extit{network-aware deployability}, a framework for validating predictive ML models on wireless access points (APs) under resource contention with networking tasks. The method involves qualification on target hardware followed by execution profiling under packet-service constraints. Benchmarks reveal significant discrepancies: five model implementations run 6.1--19.1× slower on APs versus Raspberry Pi 5, with memory usage varying by 22%. Under network saturation, default execution of a small model increases p99 RTT by 76% and reduces throughput by 7.06%, highlighting critical trade-offs for joint networking-ML deployment.
wireless access pointsresource contentionnetwork-aware deployabilityexecution profilinground-trip time
FlashKAN: B-Spline KANs via Truncated Power Form
FlashKAN accelerates Kolmogorov-Arnold Networks (KANs) by replacing Cox-de Boor recursion with truncated power form for B-spline activations, reducing 90% of forward-pass time. The method employs: (1) a torch.compile-fused GPU kernel eliminating recursion and scatter-gather operations, (2) bounded-coordinate stabilization to prevent catastrophic cancellation by clamping inputs to [0, k+1], and (3) a production-ready package (pip install flashkan) as a drop-in KAN replacement. Results show uniform cubic B-splines expressed via five shifted (x)_+^3 terms, validated through approximation theory.
kolmogorov-arnold networksb-spline activationstruncated power formcox-de boor recursioncatastrophic cancellation
Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregation
The paper introduces lightweight multi-secret-key protocols for private average aggregation in Federated Learning, based on RLWE-based Homomorphic Encryption. The method avoids collective public key generation by having clients encrypt updates under individual secret keys while maintaining homomorphic aggregation and collaborative decryption capabilities. By tracking and canceling ciphertext noise during decryption, it eliminates the need for large λ-dependent smudging noise. The BFV-based and CKKS-based variants demonstrate reduced ciphertext expansion and online costs while preserving practical aggregation performance, with proven security against semi-honest adversaries corrupting the aggregator and up to L-1 clients.
federated learninghomomorphic encryptionrlweprivate aggregationmulti-secret-key
Refining Heuristic-Based Bitcoin Address Clustering with Graph Neural Networks
The work refines heuristic-based Bitcoin address clustering by introducing contrastive embeddings from graph neural networks (GNNs), addressing limitations of flat cluster assignments and erroneous merges. It releases a public dataset of Bitcoin transaction graphs with labeled clusters, proposes a method for learning heuristic-consistent address embeddings backed by theoretical intuitions, and employs hierarchical clustering for finer analysis and suspicious merge detection. The approach enables more modular and accurate user-level address clustering compared to existing heuristic methods.
bitcoingraph neural networkscontrastive learninghierarchical clusteringaddress clustering
OR-Transformer: Scaling Real-Time Decision-Making to 1,000 Items
OR-Transformer introduces a deep reinforcement learning framework for large-scale joint replenishment under stochastic demand, addressing the computational limitations of rolling-horizon MILPs and credit assignment challenges in high-dimensional RL. The method employs an item-permutation-equivariant Transformer architecture and pathwise-gradient training through inventory dynamics. Evaluated on problem sizes up to 1,024 inventory items, OR-Transformer outperforms learning-based and rolling-horizon MILP baselines, particularly at scale, and reduces online decision-making time by over 4 million times compared to MILP solvers, enabling real-time supply chain operations.
joint replenishmenttransformer architecturepathwise-gradient trainingstochastic demandmixed-integer linear programs
CRISP: Cliff-awaRe Input-adaptive Sparse Prefilling with Structural-Mass-Motivated Routing
CRISP introduces a novel dynamic sparse attention method for long-context LLM inference, addressing quadratic scaling in attention prefilling. The method replaces Jensen-Shannon Divergence routing with C_struct, a structural proxy that directly measures mass at Vertical-Slash positions, eliminating computational overhead. CRISP formalizes the post-softmax mass cliff and employs a sink-aware threshold to mitigate O(n) background noise accumulation in long contexts. Evaluated on InfiniteBench, RULER, and LongBench across two model families, CRISP outperforms existing sparse methods, matching or exceeding dense attention on retrieval-heavy tasks, achieving up to +28.0 pp improvement and 5.30x attention speedup at 512k tokens.
sparse attentionattention prefillingpost-softmax massdynamic routinglong-context inference
Basin Geometry and Reliable Recall of Dynamical Memories in Reservoir Computing
The study identifies a novel 'octopus-like' basin geometry in reservoir-computing associative memory, characterized by a robust 'head' near attractors and thin, intertwined 'tentacles' spanning state space. Despite unpredictable dynamics in tentacular regions due to near-zero uncertainty exponents, cue-driven generalized synchronization reliably drives the system into the robust basin head. A quantitative relation is established linking minimum cue duration, synchronization rate, and basin-head radius. This phenomenon is also observed in trained recurrent neural networks, suggesting broader applicability beyond reservoir computing.
reservoir computingbasin geometrygeneralized synchronizationuncertainty exponentsrecurrent neural networks
Latent unified smooth Hamiltonians for excited state chemistry
A neural network architecture is proposed for unified modeling of electronic ground and excited states in molecular systems, incorporating conical intersections and non-adiabatic couplings. The method learns a latent, implicit basis representation of the electronic-state Hamiltonian, extendable to additional operators like transition dipole moments. Evaluated on thymine and azobenzene, the model accurately reproduces energies and oscillator strengths for ground- and low-lying excited states, capturing critical geometries and Berry phase accumulation. The approach combines quantum chemistry principles with transformer-based representation learning, enabling fast and accurate simulations of photochemical systems.
electronic-state hamiltonianconical intersectionsnon-adiabatic couplingstransition dipole momentsberry phase accumulation
Reinforcement learning to choose optimizers
The paper introduces 'Reinforcement Learning to Choose Optimizers', a method that formulates optimizer selection as a sequential decision-making problem. A recurrent policy dynamically selects both the optimizer and its duration based on the current run state, leveraging a portfolio of gradient-based and derivative-free optimizers. The approach employs a context proxy for gating network conditioning and a decoupled actor-critic trained on tasks designed to prevent optimizer dominance. Evaluations demonstrate that the learned policy outperforms individual optimizers across various budgets and maintains robustness under distribution shifts.
optimizer selectionsequential decision-makingrecurrent policygradient-based optimizersderivative-free optimizers
D-FROST: Decentralized Federated pRompt-tuning via Optimal tranSporT for Non-IID and Imbalanced Data
D-FROST introduces the first decentralized federated learning (DFL) framework for prompt tuning, addressing challenges of non-IID and imbalanced data. The method formulates prompt tuning as a Wasserstein-based optimization problem over prompt measures, leveraging optimal transport (OT) to merge neighborhood prompts into compact representative sets. Theoretical analysis bounds the Wasserstein consensus error and establishes convergence of the network-level prompt barycenter to a neighborhood of stationarity. Empirical results demonstrate D-FROST's effectiveness in heterogeneous client data scenarios, providing a parameter-efficient alternative to full-model updates in DFL.
decentralized federated learningprompt tuningwasserstein distanceoptimal transportnon-iid data
Ten Architectures, One Error: Shared Failure Modes in Hyperspectral Classification under Spatially Disjoint Evaluation
This work introduces a leakage-free evaluation protocol for hyperspectral image classification that enforces spatial separation between training and test pixels based on the model's receptive field. Testing ten diverse architectures (classical, spectral, spectral-spatial, transformer, vision-backbone, and state-space) reveals an average Macro-F1 drop of 0.147 under the new protocol, with model rankings shifting by up to five positions. The study identifies a shared spectral ambiguity across all architectures, as they consistently misclassify the same pixels, highlighting unresolved challenges in hyperspectral data.
hyperspectral classificationreceptive fieldspatial separationmacro-f1spectral ambiguity
Emergence of Fibrations, Compression, and Symmetry Breaking in Artificial Neural Networks
The study demonstrates that learning in deep neural networks induces local symmetries known as fibrations and coverings, which are proven to be stable attractors of stochastic gradient descent. These symmetries emerge across diverse architectures (multilayer, convolutional, recurrent, transformer) and enable model compression to 17% of original size without performance loss. Controlled symmetry breaking further addresses loss of plasticity, achieving state-of-the-art continual learning performance. The findings provide a symmetry-based framework for interpretable and efficient AI systems.
fibrationscovering symmetriesmodel compressionloss of plasticitycontinual learning
Toward Explainable and Policy-Aware AI for Carbon Credit Price Prediction: A Research Framework for Emerging Carbon Markets
The paper introduces EPA-CarbonNet, a six-layer architecture for carbon credit price prediction that integrates market series and policy text via cross-attention, calibrated intervals, and policy-attributed explanations. The model is evaluated on eleven years of daily S&P carbon index data, revealing underwhelming performance: a random walk outperforms EPA-CarbonNet on five-day RMSE (0.0365 vs. 0.0475), SHAP rankings show moderate agreement (rho = 0.54), and policy attention fails to align with documented regulatory events. Directional accuracy (58.6%) exceeds baselines. The authors identify ten recurring gaps in carbon market forecasting and provide code, data, and results for reproducibility.
carbon credit predictioncross-attentioncalibrated intervalspolicy-attributed explanationsshap rankings
Pooling and Drift in Delayed Bandits
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A Study of Conditional Diffusion Models for Open-Loop Control under Dry Friction and Stiction
Action Diffusion, a conditional diffusion model for open-loop control, demonstrates improved performance in point-mass systems with dry friction and stiction. The method employs a compact 1D U-Net to generate bounded control sequences conditioned on initial and target states, leveraging structured control primitives from the training prior. Compared to uniform random shooting, structured dataset random shooting, and Cross-Entropy Method (CEM), Action Diffusion reduces terminal error and stuck steps, particularly in low-sample regimes. Results indicate that conditional diffusion effectively generates temporally coherent control sequences that overcome stiction by recombining structured primitives for state-to-state control.
action diffusiondry frictionstictionopen-loop controlconditional diffusion
CAT-Flow: Curvature-Adaptive sTeps for Flow Matching
The paper introduces CAT-Flow, two training-free algorithms (CAT-OV and CAT-OT) that adapt step-sizes during Flow Matching inference by exploiting curvature information. CAT-OT estimates temporal curvature via finite-difference approximation of the vector field's time-derivative, while CAT-OV approximates spatial curvature through the vector field's gradient. Both methods achieve constant-order truncation error bounds and require no additional neural evaluations. Experiments across four text-to-image Flow Matching models show CAT-OV and CAT-OT reduce required generation steps by up to 40% while maintaining image quality compared to existing heuristics.
flow matchingadaptive step-sizegradient flowfinite-difference approximationtruncation error
Hearing the Whispers: Black-Box Membership Inference Attacks on Finetuned TTS Models
We introduce the first black-box membership inference attack (MIA) framework tailored for fine-tuned Text-to-Speech (TTS) models, addressing unique challenges in query generation and representation engineering. For query generation, we characterize the feasible query space and establish criteria (scorable extent, memorization elicitation) to evaluate five representative queries, identifying recitation as optimal. For representation engineering, we extract multi-level speech representations from embedding models and temporally align generated and target audio for fine-grained comparison. Experiments on CosyVoice2, F5-TTS, and XTTS-v2 fine-tuned on VCTK and British Dialect datasets demonstrate severe privacy leakage, with speaker-level AUC >0.80 (approaching 1.0) and record-level AUC 0.80-0.90, even in challenging same-speaker scenarios. We further identify speech characteristics disproportionately vulnerable to memorization.
membership inference attacktext-to-speechquery generationrepresentation engineeringtemporal alignment
Generative Diffusion Surrogates with Analytical Variance Schedule
The authors propose generative diffusion surrogates with analytically derived variance schedules for stochastic transport systems, enabling time-calibrated probabilistic modeling. By prescribing the forward noising rate as the time derivative of the variance—often known from macroscopic theory—the method enforces a variance path while learning non-Gaussian structure through score fields. Applied to ballistic-to-diffusive transport in turbulent plasmas, the surrogate accurately matches test-particle distributions, reproduces laboratory-measured variance scaling, and tracks simulated kurtosis evolution without schedule tuning, facilitating calibrated emulation and likelihood-based inference.
generative diffusionvariance schedulestochastic transportscore fieldkurtosis evolution
Tri-Band Channel Measurement-Enabled Multi-Layer Digital Twin for Terahertz Wireless Data Centers
A multi-layer digital twin (DT) framework is proposed for terahertz (THz) wireless data centers, enabling efficient planning and real-time optimization. The framework comprises physical, channel, evaluation, and manipulation layers, constructed progressively from tri-band channel measurements at 140, 220, and 300 GHz. A measurement-calibrated physical twin is established by optimizing geometry, material, antenna, and hybrid propagation models. An AI channel twin, based on a line-of-sight-aware implicit neural field, learns location-dependent channel statistics for real-time prediction of received power and LoS probability. Experimental results show the AI twin achieves lower power reconstruction error than baselines, with ceiling-mounted AP deployment achieving over 90% coverage under a 10 dB SINR threshold.
terahertz communicationdigital twinimplicit neural fieldsignal-to-interference-plus-noise ratiochannel reconstruction
FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making
FAIRLENS introduces a benchmark and evaluation framework for assessing fairness and validity in vision-language models (VLMs) across high-stakes domains: hiring, legal, and healthcare. It pairs real face images spanning gender, race, and age groups with over 100K image-question pairs, evaluating responses via demographic parity, soundness, demographic association, and bias in free-text generation. Evaluating eight VLMs reveals primary failures in unwarranted inference rather than unequal treatment, with models inferring qualifications, threat, illness, or professional roles from faces instead of abstaining. Parity gaps are small but impactful, and bias in free-text responses is loosely coupled with multiple-choice accuracy.
vision-language modelsdemographic paritysoundnessfree-text generationhigh-stakes domains
Median-of-Means as an Extremal Convex Estimator and a Nonconvex Route to the Trimmed Oracle
The paper revisits median-of-means estimation via deterministic optimization, introducing a family of nonconvex block-Lp estimators for robust learning under heavy-tailed and adversarially corrupted data. For a block contamination model with ≥1−ε good blocks, convex block M-estimators achieve worst-case robustness constant ≥1/(1−2ε), matching classical bounds but proving the trimmed-block oracle constant 1/(1−ε) is unattainable convexly. The proposed nonconvex block-Lp family (0 median-of-meansblock-lp estimatorsrobust learningnonconvex optimizationtrimmed oracle
FORGE: Forward-Only Test-Time Adaptation for Integer-Only Vision Models on Microcontrollers
FORGE introduces a forward-only test-time adaptation (TTA) method for integer-only convolutional networks on microcontrollers, addressing the challenge of adapting BN-folded models without backpropagation. By re-normalizing folded convolutions' outputs to clean training statistics using forward-pass estimates, it recovers 93% of gradient-based TENT's accuracy gain (+20.9 points) while adapting only 3 of 21 layers. The method operates on deployed int8 models, generalizes across datasets (up to 200 classes) and architectures, and incurs minimal overhead (8.3 mJ, 6.8% of inference energy) on an ESP32-S3 with SIMD-optimized execution.
test-time adaptationinteger-onlymicrocontrollersbatch-normalization foldingforward-pass estimation
Reinforcement Learning and Rule-Based Peer-to-Peer Pricing in Residential PV-BES Communities
The paper compares rule-based and reinforcement-learning (RL) approaches for peer-to-peer electricity pricing in photovoltaic communities, evaluating bill-sharing, mid-market rate, and supply-demand-ratio (SDR) pricing as benchmarks against Deep Q-Network-based RL policies. RL variants include multiplier-based and SDR-shaped pricing, with storage-integrated cases analyzed separately. Results show rule-based methods outperform RL in PV-only configurations (best RL policy: EUR 734.23 savings), but RL gains improve with battery storage (EUR 978.52). SDR-shaped RL consistently surpasses multiplier-based parameterization, though benefit distribution remains uneven across households, highlighting rule-based methods' competitiveness where directly comparable.
reinforcement learningpeer-to-peer pricingphotovoltaic communitiessupply-demand-ratiodeep q-network
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Generated automatically at 2026-09-03 22:18 UTC. Summaries and keywords are produced by an LLM and may contain inaccuracies — always consult the original article.