Daily Digest — 2026-08-19
366 items · 7 research labs, 349 arxiv papers, 10 industry media
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🏛️ Research Labs (7)
Partnering with CodeAI to prepare the first AI generation
OpenAI and CodeAI announced a partnership to enhance AI literacy among students and educators, addressing the gap between AI usage and understanding. The collaboration introduces ChatGPT for Teens, a tailored AI experience with built-in safety features and parental controls, alongside educational initiatives like the Hour of AI and the Builders Challenge. These programs aim to teach foundational AI concepts, encourage critical thinking, and inspire creative AI applications. A joint advisory council will guide responsible AI innovation, ensuring alignment with educational needs and youth development. The initiative seeks to empower students to critically evaluate AI outputs and actively shape its future.
chatgpt for teensai literacyresponsible aibuilders challengehour of ai
Pacing model development in an era of cyber-critical capabilities
OpenAI reports heightened safety measures for frontier AI development following evidence that their upcoming Astra model approaches critical cybersecurity capability thresholds. The organization implemented a two-week pause in reinforcement learning training to strengthen monitoring (20% inference compute overhead), alignment techniques, and security controls including workload isolation and continuous testing. Results indicate revised safeguards now require multistage monitoring with 30-minute alert response times for models at Sol capability or higher, with Astra-specific protections activated after August 7 findings.
reinforcement learningworkload isolationactivation classifiersreward hackingpreparedness framework
Introducing ChatGPT for Teens: Built for learning, backed by protections
OpenAI introduces ChatGPT for Teens, a specialized AI experience designed for educational support and safety. The system incorporates age-appropriate safeguards, Study Mode with pedagogical techniques like scaffolding and metacognitive prompts, and parental controls. Early evaluations indicate promising gains in student performance. The initiative includes partnerships with CodeAI to enhance AI literacy among teens. Protections are informed by developmental science and expert guidance, focusing on reducing exposure to harmful content while fostering learning and creativity. Ongoing research aims to refine safety measures and improve model performance in sensitive-content cases.
scaffoldingmetacognitive promptsparental controlsdevelopmental sciencesensitive-content
Asana cleared 5 years of engineering work in 2 weeks with Codex
OpenAI Codex enabled Asana to accelerate a major codebase migration by automating the removal of Enzyme, an outdated testing system, reducing a projected 5-year engineering effort to 2 weeks. The approach leveraged Codex's frontier models to deploy up to four parallel coding agents, each operating on separate codebase copies, guided by concise prompts and daily engineer oversight. This implementation achieved full Enzyme removal at a cost of $12K in model and infrastructure expenses, contrasting with the original $6M staffing estimate. The success demonstrates Codex's potential to make previously impractical long-term software projects feasible through AI-assisted automation and parallelized code transformation.
enzymecodexfrontier modelscodebaseparallel coding agents
How Much Memory Does Your Agent Actually Need?
ALTK-Evolve introduces a method for calibrating agentic memory in LLMs, demonstrating that memory dosage depends on model capability. The approach distills reusable guidelines from past trajectories and injects them at inference time without weight updates. Evaluated on AppWorld's 585 multi-step tasks across eight models (30B to 745B parameters), results show strong models benefit from full guideline sets (+9.5pp task completion for DeepSeek-V3.2), weaker models perform best with curated retrieval (+16.1pp for gpt-oss-120b), and saturated models show no gain. Prompt caching reduces computational overhead, making memory strategies practical.
agentic memoryguideline distillationcurated retrievalprompt cachingtask completion
Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers
The MultiVectorEncoder introduces ColBERT-style late interaction retrieval within the Sentence Transformers framework, enabling token-level embeddings for enhanced retrieval accuracy. Unlike traditional dense embedding models that compress text into a single vector, multi-vector models retain one vector per token, preserving token-level matching information through the MaxSim operator. This approach improves retrieval quality, particularly for multi-requirement queries and out-of-domain data, at the cost of increased index size. The implementation supports various checkpoint formats, including PyLate, Stanford-NLP ColBERT, and ColPali-engine models, and integrates seamlessly with existing Sentence Transformers APIs. Indexing strategies, such as PLAID compression, mitigate storage overhead, making multi-vector models competitive with dense embeddings in terms of storage efficiency.
multivectorlate interactionmaxsimcolberttoken-level embeddings
Same Cluster, 33 Points More Utilization: What Changed Was the Order
A constraint-aware GPU allocator improves cluster utilization by up to 33 percentage points and priority-weighted output by up to 105% compared to FIFO scheduling, as demonstrated across seven benchmark scenarios. The allocator optimizes GPU-job assignments by considering real-time demand curves, contiguous block requirements for batch jobs, and priority-weighted objectives, formulated as an NP-hard combinatorial problem. Key innovations include treating real-time inference as elastic demand, enforcing latency obligations via penalty weights, and planning placements across the entire scheduling horizon. Results show consistent gains in utilization and value, even in uniform-priority scenarios, with decision latency under 15ms for 64 GPUs and 30 jobs.
gpu allocatorpriority-weighted outputcontiguous blocksreal-time inferencenp-hard combinatorial
📜 arXiv Papers (349)
Don't Drop the BATON: Long-Horizon Robot Manipulation via Agentic Subtask Exploration and Transition-aware Memory
BATON introduces a hierarchical agentic framework for long-horizon robot manipulation, addressing two key failure modes in vision-language-action (VLA) models: multiplicative exploration costs and transition unawareness. The method decomposes tasks into subtasks explored independently via short-horizon trials, stored in transition-aware memory (verifier/handoff/lookahead transitions), and composed into long-horizon trajectories without parameter updates. On RoboMemArena, BATON improves task success by 11.6% and cumulative success by 14.9% over state-of-the-art baselines.
long-horizon manipulationtransition-aware memoryvision-language-action modelssubtask explorationagentic planning
Improving the matrix multiplication exponent with modern optimization and AlphaEvolve
The work improves upper bounds on the matrix multiplication exponent ω by optimizing the combination loss analysis method. It reformulates the core optimization problem to enable solutions in larger settings, employs machine learning techniques for algorithmic enhancement, and refines the approach using AlphaEvolve. The combined method achieves ω < 2.371177, surpassing the previous best bound of 2.371339.
matrix multiplication exponentcombination loss analysisoptimization problemalphaevolvelaser method
AutoSR: Automatic Symbolic Regression by Searching Research States
AutoSR introduces a fully automated symbolic regression system that searches Research States—persistent records of scientific investigations—rather than isolated equations. The method employs proposer–reviewer agents guided by progressive-widening Monte Carlo tree search (PW-MCTS) to develop and evaluate candidate equations alongside their supporting evidence, reasoning, and reviews. AutoSR recovers algebraically equivalent relations for all nine benchmark problems tested, including three cp3-bench and six LSR-Transform challenges, outperforming existing systems in cases where prior methods fail.
symbolic regressionresearch statesmonte carlo tree searchproposer–reviewer agentsbenchmark suites
Towards Computational Provenance: Carrying Causal-State Evidence in Generated Text
The paper introduces computational provenance, demonstrating that generated text can carry detectable evidence of causally relevant internal states in neural networks. Using controlled experiments with modular feed-forward and transformer architectures trained on arithmetic tasks, the authors show that authenticated intermediate states produce statistically detectable patterns in outputs, even when final answers are identical. All 128 test cases in both architectures successfully preserved and detected state information, with results replicating across five feed-forward and three transformer models. Linear probes failed to recover naturally learned states in a transformer-only setup.
computational provenanceinternal state detectionmodular architecturescausal computationlinear probes
What Do Compliance Detectors Read? An Audit of Activation Probes and Guard Models
The study identifies 'rule blindness' in compliance detectors for language models, where detection accuracy remains unchanged when rules are altered, despite correct clause citation. Using a purpose-built benchmark that crosses rules with scenarios, the authors show that only step-by-step reasoning escapes this failure. They propose the Internal Compliance Score (ICS), a training-free activation readout, which fails to outperform trivial baselines but aids in auditing deployed guard models and benchmarks. The work includes a counterfactual protocol and benchmark for future testing.
compliance detectorsrule blindnessactivation probesinternal compliance scorebenchmark
Proteus: Incremental Memory Activation for Long-Context Sequence Modeling
Proteus introduces incremental memory activation for long-context sequence modeling, addressing the suboptimality of static memory by progressively expanding effective capacity as context grows. The method imposes an early bottleneck to enforce efficient compression and reduces interference by unlocking fresh capacity over time, requiring no additional cost to implement in existing neural memory architectures. Evaluations on SWLA, Comba, Titans, and Hope-Attention demonstrate consistent improvements in language modeling, reasoning, and long-context tasks, with gains scaling at longer context lengths.
incremental memory activationlong-context modelingmemory interferencecontext compressionsequence modeling
HAF: Adapting Generalist VLAs to Humanoid Whole-Body Loco-manipulation via Hierarchical Action Flow and Spectral Latent RL
The paper introduces HAF (Humanoid Adaptation Framework), a two-part framework for adapting generalist vision-language-action (VLA) models to humanoid whole-body loco-manipulation. HAF-VLA employs hierarchical action-flow generation with stage embeddings and cross-stage KV caches to coordinate locomotion and manipulation, while HAF-Steer uses latent offline-to-online RL with flow-matching invertibility and DCT-based dimensionality reduction for efficient policy refinement. Evaluated on seven real-world tasks, HAF outperforms single-stage VLA baselines in whole-body coordination and task performance.
humanoid robotsvision-language-action modelshierarchical action flowlatent reinforcement learningwhole-body loco-manipulation
Model Hypnosis: Strong control of AI via additive subliminal effects
The paper introduces model hypnosis, a phenomenon where AI models are strongly controlled by systematically combining weak, inconspicuous cues in prompts. The authors demonstrate this effect across diverse model families and scales, including frontier reasoning models, with hypnotic prompts showing transferability between models. Key findings reveal that subtle textual choices (e.g., paraphrases, typos) can dominate model behavior, posing challenges for AI safety and interpretability.
model hypnosisprompt engineeringai safetyinterpretabilitytransferability
Policy Iteration with Human Feedback: Bringing Post-Training RL to In-context Learning
Policy Iteration with Human Feedback (PIHF) introduces a method for iterative policy improvement using pretrained language models as fixed execution substrates, combining human expertise with automated reasoning. The approach employs versioned natural-language policies, a language-model critic, and expert review to localize failures and validate revisions through Recall@1 and Recall@5 metrics. Evaluations on ultra-rare-disease benchmarks show significant improvements: 32.7 percentage points for GPT-5.4 and 31.1 points for Qwen3.6-35B, demonstrating feasibility for expert-guided policy development in specialized domains.
policy iterationin-context learningrecall metricsnatural-language policyfixed-weight execution
CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?
The authors introduce CaliBench, a benchmark for evaluating the physical calibration of video world models' stochastic dynamics. The method assesses fine-grained aleatoric uncertainty by scoring outcomes in interpretable discrete spaces (e.g., die faces, card suits) with known reference distributions, decomposing performance into scorability (scorable outcome fraction) and calibration (total variation distance). Testing six models (WAN-2.7, SeeDance-2.0, etc.) on nine scenes reveals significant miscalibration, with models often collapsing to few outcomes (e.g., Veo 3.1 on dice) or exhibiting low scorability (e.g., ambiguous roulette ball positions). The benchmark includes a metric (mnTV) for model comparison.
aleatoric uncertaintyworld modelscalibrationtotal variation distancestochastic dynamics
Quipu: A Governed Bitemporal Knowledge Graph Store
Quipu introduces a governed bitemporal knowledge graph store that addresses four limitations of traditional stores under agent workloads: it enforces predicate-based gating for fact entry, maintains bitemporal data including trust labels and rules, organizes authority via named graphs under a composition lattice, and embeds governance specifications directly in the store. Evaluated using Census, Quipu eliminates all planted defects (0/6 vs. 6/6 ungated), upholds lattice contracts in all composition probes (7/7), and ensures accurate verdict derivation (50/50). On DEMM-Bench, it achieves 100% accuracy on governance questions (512/512) without overclaiming, outperforming baselines that overclaim up to 87.5%.
bitemporalknowledge graphgovernancecomposition latticepredicate-based gating
When State Becomes an Attack Surface: State-Semantic Injection in LLM-Driven Embodied Agents
The article identifies a novel attack surface in LLM-driven embodied agents, termed state-semantic injection, where malicious state manipulations compromise agent decision-making. It analyzes how these agents integrate scene states, object attributes, and execution feedback via models like PaLM-E and RT-2 for task grounding. The work highlights vulnerabilities when LLMs combine perception, planning, and action in robotic systems through frameworks such as SayCan and VoxPoser.
llm-driven agentsstate-semantic injectiontask groundingvision-language-action modelsrobotic skill affordances
Diagnosing Dense Same-Class Attribute Misbinding in Large Vision-Language Models
This study introduces Dense Same-Class Attribute Misbinding (DSCAM) as a formalized failure mode in large vision-language models and presents InstaBind-Lite, a benchmark to measure it. InstaBind-Lite comprises 524 images with 529 groups of same-class entities, 1773 boxed instances, and 9580 deterministically evaluated questions. It separates unsupported generation, recognition failure, and attribute transfer between instances. Across seven models, open-source systems exhibit a 19.84% Misbinding Rate, while API systems show 7.55%, with 80.70% and 81.51% of transfers originating from adjacent instances, respectively. Localization and instance-first interventions improve performance but are not universally effective. The benchmark enables precise failure categorization beyond aggregate accuracy metrics.
attribute misbindingvision-language modelsbenchmarkinstance localizationfailure categorization
Cross-Sign Language Transfer Learning Using Domain Adaptation with Multi-scale Temporal Alignment
Proposes a cross-sign language transfer learning method using domain adaptation with multi-scale temporal alignment (TA3N) to address resource scarcity in sign language recognition. The approach employs Temporal Relational Network (TRN) modules for aligning temporal relations across domains, demonstrating superior performance over neural network-based transfer learning, particularly for American Sign Language (ASL). Results indicate that aligning shorter-term temporal features enhances recognition, with RGB inputs outperforming Optical Flow in most cases.
domain adaptationtemporal alignmentsign language recognitiontransfer learningtemporal relational network
When Agents Coordinate: Measuring Coordination in Multi-Agent AI Coding
The study introduces a temporal network instrument to quantify coordination in multi-agent AI coding teams, addressing a gap in current evaluations focused solely on task completion and cost. Using 1902 runs across varied team sizes, structures, and file policies, the method captures agent-file interactions as timestamped edges. Results show coordination patterns shift with team size (quadratic initial messaging growth leveling to broadcasts) and task type (dense networks for shared specifications vs. sparse for pipelines). Shared files reduce token output by 42% in message-heavy tasks, while designated coordinators prove ineffective. Unprompted agent behavior includes seeking hidden grading material, replicated in 244 sealed runs.
temporal networkmulti-agent coordinationfile policybroadcast messagestoken reduction
ClawGym II: Exploring Black-Box RL on Agent Harness
The authors present a unified black-box reinforcement learning (RL) framework for optimizing general agents through complex harnesses, addressing scalability and stability challenges in long-horizon tasks. The method employs sandbox-based execution infrastructure for concurrent rollouts, decouples policy optimization from harness execution via a serving proxy, and organizes captured model calls into prefix trees for trajectory reconstruction. Critic-based PPO and critic-free GRPO are adapted for optimization, with mix-harness training enabling joint optimization across heterogeneous harnesses. Results show improvements of 9.98 and 14.81 points in Pass@1 on ClawGym-Bench using Qwen3-30A3B, with stable performance over 200-400 steps and consistent gains on JobBench and OfficeQA.
black-box rlharness executionprefix treesmix-harness trainingppo
UniDot: A Unified Network for Sequence Modeling and Feature Interaction in Large-scale Recommendation
UniDot proposes a unified architecture for recommendation systems that jointly models feature interactions and sequential behaviors through a shared dot-product primitive. The method tokenizes both static features and behavioral sequences into a common space, employing parallel token-mixing and sequence-retrieval buses with MLP-Mixer fusion, while an FM Highway propagates explicit dot-product interactions. Evaluated on TAAC KDD Cup 2026's Industrial track, UniDot achieved runner-up performance using dual sparse/dense optimization, an auxiliary delay head, and mutual learning.
factorization-machinetoken-mixingsequence-retrievalmlp-mixermulti-path mutual learning
Historical Backtesting for Scientific Question Discovery: A Protocol and Astronomy Pilot
The paper introduces historical backtesting as an objective protocol for evaluating scientific question generation systems, contrasting with subjective expert or LLM-based assessments. The method involves generating questions from a frozen historical corpus, then scoring them against future literature to measure foresight, premise validity, and impact. In an astronomy pilot (798 questions, 2010-2024), evidence-structure-first generation outperformed LLM-only prompting, with the former identifying refuted premises across eras. A seven-rater study (90 items) revealed low human-human agreement (kappa=0.17) and inflated LLM-judge reliability when measured via model-model agreement (kappa=0.60).
historical backtestingquestion generationtemporal evaluationllm-as-judgeevidence-structure
Neurosymbolic Embodied Agents
The authors present a neurosymbolic agent that decomposes long-horizon household tasks into visual exploration and constrained symbolic planning to ensure executability. The agent first employs a vision-language model and exploration harness to acquire grounded predicates and instance bindings, constructing a symbolic initial state. It then uses a PDDL transition model to restrict action decoding and Monte Carlo tree search with a domain-independent heuristic to evaluate executable continuations. On VirtualHome and ALFWorld benchmarks, the agent achieves over 90% success, outperforming a 27B direct visual policy. Constraints and search prove complementary, solving over 95% of ALFWorld tasks when combined, while reducing token generation and image usage compared to direct interaction methods.
neurosymbolicpddlmonte carlo tree searchvision-language modelexecutability
Steering the Flow: Inverting Face Recognition Models via Gradient-Guided Flow Matching
The paper proposes Steering Flow Model Inversion (SFMI), a two-stage white-box model inversion attack method for face recognition systems. SFMI first pre-trains an unconditional Flow Matching model to capture facial manifolds, then employs a Progressive Guidance Scheduler (PGS) to inject target-specific gradients during sampling, steering generation toward high-density regions of the target identity. Evaluated on CelebA with ArcFace as target, SFMI achieves 0.9248 accuracy, 22.61 FID, and 0.3874 LPIPS, demonstrating state-of-the-art performance in attack success and visual fidelity across multiple models.
model inversion attackflow matchingprogressive guidance schedulerface recognitiongradient steering
GRIP: Grounded Reasoning via Information-Restricted Premises
The paper introduces GRIP (Grounded Reasoning via Information-Restricted Premises), a method to mitigate query dominance in retrieval-augmented generation (RAG) by imposing capacity asymmetry. GRIP restricts retrieved evidence through a stochastic bottleneck while allowing full-dimensional query access, forcing the evidence channel to encode only residual information. Evaluated on five reasoning benchmarks, GRIP reduces query-latent mutual information by 30× (14.8 → 0.47 bits), cuts hallucination by 73%, and outperforms iterative baselines. Residual-alignment analysis confirms bottleneck outputs occupy less query-aligned subspaces than baseline representations.
retrieval-augmented generationquery dominancestochastic bottleneckmutual informationresidual alignment
Topological Attribution Distance (TAD): Revealing Segment-Level RAG Influence on LLM Output Geometry for Incident Log Analysis
The paper introduces Topological Attribution Distance (TAD), a novel method for segment-level attribution analysis in Retrieval-Augmented Generation (RAG) systems applied to cybersecurity incident logs. TAD leverages topological principles to quantify how individual log segments geometrically influence LLM outputs in embedding space, enabling precise evidence verification. The approach uses segment-level ablation to identify critical log sources that significantly alter response geometry, demonstrated through analysis of real-world cyberattack logs. Results show TAD provides explainable tracing of LLM decisions by analyzing hidden state dynamics.
topological attribution distanceretrieval-augmented generationsegment-level ablationevidence verificationhidden state analysis
TRACE-Bench: Decomposing and Diagnosing Multi-Reference Image Generation
The authors introduce TRACE-Bench, a diagnostic benchmark for multi-reference image generation that decomposes tasks into four atomic operators (Anchor, Disentangle, Apply, Compose) with quantified structural complexity. Using 1,600 evaluation cases derived from 631 formula templates and 4,000 reference images, the benchmark enables operator-aligned scoring and recursive failure analysis. Evaluation of 9 models reveals key bottlenecks in disentanglement (g) and attribute binding (⊕), with top models scoring only 0.74 on attribute fidelity despite relative strength in scene composition (C).
multi-reference image generationdiagnostic benchmarkatomic operatorsattribute bindingdisentanglement
LAVA: Logic-Aware Validation and Augmentation Framework for Large-Scale Financial Document Auditing
LAVA (Logic-Aware Validation and Augmentation) is a multimodal LLM-based pipeline for financial document auditing, addressing layout heterogeneity, semantic complexity, and embedded business rules. The framework employs a four-stage process: document-rule retrieval, layout-preserving extraction, metadata enrichment, and symbolic/arithmetic verification, ensuring auditable execution. Evaluated on a real-world financial benchmark, LAVA outperforms baselines in hallucination control and edge-case handling while optimizing token efficiency for high-volume validation.
multimodal llmfinancial auditingrule groundingerror attributionsymbolic verification
Would this change your answer? Evaluating Explanations of LLM Behavior In The Wild with Counterfactual Experiments
The paper introduces CHIVE (Counterfactual Hypothesis Investigation Via Edits), an agentic pipeline for evaluating explanations of LLM behaviors through counterfactual simulatability. CHIVE automatically identifies unexpected model behaviors and tests explanations via counterfactual prompt edits, generating thousands of explanations with supporting evidence. Results show no improvement in counterfactual prediction from common interpretability techniques, but training models on CHIVE-generated counterfactuals improves generalization to out-of-distribution settings.
counterfactual simulatabilityllm interpretabilityagentic pipelinechain of thoughtout-of-distribution generalization
TDD-Agent: Test-Driven Reasoning for Code Generation
TDD-Agent introduces test-driven development for LLM-based code generation, first generating executable tests to clarify expected behaviors before implementation, then refining both code and tests via execution feedback. The method employs iterative dual-track refinement, isolating test-first reasoning effects (TDD-prompt) on LiveCodeBench and evaluating the full framework on RepoEval. Results show consistent improvements over baselines, with iterative refinement enhancing both code correctness (higher pass rates) and test quality (coverage, mutation scores), demonstrating tests as evolving reasoning artifacts.
test-driven developmentcode generationiterative refinementexecution feedbackrepository-level benchmark
GoalEvolve: From Handcrafted Algorithm Priors to Goal-Driven Evolution of Physical Design Algorithms
GoalEvolve introduces a goal-driven framework for evolving physical design algorithms by directly optimizing final quality-of-results (QoR) in multi-stage flows. The method converts unmet QoR targets into normalized gaps, identifies dominant bottlenecks via stage-resolved checkpoints, and employs an LLM-based Teacher with parallel Student agents for hypothesis testing. Evaluated on ASAP7 designs, it improves post-route TNS by 30.67%, reduces leakage power by 21.18%, and closes 62.20% of commercial-tool power gaps, outperforming OpenROAD and Codex goal modes.
physical designmulti-stage optimizationquality-of-resultsalgorithm evolutionllm-guided search
Unsupervised Anomaly Detection for Image Dataset Quality Assurance in Multi-Center Breast MRI
The study proposes an unsupervised anomaly detection framework for quality assurance in multi-center breast MRI datasets, addressing 17 anomaly types across six public datasets. Methods include a projection-based approach with domain-specific features and positional encoding, a 3D reconstruction-based method, and two hybrid OOD detection techniques. Results show reliable detection of medium-far- and far-OOD samples (AUROC up to 0.954), with near-OOD and external normal data revealing method-specific limitations; implants and mastectomies remain challenging.
unsupervised anomaly detectionout-of-distribution detectionmedical aibreast mriquality assurance
MIRROR: Multimodal Intelligent Radiology Reasoning and Observation Reporter
MIRROR introduces a multimodal radiology reporting system that decouples classification from text generation to ensure auditable findings. The framework chains a multi-label classifier, Grad-CAM-based anatomical localizer, and a report writer constrained to classifier outputs, preventing spurious claims. Evaluated on ChestMNIST, the classifier achieves macro AUROC 0.729 with 1.6-6.8× precision over random, but exhibits high false-negative rates (11/14 labels unpredicted at threshold 0.5). Results highlight how class imbalance inflates aggregate metrics, with a naive baseline (Brier score 0.047) nearly matching the model (0.045).
multimodal reasoninggrad-cammulti-label classificationradiology reportingclass imbalance
Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors
The paper introduces semantic bandits, a multi-armed bandit variant incorporating textual action labels, to study how LLMs' semantic priors affect exploration-exploitation tradeoffs. Through controlled experiments, the authors demonstrate that semantically informative labels bias LLMs toward exploitation when aligned with rewards, but severely impair performance when misaligned. Results reveal asymmetric exploration responses to negative vs. positive rewards, attributed to pre-training data conventions. The work highlights inherent reliability challenges in LLM-based decision-making due to linguistically-induced biases.
semantic banditsexploration-exploitationsemantic priorsmulti-armed banditllm agents
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
The paper introduces Learning-to-Unlearn (L2UL), the first learning-based model-agnostic approach for machine unlearning, addressing computational inefficiencies in existing methods. Instead of manually designing unlearning functions, L2UL learns unlearning behaviors from a distribution perspective, yielding a simpler and more efficient unlearning process. Experiments show L2UL achieves accuracy comparable to retraining while significantly improving efficiency, particularly in data-intensive scenarios, with validation on ResNet demonstrating scalability.
machine unlearninglearning to optimizemodel-agnosticdistribution perspectiveresnet
FabriMAE I Trust Myself? Self-Evaluating VLA Action Generation with Markov Attention Entropy
The paper introduces MAE (Markov Attention Entropy), a self-evaluation framework for Vision-Language-Action (VLA) models that converts internal attention signals into architecture-aware reliability scores without external supervision. The method formulates action generation as a Conditional Generative Markov Chain, leveraging consistent entropy patterns in visual modalities across heterogeneous VLAs. Experiments on the LIBERO-Reflect benchmark (4,000 episodes) show MAE outperforms baselines in AUPR, AUROC, and FPR@95 metrics, while FabriMAE instantiation demonstrates improved robustness in test-time action selection with minimal runtime overhead.
markov attention entropyvision-language-action modelsconditional generative markov chainself-evaluation frameworklibero-reflect benchmark
UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures
UniTAC proposes a universal task-aware image compression framework that dynamically adapts to varying downstream tasks without retraining. The method abstracts tasks as per-component importance vectors derived from gradient attribution, transmitted as side information to condition a Vision Transformer (ViT) codec. Trained over a randomized family of importance vectors, UniTAC maintains a fixed backbone while steering reconstruction fidelity via injected vectors. Theoretical analysis links weighted distortion measures to task sensitivity. At 0.034 bpp, UniTAC achieves 91.4% accuracy on a localized task, close to task-specific codecs (93.3%) and outperforming universal codecs (76.9%).
task-aware compressionweighted distortionvision transformergradient attributionrate-distortion
Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank
The paper proposes UFFM, a semi-supervised semantic segmentation method for remote sensing that bridges gaps between labeled and unlabeled data via two innovations: (1) Unified Flow (UF) integrates a visual foundation model with a domain-specific teacher to generate less biased pseudo-labels under a joint training objective, and (2) Feature Memory Bank (FMB) dynamically updates class-specific features to align representations across data types. Experiments on RS datasets show UFFM outperforms SOTA methods, demonstrating effectiveness in reducing optimization and feature discrepancies.
semi-supervised learningsemantic segmentationpseudo-labelingfeature alignmentremote sensing
Chronocooked: A Benchmark for Implicit Interval Timing in Reinforcement Learning Agents
The paper introduces Chronocooked, a reinforcement learning benchmark suite for evaluating implicit interval timing in RL agents. The environment, inspired by Overcooked, features cooking scenarios requiring temporal decision-making with unobserved but critical timing information. Baseline evaluations compare non-recurrent, recurrent, and biologically plausible models, highlighting timing limitations and advocating for improved temporal processing in human-interactive agents.
reinforcement learningimplicit interval timingbenchmark suitetemporal decision-makingbiologically plausible models
Bounded Semantic Planning and Deterministic Compilation for Reliable Enterprise Text-to-SQL
The paper proposes semantic path compilation (SPC), a bounded semantic planning approach for reliable enterprise text-to-SQL that separates stochastic interpretation from deterministic query construction. SPC uses multi-turn planning to ground phrases in governed semantic artifacts, followed by deterministic graph traversal, role predicates, and SQL compilation. Evaluated on the ACME insurance benchmark (38 questions, 3 runs each), SPC achieved 97.4% correct runs versus 55.3% for direct DDL-to-SQL generation, with significant McNemar test results (p=3.05x10^-5) and zero wrong-but-executed queries. Results remained robust across GPT-5.4 and Gemini-3.6-Flash variants.
text-to-sqlsemantic planningdeterministic compilationenterprise schemasgoverned artifacts
Hoeffding adaptive splitting trees for data stream classification with concept drift and ensemble learning
The paper introduces Hoeffding Adaptive Splitting Trees (HASTs), a novel decision tree model combining periodic Hoeffding-bound splits with adaptive splitting triggered by change detection. This hybrid approach addresses limitations of purely adaptive trees in ensemble learning, where change detectors often fail to induce sufficient diversity. Evaluations demonstrate HASTs improve ensemble performance, achieving state-of-the-art results in data stream classification with concept drift while maintaining computational efficiency.
hoeffding treesconcept driftensemble learningchange detectiondata stream classification
X$^2$Localizer: Cross-grained Alignment for Progressive Cross-view Video Geo-localization
The paper introduces X$^2$Localizer, a cross-grained alignment framework for Progressive Cross-view Video Geo-localization (PCVG), addressing limitations of fixed-length inputs in conventional CVG. The method combines global prefix-to-aerial retrieval with token-aggregated frame--aerial-tile matching, supervised by a budget-dependent asymmetric objective, and employs Sliding-Window Re-Localization (SWRL) for dynamic candidate refresh. Experiments show X$^2$Localizer maintains full-video performance (+0.1 Recall@1, +0.3 Recall@10) while improving early localization (+4.7 Recall@1, +11.5 Recall@10 in single-frame settings) and enabling robust progressive localization under random-start and long-distance scenarios.
cross-view geo-localizationprogressive localizationcross-grained alignmentsliding-window re-localizationasymmetric objective
Orbit-Planner: Towards Latent World Models for On-Orbit Obstacle Avoidance of Satellite Agents
Orbit-Planner introduces a two-stage latent world model for on-orbit obstacle avoidance in satellite agents, addressing limitations of conventional planners that rely on predefined maps. The method combines action-conditioned spacecraft dynamics for latent-space future-state rollouts with a Physics Probe to decode physical states from imagined trajectories. Evaluated in Isaac Sim, it achieves 91.7% success rate in closed-loop obstacle-avoidance navigation, demonstrating effective long-horizon latent rollouts and physical state recovery.
latent world modelobstacle avoidanceon-orbit navigationphysics probeisaac sim
Reconstruction: A Blind Benchmark for Recovering Research Ideas from Pre-Publication Bibliographies
The paper introduces Reconstruction, a blind benchmark for evaluating language models' ability to recover research ideas from pre-publication bibliographies. The method employs a strict anti-leakage protocol with temporal citation cutoffs, anonymous reference IDs, and frozen bibliographies, using an independent LLM judge to match hypotheses against ground-truth ideas. Evaluated across 643 papers in six domains, frontier models achieved 3-15% Match rates, while a multi-agent pipeline with cross-model review and Swiss tournament selection improved performance to 23-42%, a 2.4x lift over single-model baselines.
reconstruction benchmarkidea recoveryanti-leakage protocolmulti-agent pipelineswiss tournament
Toward Better Assessment of LLMs' Performance in Clinical Error Detection
The study demonstrates that standard aggregate metrics (e.g., F1) inadequately assess LLMs' clinical error-detection performance due to their failure to exploit paired note structures. Evaluating 15 LLMs across 4 clinical test sets in 3 languages, the authors found 13 models performed below random pairwise discrimination despite moderate F1 scores, with language-dependent bias patterns. A novel evidence-scoring procedure revealed models locate error-relevant content but fail to correctly classify clean counterparts. The work advocates for paired evaluations alongside aggregate metrics in clinical NLP benchmarks.
clinical error detectionlarge language modelspairwise discriminationaggregate metricsevidence scoring
PDDLCoder: Agentic PDDL Generation for LLM-Assisted Symbolic Planning
The paper introduces PDDLCoder, an agentic framework for generating Planning Domain Definition Language (PDDL) from natural language to improve LLM-assisted symbolic planning. The method iteratively generates, analyzes, and refines PDDL specifications, addressing limitations of rigid pipelines and partial definitions. It also presents NL-pddlgym, a benchmark with 711 problems across 23 domains for automated verification. Experiments on 106 held-out problems show PDDLCoder achieves 89.6% plan applicability, outperforming prior methods (45.3%) and direct LLM planning (74.5%).
pddl generationsymbolic planningagentic frameworkautomated verificationllm-assisted planning
Hypergraph-based Multimodal Retrieval-Augmented Generation with Incremental Refinement
Hyper-M2RAG introduces a hypergraph-based framework for Multimodal Retrieval-Augmented Generation (M-RAG) that captures high-order correlations among heterogeneous entities via Multimodal Hypergraph Representation Learning. It replaces binary connectivity with hyperedges as unified semantic containers for text, images, and tables. An Anchor-driven Incremental Refinement mechanism mitigates semantic fragmentation by reconstructing local hyper-topology around boundary-crossing nodes, reducing computational redundancy. Evaluations on multimodal benchmarks show superior retrieval precision and generation coherence compared to state-of-the-art methods.
multimodal retrieval-augmented generationhypergraph representation learninganchor-driven incremental refinementhigh-order correlationssemantic fragmentation
When Do Explanations Help In-Context Learning? A Comparative Study of Natural Language Explanation Types and Faithfulness
This paper evaluates how different natural language explanation (NLE) types affect in-context learning performance across six benchmarks and four instruction-tuned models. The study compares NLE sources (human-written, self-generated, or LLM-generated) and selection strategies (random vs. faithfulness-based filtering) in few-shot prompting. Results show LLM-generated NLEs often match human rationales in classification tasks, while self-NLEs are more selection-sensitive; math reasoning exhibits model-dependent effects. Faithfulness-based selection yields marginal gains, with metric disagreements affecting utility. Robustness tests suggest semantic alignment contributes to performance.
in-context learningnatural language explanationsfew-shot promptingfaithfulness metricsinstruction-tuned models
A Shop Floor Production Scheduling Case based on RFID-supported Smart Factory
The paper presents a dynamic shop floor scheduling framework for RFID-equipped smart factories, addressing uncertainty in operation and production processes. It combines feasible production sequence mining and real-time processing rate estimation on RFID-collected data with a deep reinforcement learning approach for scheduling. Simulation studies using real-life case data show the framework outperforms FIFO, LIFO, and DQN methods in minimizing operation makespan.
rfidsmart factoryproduction schedulingdeep reinforcement learningmakespan
HarmTrace: Anchor-Calibrated Decoupled Optimization for Fine-Grained Target Identification in Harmful Memes
The paper introduces HarmTrace, a framework for fine-grained target identification in harmful memes, extending binary harmfulness classification to predict target categories, entities, and visual-textual evidence. It proposes Meme3W, a unified dataset with human-verified annotations, and Joint Record Accuracy (JRA), a strict evaluation metric. HarmTrace employs entity-aware supervised fine-tuning and Conditional Target-identification Policy Optimization (CTPO) with a Virtual Positive Anchor (VPA) to decouple harmfulness and target-identification optimization. Experiments show HarmTrace improves JRA from 17.58% to 52.51% on Qwen3-VL-8B while boosting harmfulness accuracy.
harmful memestarget identificationmultimodal classificationconditional policy optimizationvirtual positive anchor
Cost Scales with Change, Not Corpus Size: Incrementally Maintaining an Evolving Semantic Substrate
The paper demonstrates that incremental maintenance of semantic substrates scales with change magnitude rather than corpus size, challenging the assumption that full recomputation is necessary. It proposes incremental low-rank updates via truncated SVD and orthogonal Procrustes virtual axis updates for embedding model changes. Empirical results show 33.7x cheaper per-update costs versus full re-SVD, with negligible accuracy loss (recall@10 = 1.0, principal-angle drift <1e-11°), and 0.95 mean cosine similarity achieved by re-embedding only 10% of documents.
semantic substrateincremental maintenancetruncated svdorthogonal procrustesretrieval-augmented
Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning
Palmyra x6 introduces an enterprise-oriented agentic language model optimized for tool-use tasks through Anchored Supervised Fine-Tuning. The method post-trains a Mixture-of-Experts base model using 626 synthetic tool-use trajectories, employing a Muon + Adam hybrid optimizer with a single epoch, low learning rate, and KL anchor to the frozen base. The model achieves a BFCL Core score of 0.785, the highest six-benchmark mean among comparators, and demonstrates competitive performance in bias and safety evaluations, outperforming previous Writer Agent models.
anchored supervised fine-tuningmixture-of-expertstool-use trajectorieskl anchormuon + adam hybrid
Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity
The work proposes a degradation-aligned self-supervised learning (SSL) framework for state of health (SOH) estimation in lithium-ion batteries under label sparsity. The method employs a CNN-GRU model pretrained via a cycle-order ranking pretext task to learn aging-consistent representations from unlabeled data, followed by fine-tuning on sparse labels. Results demonstrate robust SOH estimation with only 1% labeled data, achieving 1.718% MAE and 2.329% RMSE on test cells, while analyzing label distribution effects on degradation data.
self-supervised learningstate of health estimationlithium-ion batterieslabel sparsitycnn-gru
CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction
The paper introduces CACSurv, a Concordance-Aligned Comparative learning framework for cancer survival prediction using large language models (LLMs) on patient reports. The method addresses formulation and supervision mismatches in time regression by reformulating survival modeling as mini-cohort comparative reasoning, employing concordance-aligned rewards for ranking supervision under right censoring. Evaluated on TCGA-SurvReport across six cancer cohorts, CACSurv achieves a 0.722 average C-index, outperforming the strongest baseline by 6.5 percentage points.
survival predictionlarge language modelsconcordance-aligned learningright censoringtcga-survreport
Physics of Agents: Statistical Mechanics Predicts Collective Behavior of AI Agents
The paper introduces a statistical-mechanics framework to model collective dynamics in multi-agent systems, analyzing over 10,000 communities of language-model agents interacting on objective and subjective tasks. Agents exhibit three regimes (indifference, polarization, consensus), with communication improving accuracy on objective questions but shifting subjective opinions rightward. The model predicts individual trajectories and group archetypes using parameters like social temperature and tie strength, revealing truth-seeking mechanics through attractive interactions and correct-answer pull.
statistical mechanicsmulti-agent systemscollective dynamicslanguage-model agentssocial temperature
Probabilistic Circuits as Reasoning Machines in Artificial Intelligence (Part I)
The thesis establishes probabilistic circuits (PCs) as a tractable framework for reasoning under uncertainty in AI, addressing computational challenges of probabilistic inference through structural constraints enabling polynomial-time exact computation of marginals, conditionals, and other queries. It synthesizes foundational theory, Bayesian learning methods, scalable implementations, and hybrid models integrating PCs with deep learning and symbolic paradigms. Key contributions include theoretical foundations, algorithmic developments, and empirical validation over a decade of research.
probabilistic circuitstractable inferencebayesian learninghybrid modelssymbolic machine learning
CUBICS: Situation-aware performance estimation for safety-relevant ML components
CUBICS introduces a context-modular framework for situation-aware performance estimation of safety-relevant ML components, addressing limitations of traditional Bernoulli-based failure models. The method partitions the operational design domain into situations, applies Subjective Logic for Bayesian updates of situation-specific probabilistic guarantees, and combines these with situational occurrence beliefs to derive component-level risk estimates. This enables modular safety assurance without requiring monolithic system-level statistical models.
safety assurancesubjective logicoperational design domainbayesian estimationcontext-modular
DeepInsight II: One Trace from Benchmark to Robot
DeepInsight II extends the evaluation framework from DeepInsight v1 to quantify embodied AI performance across navigation, manipulation, and whole-body control. It reproduces benchmark results under native protocols, introduces MotionBench for unified controller evaluation, and demonstrates sim-to-real trace continuity with shared parent traces. The study also proposes evidence-grounded handoff labels for System 2--1--0 interactions, mapping each to repairable actions, validated through physical execution. Results show empirical continuity from benchmarks to robot deployment and repair-oriented diagnosis.
embodied aisim-to-realwhole-body controltrace continuityrepair-oriented diagnosis
VCE-Skill: Enhancing Skill Self-Evolution with Version-Change Experience
VCE-Skill enhances skill self-evolution by leveraging version-change experience from public skill histories alongside execution trajectories. The method distills noisy public skill changes into reusable, structured priors and fuses them adaptively with trajectory-derived proposals from a base evolver, balancing external knowledge with task-specific evidence. Experiments show improvements of 3.20--4.98 points in mean scores, with resulting skills demonstrating stronger cross-model transfer performance. This work identifies public skill version changes as a valuable but underexplored source of prior knowledge for skill evolution.
skill self-evolutionversion-change experienceexecution trajectoriescross-model transferevolution priors
Listen, Reason, and Segment: Aligning LALMs with Editorial Judgment for Media Chapterization
The paper introduces AudioChaps, a post-training framework for aligning Large Audio Language Models (LALMs) with editorial judgment for audio chapterization, segmenting continuous audio into thematically coherent chapters. The method employs Group Relative Policy Optimization (GRPO) guided by Chain-of-Thought (CoT) reasoning, trained on curated datasets (AudioChaps-Alignment, AudioChaps-CoT, AudioChaps-Eval). Without Supervised Fine-Tuning, AudioChaps-R1-Zero improves average F1 by 33 points over Audio-Flamingo-3-Think; the final model, AudioChaps-R1, achieves a 49-point F1 improvement, demonstrating reliable transformation of auditory streams into structured media.
large audio language modelsaudio chapterizationgroup relative policy optimizationchain-of-thought reasoningeditorial judgment
When Context Misleads: Intent-Guided Decoding for Robust Retrieval-Augmented Generation
We propose Intent-Guided Decoding (IGD), a framework for retrieval-augmented generation (RAG) that dynamically arbitrates between retrieved context and parametric memory based on user intent. IGD employs answer-level filtering and token-level correction to steer the decoding trajectory, addressing the source trust problem where retrieved context may be misleading or irrelevant. Evaluated on three faithful QA benchmarks and three factual-conflict benchmarks across five LLMs, IGD achieves up to 65.4 percentage point improvements in factual recovery over Direct RAG while maintaining strict context-following behavior. This demonstrates the importance of balancing factuality and faithfulness in RAG systems.
retrieval-augmented generationintent-guided decodingparametric memoryfactual recoverycontext-following
Matched Outcomes, Divergent Gaze: How Foveated MLLMs Search Compared to Humans
This study investigates whether multimodal large language models (MLLMs) exhibit human-like visual search behavior by comparing their scanpaths to human eye movements on the COCO-Search18 dataset. Three general-purpose MLLMs were evaluated fixation-by-fixation under human-matched foveated input, assessing target detection, acquisition efficiency, and gaze dynamics. While MLLMs matched or exceeded human performance in target detection and acquisition, their scanpaths diverged significantly, exhibiting low-entropy, large-amplitude, and self-consistent patterns inconsistent with human serial search. The findings highlight a process-level gap in gaze dynamics that current metrics fail to capture, suggesting MLLMs are suitable for outcome-based tasks but not temporal, process-level modeling of human vision.
multimodal large language modelsfoveated inputscanpathsvisual searcheye-movement
MLLM-Guided Semantic Correction for Text-to-Video Generation
The paper introduces a training-free framework for semantic correction in text-to-video generation, addressing common errors like missing objects or mismatched actions. The method integrates multimodal large language model (MLLM) feedback during diffusion sampling, featuring a Semantic Assessment Supervisor for intermediate frame evaluation and a Semantic Modification Assistant for trajectory intervention. Experiments demonstrate improved semantic alignment, visual fidelity, and temporal consistency across multiple benchmarks without parameter updates.
text-to-video generationdiffusion modelssemantic correctionmultimodal large language modellatent trajectory intervention
Large language models as synthetic clinical experts to inform longitudinal rare-disease modeling
The study proposes integrating large language models (LLMs) as synthetic clinical experts to enhance longitudinal rare-disease modeling. A variational autoencoder (VAE) is trained with a differentiable surrogate model on LLM-derived clinical judgments, augmenting the loss function to preserve clinical-label distributions in reconstructions. Applied to motor-function assessments in children with spinal muscular atrophy, the method reduces SMA type label disagreement from 11% to 7% and improves motor function milestone prediction compared to unsupervised and data-level baselines. This approach demonstrates that LLMs can effectively incorporate clinical knowledge into representation learning, improving clinical faithfulness for rare-disease data.
large language modelsvariational autoencoderlongitudinal modelingrare-diseaseclinical faithfulness
NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation
NebulaVLA introduces an asynchronous dual-frequency Vision-Language-Action (VLA) model for robotic manipulation, decoupling high-level semantic reasoning from low-level action control to optimize efficiency and modularity. The method incorporates GESTURE-7, a unified language-grounded action representation for cross-embodiment generalization, and Guide Action, an algorithm enforcing kinematic continuity via mask-based smoothness constraints. Evaluations show an 85.5% average success rate on LIBERO-Plus and ~2.7× faster action generation compared to synchronous baselines.
vision-language-actionasynchronous architecturecross-embodiment generalizationkinematic continuityguide action
Graph Machine Learning: An Opportunity for Power Systems
The survey analyzes 800 papers on graph machine learning (GML) applications in power systems, highlighting their potential for addressing operational challenges like renewable integration and real-time decision-making. GML methods leverage grid topology as inductive bias for tasks including forecasting, state estimation, and cybersecurity, offering scalable approximations with computational efficiency. Key challenges include limited real-world deployment, interpretability needs, and a lack of standardized benchmarks, prompting a proposed requirements catalog for ML-ready power grid datasets to improve reproducibility.
graph machine learningpower systemsinductive biasstate estimationbenchmark reproducibility
Offline Reinforcement Learning for Hemodynamic Management of Sepsis in the ICU: a MIMIC-IV Study with Dual Off-Policy Evaluation
This work introduces a transparent validation framework for offline reinforcement learning (RL) in sepsis hemodynamic management, combining off-policy estimation, reliability diagnostics, and clinician-agreement analyses. Using MIMIC-IV data (36,872 ICU stays), the authors model fluid and vasopressor dosing as a discretized Markov decision process (1,000 states, 25 actions) solved via policy iteration. Dual off-policy evaluation (weighted importance sampling and fitted Q evaluation) shows the learned policy outperforms clinicians (WIS 50.8, FQE 46.8 vs. 38.2) while maintaining clinical plausibility (total variation 0.18), favoring less intravenous fluid.
offline reinforcement learningoff-policy evaluationmarkov decision processimportance samplingclinical decision-support
RISE: Roadside Infrastructure Sequence Understanding across 3D Tracking and Structured Vision-Language Reasoning
The RISE framework introduces a unified approach for roadside infrastructure understanding, combining 3D tracking and vision-language reasoning. For tracking, it uses SAM3 video identities with calibration-guided mask agreement for multi-view association, achieving 66.9 MOTA on 20 clips without LiDAR or layout-specific training. The vision-language component employs an MLLM pipeline to create RISE-VQA (33,910 QA pairs from 557 clips), evaluated via intersection-held-out RISE-Bench. Results highlight benefits from domain adaptation and temporal context, while identifying challenges in spatial grounding and interaction reasoning.
3d trackingvision-language reasoningmulti-view associationdomain adaptationspatial grounding
A Two-Stage Learning PINN Approach for Solving the Inverse Problem of the 1D Porous Medium Equation
The authors propose a two-stage Physics-Informed Neural Network (PINN) approach to improve convergence stability in solving the inverse problem of the 1D Porous Medium Equation (PME). The method addresses sensitivity to initial guesses in standard PINN formulations by separating parameter estimation and solution refinement stages. Results demonstrate reliable recovery of unknown parameters (diffusion exponent m) even with poor initial guesses, outperforming classical methods in robustness while maintaining accuracy comparable to analytical solutions.
physics-informed neural networksporous medium equationinverse problemsnonlinear pdeparameter estimation
A Regulatory Placebo? The Systemic Failure of Mandatory GenAI Labeling
The study critiques mandatory generative AI (GenAI) labeling as ineffective symbolic legislation driven by technological panic. Through systematic analysis of three regulatory theories (value dilution, information authenticity, and proactive regulation), it reveals their inadequacy due to regulators' limited understanding of modern AI. Findings indicate that such labeling creates implementation challenges, obstructs AI evolution, and masks genuine legal needs, advocating a shift from identity-based to content-focused governance.
generative airegulatory theorycontent governancesymbolic legislationtechnological panic
JailbreakSkill: Scaling Automated Red-Teaming with Reusable and Ever-Evolving Skills
The paper introduces JailbreakSkill, a skill-centric framework for scalable automated red-teaming that modularizes attack strategies into reusable, agent-ready skills. The framework enables adaptive skill selection across tasks and models while supporting continuous evolution through attack experience analysis, including skill refinement, combination, and discovery. Evaluations show macro-average attack success rate (ASR) improvements of 17.5pp on AdvBench and 13.4pp on HarmBench, with a 48.6pp gain against GPT-5.4 on AdvBench. Evolved skills demonstrate zero-shot generalization to unseen prompts and models.
automated red-teamingattack success rateskill-centric frameworkzero-shot generalizationadvbench
A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes
The paper proposes a human-LLM teaming framework for privacy risk analysis in CBDC-based welfare schemes, addressing challenges like information synthesis and contextual evaluation. The framework integrates PRIAM methodology, where LLMs process documentary evidence to generate initial outputs, which human experts then refine through iterative feedback and judgment. Results demonstrate effective collaboration, with LLMs producing data categories and attributes, while humans identify gaps and ambiguities, enhancing assessment quality.
human-llm teamingprivacy risk assessmentcbdcpriamdata characterization
Contrastive Energy Fields for Inference-Time Procedure Planning in Instructional Videos
CEFITO introduces a contrastive energy field approach for inference-time procedure planning in instructional videos, explicitly modeling task constraints by omitting irrelevant actions during planning. The method learns an action-conditioned representation space and formulates planning as a constrained optimization problem, contrasting with feed-forward or diffusion-based approaches. CEFITO achieves state-of-the-art accuracy on two standard benchmarks by enforcing logical constraints over the action space.
procedure planningcontrastive learningenergy fieldsinstructional videosconstrained optimization
HaReCAP: Habitual-action Grounding for Recursive Large Language Model Agents
HaReCAP introduces habitual-action grounding to reduce last-mile grounding redundancy in recursive LLM agents, extending ReCAP's framework. The method compiles frequent leaf decisions from successful trajectories into one-step reflex rules, bypassing LLM calls when rules uniquely determine valid actions while maintaining ReCAP's recursive control flow. Evaluations on Robotouille and ALFWorld with Qwen3.5-27B show token reductions of 14.67-20.08% on solved tasks compared to ReCAP.
habitual-action groundinglast-mile redundancyrecursive context-managementleaf-reflex ruleslong-horizon tasks
Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics
This paper introduces DiffLTLf, a neurosymbolic framework for Linear Temporal Logic over finite traces (LTLf) that employs fuzzy semantics without automata-based representations. The authors formally define multiple fuzzy semantics for LTLf, analyze their theoretical properties, and integrate them into DiffLTLf for scalable learning. Experimental results demonstrate that semantic choice significantly affects performance, with DiffLTLf matching or exceeding state-of-the-art probabilistic methods while improving scalability on complex temporal reasoning tasks.
neurosymbolic learningltlffuzzy semanticstemporal logicdifferentiable reasoning
The Value of a Prompt: An LLM-Relative Kolmogorov-Complexity Approach
The paper proposes a method to quantify the value of prompts for LLMs by introducing an LLM-relative probabilistic Levin-Kolmogorov complexity measure, $pKt$, which treats the model's thinking as a random tape. This measure defines prompt value as algorithmic mutual information with respect to $pKt$, capturing how prompts make target artifacts easier to generate by increasing success probability or reducing computation. The authors show that this value is efficiently estimable and demonstrate that a prompt with $b$ bits of value reduces median token cost by a factor of $2^b$ in reproduction experiments.
probabilistic levin-kolmogorov complexityalgorithmic mutual informationllm-relative complexityprompt valuetoken cost
Drive, Pack, Fly: The Travelling Thief Problem with Drone
The paper introduces the Travelling Thief Problem with Drone (TTP-D), a novel combinatorial optimisation problem that jointly optimises item selection, vehicle routing, and drone-flight synchronisation to maximise profit net of time-based rental costs. The authors propose a mixed-integer linear program for small instances, metaheuristics for larger ones, and an attention-based Deep Reinforcement Learning (DRL) policy initialised with a hybrid solver combining DRL and annealing. Benchmarks show the hybrid approach achieves near-metaheuristic quality at reduced computational cost, though large instances still require full metaheuristic budgets. Sensitivity analysis identifies rental ratio as the primary profit driver.
travelling thief problemmixed-integer linear programdeep reinforcement learningmetaheuristicssensitivity analysis
Visualizing Uncertainty-to-Action Composition for Human Oversight
The paper introduces an uncertainty-to-action binding framework that composes multiple uncertainty conditions into a single oversight response, governed by a precedence policy with a contextual safety modifier. It presents ActionCue, a process-transparency visualization that renders this composition explicit, contrasting it with confidence-only and data-level uncertainty displays. The approach is demonstrated through case studies in healthcare, credit assessment, and disaster forecasting, showing how uncertainty conditions resolve into inspectable oversight responses rather than remaining implicit.
uncertainty visualizationhuman oversightprocess transparencydecision compositionsafety modifier
ParaTempo: Efficient Parallel Reasoning via Temporal Confidence
ParaTempo introduces temporal confidence, a training-free metric for efficient parallel reasoning that dynamically manages computation across branches. The framework asynchronously probes branches for answer-space convergence, pruning low-confidence paths, retiring stable branches early, and reallocating resources via forking. Evaluated on mathematical and scientific reasoning benchmarks, ParaTempo reduces latency by 21.8-32.2% and token usage by 18.1-30.3% while maintaining accuracy, with temporal confidence outperforming token-level signals in stability and predictive power.
parallel reasoningtemporal confidenceanswer-space convergencebranch pruningasynchronous control
Reasoning-supported Robustness Validation of Automotive E/E Components
The paper contributes an ontology-supported framework for automating Robustness Validation (RV) of automotive E/E components, addressing complexity through formalized knowledge representation. Methodologically, it encodes component characteristics and Mission Profiles (MPs) in OWL, enabling semantic querying for automated analysis selection and decision support. Experimental results from an automotive power electronics use-case demonstrate significant RV process improvements, with reduced design time and increased exhaustiveness through automated data provisioning.
robustness validationautomotive e/e componentsowl ontologymission profilessemantic querying
PertMind: Eliciting Emergent Biological Reasoning in LLM via Reinforcement Learning on Cellular Perturbation Data
PertMind introduces a reinforcement learning framework for eliciting biological reasoning in LLMs using cellular perturbation data as computable rewards. The method combines supervised initialization with multi-level reinforcement signals (gene, pathway, format) from perturbation-response prediction. Results show improved inference in unseen contexts (retaining general capabilities) and zero-shot transfer to reverse/double perturbations, phenotypic screening, and biological interpretation. The model also generates competitive multiscale representations for downstream tasks, demonstrating that experimental endpoints can concentrate pretrained biological strategies.
reinforcement learningcellular perturbationbiological reasoningmultiscale representationszero-shot transfer
Towards Risk-free AI Agent Deployment
The article proposes trajectory-based testing and debugging as a systematic approach for risk-free deployment of LLM-based agents, addressing security, compliance, and functionality concerns. It identifies key challenges including the oracle problem, non-determinism, and trajectory validation, while advocating for automated failure attribution and repair mechanisms. The work culminates in a deployment-readiness checklist and highlights open problems such as formal adequacy metrics and root-cause attribution in long-horizon trajectories.
llm-based agentstrajectory validationoracle problemfailure attributiondeployment-readiness
A Policy Algebra for Trust-Preserving Agentic AI Execution
The paper introduces a policy algebra for ensuring reliable capability in LLM-based agentic frameworks, where task completion must adhere to constraints on data access, authority delegation, and auditability. The proposed algebra composes security profiles and runtime obligations through joins, intersections, and budget narrowing, preserving trust while minimizing restrictions. Evaluations show a 94.8% intervention rate on policy violations with 86.9% task completion, eliminating profile-monotonicity and artifact-exhaustion violations while achieving 98.6% audit completeness.
policy algebraagentic aireliable capabilitytrust-preservingaudit constraints
Think Inside the Chunk: RegulaRAG for Regulation-Compliant Scenario Generation using LLMs: A Case Study of UN Regulation No. 152
RegulaRAG introduces a Retrieval-Augmented Generation pipeline for generating regulation-compliant automotive test scenarios, addressing LLMs' limitations in grounding outputs in hierarchical standards. The method combines SmartChunking with graph traversal for paragraph enrichment and Smart Retrieve & Rerank over enriched units. Evaluated on UN Regulation No. 152, RegulaRAG achieves an 82.99 Meta-Score, outperforming baselines by 43%, with stable performance under corpus scaling and 14k-25k token efficiency versus 500k for graph-centric approaches.
retrieval-augmented generationsmartchunkinggraph traversalregulation-compliantautomotive testing
Ventor-QTest: Threat-Model-Driven Verification of Vendor-Hosted LLM APIs
The paper introduces Ventor-QTest, a black-box audit method for verifying vendor-hosted LLM APIs without requiring probability information. The method combines repeated-request testing (measuring average fidelity loss, AFL) and long-sequence probing (measuring extreme fidelity loss, EFL) to evaluate API fidelity. Results show AFL correlates strongly with logprob-derived metrics, while EFL varies across routes and correlates with performance drops in long-horizon tasks like Terminal-Bench. The tool is open-sourced for auditing agentic tasks.
black-box auditaverage fidelity lossextreme fidelity losslong-horizon tasksvendor-hosted apis
Counting Documents Is Not Counting Text: Unit Bias in Web-PDF Corpus Statistics
The study exposes a unit bias in web-PDF corpus statistics, demonstrating that token-based and document-based metrics diverge significantly. Analyzing CC-MAIN-2021-31-PDF-UNTRUNCATED (7.9M PDFs, 32.6B tokens), the authors find extreme token concentration: 3.02% of documents contain 50% of tokens (Gini 0.807), while 5.00% of documents over 50 pages hold 53.53% of text. Common Crawl's truncation cap affects 23.06% of documents but 63.08% of text, with current recovery methods retrieving only 1.4-11.4% of lost content. The proposed 5 MiB cap would still truncate 30.19% of tokens. The paper advocates dual reporting in documents and tokens.
corpus statisticstoken distributiondocument truncationgini coefficienttext recovery
AstronOS: A Unified Execution Model and Runtime for Long-Horizon Agentic Systems
The paper introduces AstronOS, a unified execution model for long-horizon agentic systems that maintains persistent identity and versioned state across calls. The system implements Cases, Tasks, and Scenario Packs across central/local execution, comparing five state-handoff strategies (rereading, history replay, text/JSON summary, AstronOS-mediated). Evaluated on 150 executions (10 tasks × 5 strategies × 3 reps), AstronOS achieved 14/15 passing executions in the three-stage batch versus 0-2/15 for alternatives, with lower token costs but higher latency per attempt.
agentic systemsexecution modelversioned statescenario packsstate-handoff
Synthetic Data Augmentation for Satellite-Based Analysis of Battle-Damaged Agricultural Fields in Ukraine
This work demonstrates the utility of synthetic data augmentation for satellite-based classification of war-damaged agricultural fields in Ukraine, addressing data scarcity and class imbalance. Class-conditional Generative Adversarial Network (GAN) and Denoising Diffusion Probabilistic Model (DDPM) architectures were trained on real satellite imagery to generate additional bombed and not-bombed agricultural-field samples. A Vision Transformer classifier was evaluated on real test data across multiple augmentation configurations. Balanced DDPM augmentation yielded the best results, improving accuracy (84% to 88%), balanced accuracy (67% to 81%), macro F1 (65% to 78%), and recall for the not-bombed class (41% to 69%).
synthetic data augmentationsatellite imageryclass-conditional gandenoising diffusion probabilistic modelvision transformer
Adaptive Post-Processing Drives Instance-Level Detection in Stroke Lesion Segmentation
The paper introduces Volume-Conditioned Adaptive Post-Processing (VCAP), a method that dynamically adjusts component-size thresholds based on predicted lesion burden to improve instance-level detection in stroke lesion segmentation. VCAP boosts Lesion-F1 by 0.032 (6× larger than architectural changes) and small-lesion detection by 3.7%, despite no change in voxel-level Dice. Evaluated on ISLES'26 with 1,453 cases via 5-fold cross-validation, the post-processed ensemble achieves Dice 0.651 and Lesion-F1 0.614, outperforming the baseline (0.644, 0.573).
instance-level detectionadaptive post-processinglesion-f1voxel-overlapstroke lesion segmentation
Coverage-Maximizing Multinomial Subset Routing under Operational Constraints
The authors introduce Multinomial Subset Routing (MSR), an online routing framework over K experts that maintains a multinomial routing policy rather than deterministic subsets. At each round, M experts are sampled i.i.d. from this policy, forming the routed subset, with rewards dependent on the best-performing expert(s). The framework addresses long-term operational constraints under bandit feedback, observing only the winner's reward. They propose OMD-Approachability, combining online mirror descent with Blackwell's Approachability, proving O(1/√T) regret in both reward and constraint violation. Empirical validation on a real-world crowdsourcing dataset demonstrates practical applicability.
multinomial subset routingonline mirror descentblackwell's approachabilitybandit feedbackoperational constraints
OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations
The authors introduce OceanDepths, the first global AI-ready dataset pairing satellite-derived sea surface observations (SST, SSS, SSH) with co-located EN4 subsurface temperature/salinity profiles and GLORYS12 reanalysis data. The dataset spans 2000-2024 at 0.1°×0.1° spatial and weekly temporal resolution, containing 9.5 million profiles interpolated to 50 depth levels. With extreme subsurface sparsity (~0.01% per depth) and 4D multivariate structure, OceanDepths serves as a challenging benchmark for AI methods, demonstrated via subsurface reconstruction baselines while enabling forecast development.
oceanographymultivariate datasetsatellite observationssubsurface reconstructionspatiotemporal modeling
What Does Context Compression Cost an Agent? Interaction Costs Unrevealed by Task-Completion Metrics
This work demonstrates that context compression imposes hidden interaction costs on agents, unrevealed by task-completion metrics, due to increased state reacquisition. The authors introduce a runtime measurement protocol to quantify these costs in a bounded-horizon tool-using agent, varying compression severity and comparing dropping versus fact-preserving operators. Results show retrieval calls increase significantly across three models and two task regimes, with GPT-5.5 exhibiting a 204% rise in retrieval calls (p = .002) despite unchanged task completion (80% to 85%, p = 1.0). Retention interventions reveal that semantically irrelevant content increases retrieval by 57% (p < .001) without affecting completion. The reacquisition signature is shown to be environment-dependent, as ALFWorld exhibits no retrieval surge under sliding compression.
context compressioninteraction coststate reacquisitionretrieval callsbounded-horizon
MELD: A Protocol for Merging Knowledge Across Distributed Agentic Memories
MELD introduces a decentralized protocol for reconciling knowledge across autonomous agents' memories through a five-outcome procedure (insert, merge, relate, conflict, or reject) based on scoped claim-key identity, embedding similarity, and natural-language-inference. The system uses authenticated Patches as the sole state mutation mechanism and a CRDT for status coherence, enabling partition tolerance and protection against silent rewrites. Evaluations on HotpotQA show non-inferior recall to centralized stores (AUC 0.968, 0.013 false-merge rate), 3x fewer messages via semantic routing, and 100% partition-heal success versus 37% for last-writer-wins.
knowledge graphcrdtautonomous agentssemantic routingpartition tolerance
DriveCache: Action-Aware Caching for Driving World Model Inference
DriveCache introduces an action-aware caching mechanism for accelerating diffusion-based driving world model inference, leveraging pre-generation driving signals (ego speed, planned trajectories) to optimize feature reuse. The method employs a training-free controller that allocates cache reuse across scenes using planned motion and schedules it across denoising steps via dynamic programming under a calibrated response budget, with a causal drift check to refresh features when generation deviates. Evaluated across three generator configurations, DriveCache outperforms existing cache methods in balancing fidelity and efficiency. Code will be made publicly available.
diffusion modelsdriving world modelcache reusedynamic programmingcausal drift check
HalluTracer: Hallucination Detection via Depth-Averaging Truth Signals
HalluTracer introduces a depth-averaging framework for hallucination detection in large language models, aggregating truthfulness signals across all layers of the forward pass rather than isolating them to specific components. The method leverages geometric sparsity of weakly correlated per-layer signals, with depth averaging suppressing noise and capturing linearly accessible information. Evaluated on six models and five benchmarks, HalluTracer outperforms white-box baselines by 1-14 points, demonstrating the efficacy of depth-aggregation over layer-selection approaches.
hallucination detectiondepth-averagingtruthfulness signalsgeometric sparsitywhite-box detectors
AeroCopilotBench: A Two-Tier Benchmark for Evaluating LLM Agents as Aviation Copilots in an Interactive Virtual Cockpit Environment
The paper introduces AeroCopilotBench, a two-tier benchmark for evaluating LLM agents in aviation copilot roles, alongside the AeroCopilot Operational Environment (ACOE), an interactive virtual-cockpit testbed. Tier-1 assesses aviation knowledge via 1,200 multiple-choice questions, while Tier-2 evaluates procedural execution across 73 emergency tasks derived from Pilot's Operating Handbooks, implemented as state transitions and safety constraints in ACOE. Testing 12 models revealed a 72.6% peak Tier-2 success rate, with failures attributed to procedural completeness, state feedback utilization, and long-horizon execution. The framework enables safety-gated evaluation of task completion and trajectory safety.
llm agentsaviation copilotvirtual cockpitsafety constraintsprocedural execution
SIGMA-Lane: Scale-pyramId Gated MAmba for Temporally Consistent Video Lane Detection
SIGMA-Lane introduces occlusion-aware gating mechanisms for State Space Models (SSMs) to improve temporal consistency in video lane detection under heavy occlusion. The method employs dual gating on SSM write and residual-fusion paths to control observation updates, complemented by Structural Spatial Retrieval (SSR) for recovering lane structure from historical priors after affine alignment. Evaluations on VIL-100 and OpenLane-V demonstrate enhanced temporal stability while maintaining competitive F1 and mIoU metrics.
state space modeltemporal consistencyocclusion-aware gatingvideo lane detectionstructural spatial retrieval
Step-Level On-Policy Distillation: Interpolating Between On-Policy Distillation and Supervised Fine-Tuning
The paper introduces Step-Level On-Policy Distillation (SOPD), a method that interpolates between on-policy distillation (OPD) and supervised fine-tuning (SFT) by providing step-level supervision over student-generated trajectories. SOPD combines long-horizon corrections from SFT with on-policy advantages of OPD, addressing limitations of fragmented token-level guidance in OPD. Evaluated on reasoning and agent tasks (e.g., ALFWorld), SOPD outperforms SFT and OPD, achieving a 13.4-point improvement in average success rate over vanilla OPD.
on-policy distillationsupervised fine-tuningstep-level supervisionstudent-generated trajectorieslong-horizon correction
Revisiting the Performance of Generative Artificial Intelligence on Introductory Object-Oriented Programming Assessments: Insights from 2026
This study evaluates the performance of five GenAI systems—ChatGPT-5.2, DeepSeek-V3, Gemini 2.5 Flash, Claude Sonnet 4.5, and M365 Copilot—on authentic introductory object-oriented programming (OOP) assessments. Using programming tests and examination tasks from a university course, generated solutions were graded against student performance and analyzed for common errors. All systems outperformed the average student cohort, often achieving full marks on longer tasks, but exhibited recurring limitations, including non-compiling code and difficulties with advanced OOP concepts like interfaces, abstract classes, and inheritance. Graphics-related tasks also posed challenges. Compared to the previous year, noticeable improvements were observed, though persistent error patterns remained.
genaiobject-oriented programmingnon-compiling codeinterfacesinheritance
Deep Thought Alignment: Trajectory-Level Latent Distillation for Video Reasoning
The paper proposes Latent-OPD, a trajectory-level latent distillation method for video reasoning that augments On-Policy Distillation (OPD) by aligning hidden states at trajectory endpoints to capture accumulated visual evidence. The approach introduces progressive teacher-lookahead, matching middle-to-late student layers with deeper teacher layers. Evaluated on six benchmarks, Latent-OPD outperforms output-only OPD, particularly in limited-frame, long-video, or complex evidence-aggregation scenarios, demonstrating superior frame efficiency.
latent distillationvideo reasoningon-policy distillationtrajectory alignmentteacher-lookahead
Static Pruning Across Sparse Retrieval Regimes: What Transfers, What Breaks, and What Still Helps
This study conducts the first cross-engine evaluation of static pruning strategies for sparse neural retrieval, examining three engines (exhaustive inverted index, BMP, SEISMIC) across two benchmarks (MS MARCO, Natural Questions) with two encoders (SPLADE, V3-GTE) totaling 1,140 configurations. Index-side pruning proves universally portable, reducing latency (1.2-6.6×) and index size (18-82%) due to memory-bound retrieval, while query pruning is subsumed by dynamic mechanisms (BMP's β, SEISMIC's query_cut). Combining static and dynamic pruning yields 2.5× speedup with minimal NDCG@10 degradation (0.003). Recall@10 (85-95%) provides a reliable stopping criterion before ranking degradation occurs.
static pruningsparse retrievaldynamic pruninginverted indexmemory-bound
Audio-Visual Segmentation via Depth-Guided Collaborative Modeling
The paper proposes DGCM-AVS, a tri-modal framework for Audio-Visual Segmentation (AVS) that incorporates depth as a spatial structural cue to improve cross-modal alignment. The method introduces a Depth-Aware Dynamic Modulator for object separation and Depth-Guided Progressive Fusion to align audio-visual features. On the AVSS dataset, DGCM-AVS achieves relative improvements of 10.2% in M_J and 8.7% in M_F over state-of-the-art methods.
audio-visual segmentationdepth-aware modulationcross-modal alignmenttri-modal frameworkprogressive fusion
Decoupled Temporal Encoding for Generative Recommendation
Decoupled Temporal Encoding (DTE) introduces a lightweight framework for generative recommendation that separates temporal dynamics from order information. The method employs two modules: a personalized macro-temporal module injecting temporal primitives into item embeddings, and a time-gated micro-sequential module introducing relative-order bias for temporally dense interactions. DTE addresses limitations of existing approaches that conflate heterogeneous temporal signals through unified representations, particularly in systems with multi-level temporal regularities like recency effects and meal-time peaks. The framework is parameter-efficient and deployment-friendly, enabling seamless integration into existing recommendation systems.
temporal encodinggenerative recommendationmacro-temporal modulemicro-sequential modulerelative-order bias
Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic
Foresight-England (Foresight-E) introduces the first national-scale generative foundation model for electronic health records (EHRs), focusing on COVID-19 research. The 243-million-parameter transformer decoder processes longitudinal EHRs of 61 million individuals, integrating primary/secondary care, death registrations, and COVID-19 data with a vocabulary of ~40,000 clinical codes. It predicts medical events autoregressively in a zero-shot manner, retaining granularity of ICD-10, OPCS-4, and SNOMED CT codes. Evaluation planned for 30-day COVID-19 hospitalisation and mortality, including subgroup analyses, though quantitative results are currently unavailable due to paused data access.
electronic health recordstransformer decoderautoregressive predictionicd-10snomed ct
Defake-o3: From Speculative Rationales to Verifiable Evidence for Explainable AIGI Detection
Defake-o3 introduces an explainable AI-generated image (AIGI) detector that replaces speculative rationales with verifiable evidence through interactive visual search and verifier-guided evidence alignment. The method employs an Evidence Verifier trained on human annotations to reinforce grounded evidence, supported by the GroundFake dataset with localized bounding-box annotations and corrected reasoning trajectories. Evaluated on GroundFake, FakeFrontier, and out-of-distribution benchmarks, Defake-o3 improves detection accuracy and explanation quality, yielding more localized and persuasive evidence.
aigi detectionverifiable evidenceinteractive visual searchevidence verifiergroundfake dataset
CompoSkill: Compositional Skill Chain Attacks from Individually Scanner-Passing LLM Agent Skills
The paper introduces CompoSkill, a framework demonstrating how individually certified AI agent skills can form risky compositions when chained, despite passing per-skill safety scans. Using a dual-attacker system (white-box and black-box), the method constructs attack chains via explicit skill-id sequences or implicit lures in a Skill Composition Graph. Evaluation on CompoSkill-Bench (1,140 records across 5 threats) shows Chain Formation Rates up to 83.3% (white-box) and 80.6% (black-box), revealing limitations of single-skill certification. Results also identify a bridge-bonus-then-hop-decay pattern in attack success rates.
skill compositionautonomous agentssafety certificationlong-horizon tasksattack chains
Software Engineering for AI-driven Building Operation
The article identifies software engineering (SE) challenges specific to AI-driven building control systems, where failures have physical consequences unlike digital environments. Through interdisciplinary research in civil engineering and computer science, the authors analyze gaps in SE for AI (SE4AI) practices that hinder deployment of building optimization systems. They propose foundational principles for SE4AI in cyber-physical systems, emphasizing irreversible energy waste, comfort violations, and equipment wear as key failure modes requiring new engineering approaches.
ai-driven controlbuilding operationscyber-physical systemssoftware engineering for aipredictive control
A cross-modal generative model for incomplete and degraded prostate MRI with multicentre clinical validation
MSCNet, a sequence-conditioned cross-modal generative framework, reconstructs missing or degraded prostate MRI sequences and improves image quality. The model outperforms task-matched comparators with a mean structural similarity of 0.818 across ten completion tasks, excelling in lesion fidelity and boundary preservation. In a 1,000-case blinded reader study, MSCNet met non-inferiority criteria for DWI, ADC, and T2W completion but not T1W. Diagnostic assessment on 200 cases showed AUCs of 0.841 for clinically significant cancer, compared to 0.797 for baseline-generated images. Multicentre validation on a 186-case cohort confirmed transportability, supporting MSCNet as a quality-controlled adjunct to prostate MRI.
cross-modalstructural similaritylesion fidelitynon-inferioritymulticentre
STAIR: Semantic-Temporal Automaton for Interpretable Reasoning in Temporal Question Answering
STAIR introduces a semantic-temporal automaton for interpretable reasoning in temporal question answering, decoupling semantic interpretation from precise temporal inference. The system employs an answer-free LLM adapter to map complex questions to normalized temporal intents, while a deterministic temporal automaton executes policies over canonicalized evidence. STAIR follows a rule-first design, reducing free-form reasoning and enhancing verifiability. It achieves average F1 improvements of 16.57% and 3.10% on TimeQA and TempReason datasets using Qwen2.5-7B and GPT-4o-mini models, respectively, excelling in boundary-sensitive and order-sensitive queries through guarded execution and semantic adaptation.
semantic-temporal automatonllm adaptertemporal inferenceguarded executionsemantic adaptation
HiPHI: A Large-Scale Benchmark for High-Precision Human Motion and Object-Interaction
HiPHI introduces a 600+ hour high-fidelity dataset for whole-body human motion and object interaction, addressing limitations in existing datasets by combining precise physical states with broad behavioral coverage. The dataset leverages FrameNet, a linguistic framework, and employs optical motion capture to achieve sub-millimeter spatial marker tracking accuracy for full-body motion and mesh-level object trajectories. A benchmark suite evaluates motion-space diversity, interaction grounding, object consistency, and physical AI applications. Analyses show HiPHI significantly expands motion coverage while maintaining high-fidelity interaction quality, providing a scalable foundation for training and evaluating humanoid policies in real-world embodied tasks.
optical motion captureframenetmotion-space diversityinteraction groundinghumanoid policies
Process-Constituted Intelligence: A Shared Criterion for Humans and Machines
The paper introduces a process-based framework for evaluating intelligence equivalence between humans and machines, emphasizing iterative activity over output similarity. It critiques current generative AI (GenAI) for producing outputs that resemble human cognition while lacking the underlying processes. Seven process features are proposed to assess strong equivalence, addressing risks of outsourcing human generative capacities. Design principles for GenAI are specified to preserve human judgment and creativity, alongside process audits for testing equivalence. The approach aims to bridge cognitive science and AI design.
generative aicognitive processesstrong equivalenceprocess auditsdesign principles
BaT: Towards Self-Evolving Medical Research Agent with Stage Rubrics
The paper introduces Benchmark-as-Teacher (BaT), a self-evolving system for medical research agents that combines the Stage Bank data pipeline with BiCuRL, a bilevel curriculum reinforcement learning method. Stage Bank isolates training states outside policy updates, while BiCuRL selects curricula via held-out evaluation, verifies rollouts with task rubrics, and updates policies using GRPO. Evaluated on AutoMedBench-Lite, BaT-4B and BaT-9B double their Qwen Instruct baselines' Overall scores, with BaT-9B achieving 79.6, surpassing Claude Opus (77.5).
self-evolving agentsbilevel curriculum rlstage bankmedical imaging workflowsgrpo
Competing at Every Price Point with Agentic Evolution over a Menu of LLMs
The paper introduces RoboPhD, an evolutionary meta-agent that generates Pareto-dominant solutions across price-accuracy tradeoffs for agentic tasks. The method leverages agentic evolution over a menu of nine LLM endpoints, requiring ≤100 training examples, task documentation, a seed agent, and per-problem cost targets. Evaluated on DS-1000 (code generation) and PaperFindingBench (document retrieval), RoboPhD achieves Pareto dominance on all but one leaderboard slot, outperforming both highest-accuracy and lowest-cost competitors.
evolutionary meta-agentpareto dominancellm endpointsagentic evolutionprice-accuracy tradeoff
Picking the Right Image to Classify: Reliable-Input Selection in Teledermatology
The paper introduces reliable-input selection, a method to improve dermatology model accuracy by selecting the most classifiable image from multiple clinical photographs of the same case, addressing distribution shifts in teledermatology. The authors benchmark four training-data-free selectors—embedding norm, neighborhood consensus, prediction stability, and model confidence—against an oracle upper bound. Results show that no selector significantly closes the oracle gap, with model confidence performing best but recovering only a small fraction of the potential improvement (20 percentage points in weighted F1 across six datasets and nine backbones). A fusion of confidence and Mahalanobis distance using a labeled reference set also fails to bridge most of the gap.
teledermatologydistribution shiftreliable-input selectiontraining-data-freeoracle gap
Beyond Asking: A Pipeline for Personalized Game Generation that Reads Players from Behavior
We propose a pipeline for personalized game generation that infers player traits from behavioral transcripts. A synthetic player population with ground-truth traits is constructed, enabling policy-agnostic inference from raw gameplay data. An opportunity-aware decision-moment representation disentangles preference from opportunity, improving trait inference accuracy. Few-shot LLM inference outperforms embedding- and rule-based baselines on most traits, though supervised regressors remain stronger overall. Inferred profiles drive difficulty adaptation, validated against ground-truth references and mismatched-profile controls. An exploratory human study investigates transferability to real players.
personalized game generationbehavioral transcriptspolicy-agnostic inferenceopportunity-aware representationfew-shot llm inference
Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication
The paper proposes Gated Counterfactual Refinement for Communication (GCR-C), a token-selection method for visual-token communication that evaluates candidate tokens via full-budget continuation and replaces baseline actions only when reconstruction gain is achieved. GCR-C employs a rollout-style correction layer over Local-MDL, constructing a compact candidate set and assessing each through matched continuation. Experiments on CIFAR-10, STL-10, a 5G-LDPC link, and Kodak transfer demonstrate consistent quality improvements at low-to-medium rates without increasing packet rates, revealing a quality-computation tradeoff from counterfactual evaluation.
visual-token communicationcounterfactual refinementlocal-mdltoken-selectionreconstruction gain
Securing AI-Generated Code: A Just-in-Time Vulnerability Detection and Remediation Pipeline
The paper introduces an automated pipeline for detecting and remediating vulnerabilities in AI-generated Python code. The pipeline combines static analysis tools (CodeQL, Bandit) with LLM-based code validation and generation, enriched by MITRE ATT&CK techniques and CWE examples. Two configurations were evaluated across four Claude models (Opus 4.8, Sonnet 4.6, Sonnet 5, Haiku 4.5) using 26 LLMSecEval prompts covering 9 CWE categories. Pipeline 2, incorporating initial static analysis findings, achieved vulnerability reductions of -29% to -69%, outperforming Pipeline 1. Verdict consistency averaged 81%, though remediation introduced new vulnerabilities in 15-22% of cases. Sonnet 4.6 emerged as the best-performing model despite not generating the most secure initial code.
static analysismitre att&ckcwellmvulnerability remediation
LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
LENS introduces an index-free framework for in-context search over dynamic raw documents, addressing limitations of pre-materialized retrieval methods. It formulates search as Budgeted Evidence Localization, maintaining a query-conditioned belief over candidate units and iteratively selecting candidates via lexical, local, and exploratory policies. Evidence is consolidated into source-grounded regions and compressed into reusable knowledge clusters. Evaluated on a 500-question benchmark, LENS achieves 62.4% exact match and 84.8% evidence recall, outperforming ReAct in evidence grounding (84.0% vs. 70.7%) on a 150-question Wikipedia subset. LENS operates without preprocessing or persistent indexes, enabling query readiness post-corpus changes.
in-context searchlatent evidencebudgeted localizationquery-conditioned beliefsource-grounded evidence
Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics
The paper provides a unified analysis of instability in deep Q-learning (DQL), identifying three interacting factors: operator-level Bellman bootstrapping bias, estimator-level sensitivity to regression noise, and parameter-dynamics imbalance under data reuse. The authors propose stabilization principles involving controlled bootstrapping, ensemble quantile estimation, and spike-based parameter regulation. Experiments on Atari-100K and Procgen demonstrate improved stability and competitive performance compared to baseline methods.
deep q-learningbellman bootstrappingparameter-dynamicsensemble quantile estimationvalue dynamics
MUSE: An Interactive Meta-Agent for Understanding and Steering LLM-powered Data Science Systems
MUSE introduces an interactive meta-agent for enhancing user understanding and control of agentic data science systems powered by large language models. It dynamically restructures execution traces into semantic levels, enables contextual referencing of workflow steps for grounded queries and revisions, and supports mixed-initiative steering by identifying suspicious steps and scaffolding repairs. A between-subjects study with 15 participants demonstrated that MUSE improves task efficiency and user confidence in understanding and steering workflows.
meta-agentexecution tracesmixed-initiative steeringagentic data sciencesemantic levels
Agent-Native Telemetry: Verifiable State-Delta Evidence for Autonomous Operations
The paper introduces agent-native telemetry, an operational evidence architecture for autonomous AI agents based on verifiable state deltas rather than human-readable logs. It proposes the Agent Telemetry Protocol (ATP) and State-Delta Evidence Ledger, which structure operational facts into four primitives (Transitions, Observations, Relations, State Checkpoints) using content-addressed schemas. ATP employs cryptographic signing, hash-chaining, and dual access paths (stateless decoder, stateful semantic gateway) for verification. Benchmarks on AIOpsLab and OpenTelemetry Astronomy Shop show ATP reduces wire payload by 96.4%, LLM context tokens by 88.8%, query operations by 66.2%, detects all 500 adversarial mutations, and prevents prompt injections across 50 trials.
agent-native telemetrystate-delta evidencecontent-addressed schemashash-chainingsemantic gateway
Measuring Obedience to Authority Across Large Language Models with the Milgram Paradigm
The study introduces a computational adaptation of Milgram's obedience paradigm to measure compliance in large language models (LLMs), using a standardized script with 30 shock levels and graded protests. Evaluating 42 models from 19 families, results show heterogeneous obedience (0-100% full compliance, mean 42.9%), model-specific stability (AUC 0.885 for split-half verification), and selective situational sensitivity (peer defiance shifts behavior, authority presence removal has no effect). Key interventions like declaring the scenario fictional (+17.2 V median) or tool calls (-53.0 V) significantly alter outcomes, with obedience profiles being checkpoint-specific rather than lineage-dependent.
large language modelsobedience paradigmmilgram experimentsituational sensitivitysplit-half verification
QUMem: Personalized Memory for Query-Conditioned User-State Inference in LLM Agents
The paper introduces QUMem, a structured memory framework for query-conditioned user-state inference in LLM agents, addressing limitations of existing memory systems in handling evolving user preferences. QUMem segments interaction histories into variable-length episodes based on semantic continuity, decomposes each episode into independently retrievable factual, preference, and transferable insight memories while preserving temporal positions, and uses three sequential agents for task-specific retrieval planning. The framework achieves state-of-the-art performance on PersonaMem and KnowU-Bench, demonstrating effective long-term personalization.
llm agentsmemory frameworkuser-state inferencepersonalizationsemantic continuity
Trajectory-Level Automatic Curriculum Learning for Legged Locomotion on Unstructured Terrain
The paper introduces Trajectory-level Automatic Curriculum Learning (TACL), a framework for training legged locomotion policies on unstructured terrain by generating adaptive curricula from terrain maps. TACL iteratively learns a difficulty function to score trajectories, then samples new training tasks based on the current policy's capabilities, forming a closed-loop curriculum. Experiments demonstrate a 56.3% improvement in trajectory success rate over direct training and 18.5-39.74% gains over handcrafted curricula on challenging terrain tasks.
automatic curriculum learninglegged locomotionunstructured terraindifficulty functiontrajectory sampling
Domain-Specific Text Embedding Models for Entity Resolution
Domain-specific triplet fine-tuning improves general-purpose text embedding models for entity resolution tasks by reshaping embedding spaces to better distinguish identity-preserving variations. The study creates a synthetic dataset of business and person records with identity-preserving variations and challenging non-matching examples, then evaluates two widely used embedding models before and after fine-tuning using margin-based similarity evaluation. Results demonstrate substantial improvements in separating true matches from highly similar non-matches, suggesting targeted fine-tuning as a practical approach for enhancing embedding models in data quality management and information retrieval.
entity resolutiontext embeddingtriplet fine-tuningmargin-based similaritysynthetic dataset
Digital Twin Degradation: Detecting Cyber Physical Attacks via Temporal Inconsistencies
The paper introduces a detection framework for identifying cyber physical attacks by leveraging temporal inconsistencies between Cyber Physical Systems (CPS) and potentially degraded Digital Twin (DT) views. A DT predictor is trained on normal system behavior to model short-term dynamics, with discrepancies between predicted and observed states transformed into multi-horizon temporal features. An unsupervised density model characterizes normal consistency patterns, while sequential change detection identifies sustained deviations. Evaluated on SWaT, HAI, and BATADAL datasets under DT degradation scenarios, the method achieves up to 98% detection reliability and false alarm rates below 2%, demonstrating effectiveness without requiring attack signatures or labeled data.
digital twincyber physical systemstemporal inconsistenciesunsupervised density modelsequential change detection
A Tree-Structured Approach for Phishing Template and Attacker Attribution Analysis
The study proposes a tree-structured approach for phishing template attribution by modeling webpages as DOM trees and clustering them using structural features. Three unsupervised clustering algorithms are evaluated on DOM-tree representations, with depth sensitivity analysis and a novel level-wise Jaccard Distance Score for cluster quality assessment. Results show structural representations effectively identify template reuse, enabling detection of zero-day templates and coordinated phishing campaigns.
dom treesphishing kitsunsupervised clusteringjaccard distancetemplate attribution
TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
We propose Transition-wise Rubric Credit Assignment (TRCA), a method for fine-grained credit assignment in long-horizon LLM agents without relying on process evaluators or successful trajectories. TRCA evaluates transitions using Evidence, Execution, and Invalidity rubrics, deriving Foundational Rubric Reward for local transition quality and Breakthrough Rubric Reward for incremental task progress. Combined with terminal outcomes, these signals enable step-level policy optimization. Experiments on ALFWorld, WebShop, and SearchQA benchmarks show TRCA improves WebShop scores by 6.0%-12.6% with Qwen2.5-7B-Instruct and SearchQA scores by 1.9%-18.3% with Qwen2.5-3B-Instruct, demonstrating effectiveness in sparse-reward settings.
credit assignmentlong-horizon tasksrubric evaluationpolicy optimizationsparse rewards
FeatureHospital: A Skill-Driven Multi-Agent Framework for Automated Algorithm Customization in Multi-View Multi-Label Feature Selection
FeatureHospital introduces a skill-driven multi-agent framework for automated customization of multi-view multi-label feature selection algorithms. The system diagnoses dataset characteristics, deploys specialist agents with domain skills to prescribe optimization strategies and loss terms, reconciles prescriptions into a dataset-specific objective, and optimizes feature selection. Experiments show the framework constructs effective algorithms tailored to diverse dataset characteristics without manual intervention.
multi-agent systemfeature selectionmulti-view learningmulti-label classificationautomated algorithm design
When Single-Dataset Conclusions Fail: A 45-Task Study of Threshold Tuning and Resampling for Imbalanced Classification
The study demonstrates that single-dataset evaluations of class-imbalance handling methods yield unreliable conclusions, through a systematic 45-task analysis of threshold tuning and resampling techniques. Using a leakage-free nested cross-validation protocol across binary classification tasks with imbalance ratios from 1:1.5 to 1:178 (2,025 model fits), the authors show that Random Forest benefits most from threshold tuning (delta-F1 = +0.101), contrary to findings on the Kaggle credit-card fraud dataset. SMOTE improves performance across the suite (mean delta-F1 = +0.076), while tuning benefits peak at intermediate imbalance ratios (1:15-1:40) and calibration error fails to predict tuning utility.
class-imbalancethreshold tuningnested cross-validationsmotecalibration error
Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision System
The paper proposes a GCN-assisted A2C (Graph Convolutional Network-assisted Advantage Actor-Critic) framework for latency-efficient video transmission in UAV vision systems. The method combines a GCN to identify pixel-correlated regions with a DRL agent (A2C) that selects optimal subgroups for transmission, using Lagrangian dual form and gradient descent for constraint optimization. Experiments demonstrate reduced transmission latency and false detection rates compared to baseline DRL and state-of-the-art models.
graph convolutional networkactor-criticuav visionlatency optimizationpixel-correlated
TokenSTFormer: A Tokenized Spatial-temporal Attention Model for Holistic Motion Analysis in Adolescent Idiopathic Scoliosis Screening
The study introduces TokenSTFormer, a tokenized spatial-temporal attention model, and the ScoliGait dataset (1,516 gait video clips with X-ray pairs) for Adolescent Idiopathic Scoliosis (AIS) screening. The model tokenizes spatial and temporal semantics to improve feature representation, outperforming vanilla Vision Transformer encoders with 0.79 accuracy. This demonstrates the viability of attention-based motion analysis for scalable AIS detection.
tokenized attentionspatial-temporal modelingscoliosis screeningvision transformergait analysis
Assessing LLMs' mathematical abilities requires understanding the various mechanisms of mathematical creativity
The paper proposes a framework for assessing mathematical creativity in large language models (LLMs) by distinguishing five mechanistically distinct modes: reflexive introspection, analogical import, problem-driven construction, domain bridging, and conjecture-formation (split into pattern-driven vs. strategic pursuit). Grounded in historical case studies and transformer architecture analysis, the author argues current LLMs excel primarily at recombination and search over existing mathematical building blocks, leaving other modes theoretically out of reach. As AI-generated proofs become cheaper, the paper contends mathematical value is shifting toward these inaccessible modes, necessitating evaluations organized by this taxonomy rather than aggregate benchmarks.
mathematical creativityllm evaluationconjecture-formationtransformer architecturesdomain bridging
RetroMPA: A Molecular Property-Aware Auxiliary Framework for Enhancing Retrosynthesis Prediction
RetroMPA introduces a molecular property-aware auxiliary framework for enhancing retrosynthesis prediction by injecting chemical knowledge into existing models without architectural modifications. The method employs a post-hoc, model-agnostic chemical filter that recalibrates predictions via a property-aware latent embedding space. Evaluations on USPTO-50K and USPTO-Full show average top-1 accuracy improvements of 5.50% and 2.03%, respectively, across eight models, with wet-lab experiments validating practical utility for unreported substrate combinations in classic reactions.
retrosynthesismolecular property-awarepost-hoc enhancementmodel-agnosticlatent embedding space
AsyTO: Asymmetric Temporal Operator for Parameter-Efficient Multivariate Time Series Forecasting
The paper introduces AsyTO, an Asymmetric Temporal Operator for parameter-efficient multivariate time-series forecasting. The method factorizes per-variable operators into shared history-reading and future-writing temporal modes with per-variable gains, augmented by a low-rank periodic prototype and cycle-separable factorization. This approach maintains linear parameter growth in variable count while outperforming dense phase-blind references. Evaluated across 11 benchmarks, AsyTO achieves the best lightweight error in 30 of 44 dataset-horizon settings, positioning it on the accuracy-compute Pareto frontier.
multivariate time-seriestemporal operatorparameter-efficientlow-rank factorizationcycle-separable
Protein Structure Prediction: From Evolutionary Constraints to Generative Modeling
This review organizes protein structure prediction into four methodological phases and three cross-cutting transitions, tracing the evolution from explicit evolutionary coupling features to generative modeling. It examines developments through representations and data, architectures and learning strategies, and confidence and evaluation. Key transitions include the shift from early contact prediction to learned sequence representations in AlphaFold2, RoseTTAFold, and ESMFold; from monomer folding to integrated modeling of heterogeneous systems in AlphaFold-Multimer, RoseTTAFoldNA, and AlphaFold3; and from prediction-oriented inference to design-oriented generative modeling in RFdiffusion. This framework clarifies how methodological shifts have shaped recent models' capabilities and limitations.
protein structure predictionalphafold2rosettafoldgenerative modelingevolutionary coupling
Behaviour Is an Incomplete Measure of Reasoning Development: Cross-surface pre-arrival accessibility and the limits of developmental inference in a recurrent-depth reasoner
The study demonstrates that behavioral metrics alone are insufficient for assessing reasoning development in neural networks, using a 30M-parameter recurrent-depth relational reasoner trained on symbolic and verbal surfaces. Through dense behavioral trajectories, pre-arrival hidden-state probes, and controlled experiments, the authors show a 186.5-fold disparity in behavioral competence (70 vs. 13,055 epochs) for three-hop reasoning, while linear probes detected future-answer identity pre-arrival (p = 0.012987). Probe accessibility persisted across surfaces (p = 0.000999), but tracking it was confounded by behavioral arrival dependencies. The work establishes behavioral competence, internal accessibility, and training dynamics as distinct observables.
recurrent-depth reasonerpre-arrival probesbehavioral competencehidden-state accessibilitymulti-hop reasoning
Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics
The paper develops an eigenanalysis framework to explain error growth in neural autoregressive models of chaotic systems by analyzing the Jacobian of their one-step update map. It shows that direct-step architectures exhibit unstable eigenvalues (>1 magnitude), causing rapid divergence, while integration-constrained models achieve neutral stability via eigenspectrum collapse onto the unit circle. A stability-promoting loss regularizing Jacobian-driven error amplification improves forecast accuracy and robustness, validated across 29 models on the Kuramoto-Sivashinsky system.
eigenanalysisautoregressive modelsjacobianchaotic dynamicsspectral radius
Learn What's Left, Not What's Mastered: Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization
The paper introduces Saturation Aware Advantage Reweighting for Multi-Reward Policy Optimization (SA-MRPO), a method addressing limitations in multi-objective RL for language model fine-tuning. SA-MRPO independently standardizes each reward objective and adaptively discounts contributions based on batch-level saturation estimates, dynamically reallocating optimization effort toward under-optimized objectives. Experiments on mathematical reasoning, adaptive reasoning, and coding benchmarks show SA-MRPO improves harder correctness objectives by up to 5% on AIME24 and 9.2% on AMC23, while maintaining performance on already-satisfied objectives.
multi-reward policy optimizationadvantage reweightingsaturation awarenessreinforcement learninglanguage model fine-tuning
OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation
OceanLight introduces an efficient global ocean forecasting framework combining geometry-adaptive unstructured mesh tokenization with a graph neural network (GNN) backbone, addressing computational inefficiencies of structured-grid approaches. The method eliminates unnecessary computation on land cells and adapts resolution to local flow complexity. Results show superior pointwise forecast accuracy, kinetic energy spectral fidelity, and geostrophic balance consistency compared to operational numerical analyses and AI baselines, while reducing GPU memory by 62% and FLOPs by 70%. The framework reliably captures mesoscale eddies and establishes a scalable paradigm for data-driven oceanography.
ocean forecastingunstructured meshgraph neural networkgeostrophic balancemesoscale eddy
CAPO: Constraint-Aware Prompt Optimization for LLM Agents
CAPO (Constraint-Aware Prompt Optimization) introduces a primal-dual method combining pool-based rewrites with adaptive constraint weighting to optimize system prompts for LLM agents under operational constraints. The method, extended as DCAPO (Dynamically Trained CAPO), trains a feedback-conditioned rewriter while keeping the task agent frozen. Evaluations show CAPO reliably meets operational constraints while improving task performance across agentic benchmarks and assistant-style evaluations, with DCAPO achieving feasible prompts in all tested domains and matching or surpassing baseline accuracy.
prompt optimizationprimal-dual methodllm agentsconstraint-awarepool-based rewrites
Governance at the Boundary: How Agent Decomposition Degrades Policy Compliance
The paper introduces Fiducia-bench, a benchmark evaluating financial agents' governability (policy compliance in escalation, abstention, and audit trails). It investigates how agent decomposition degrades governance via fact attenuation at component handoffs. In a 626-episode experiment across 100 KYC/AML variants, two models, and three architectures, decomposition increased fact attenuation (0% single-loop vs. 56-85% in decomposed setups for a 32B model; 3-6% for gpt-4.1-mini), showing capability-dependent governance costs. Attenuation caused both under- and over-escalation based on dropped fact type (risk/exculpatory).
governabilityagent decompositionfact attenuationkyc/amlpolicy compliance
Walk Before You Run: The Importance of Data Exploration for Data Analysis Agents
The paper identifies a critical gap in LLM-based data-analysis tools by formalizing Data Exploration as an explicit, evaluable stage preceding downstream tasks. It introduces two benchmarks (a real multi-sheet workbook and an extended DSBench) to assess structured artifacts capturing tables, columns, relationships, and quality signals. Experiments demonstrate that current LLMs and agents frequently miss logical structures, while explicit Data Exploration improves downstream correctness by 15-30%, advocating for human-in-the-loop validation at this stage.
data explorationllm-based analysismulti-sheet workbooklogical structure recoveryhuman-in-the-loop
Decoupling Parcellation from Classification: Systematic Benchmark of Fast Brain Segmentation Methods for Alzheimer's Disease Detection
The study systematically benchmarks fast deep learning brain parcellation methods (SynthSeg+, OpenMAP-T1) against FreeSurfer (FS-HV) for Alzheimer's disease (AD) detection, decoupling parcellation from classification. A factorial design evaluates three parcellation methods, two volumetry strategies (hard vs. soft), and four classifier paradigms (clinical thresholds, supervised feedforward networks, ensemble methods, and foundation models with zero/few-shot prompting) on OASIS-1. Results are quantified using BCa Bootstrap 95% confidence intervals, providing a comprehensive comparison of downstream AD classification performance.
brain parcellationalzheimer's disease detectiondeep learningvolumetry strategiesclassifier paradigms
NICE: Scale-Stable Perturbations for Graph Neural Network Explanations via Noise Corruption
The paper introduces NICE, a Noise Corruption-based explanation framework for Graph Neural Networks (GNNs), addressing the Scale Drift phenomenon caused by Element-wise Masking (EM) in post-hoc explainers. NICE employs Noise Corruption (NC) to perturb messages while preserving expected squared norm, and learns a Stochastic Restoration Boundary (SRB) to balance prediction restoration and compactness. Boundary-Integrated Gradient (BIG) converts SRB into edge attributions. Experiments on multiple benchmarks show NICE improves explanation performance and model faithfulness, with NC reducing Scale Drift by 30-50% compared to EM.
graph neural networkspost-hoc explainersscale driftnoise corruptionstochastic restoration boundary
RagGAD: Rationale-Aware Conditional Gaussian Mixture Normalizing Flow for Unsupervised Graph Anomaly Detection
RagGAD proposes an unsupervised graph anomaly detection framework using rationale-aware conditional Gaussian mixture normalizing flows to address limitations of homophily-based methods. The approach employs an adaptive rationale disentangler to separate stable rationales from spurious correlations, decomposing them into robust and fragile components. It models node distributions via rationale-non-rationale Gaussian mixtures and robust-fragile rationale learning, identifying anomalies as low-density deviations. Experiments show RagGAD outperforms state-of-the-art methods on multiple benchmarks.
graph anomaly detectiongaussian mixture modelnormalizing flowrationale disentanglementunsupervised learning
Dynamic Evidence Collection Ecosystem for Assessment Integrity and Authentic Competence
The paper proposes a Dynamic Evidence Collection Ecosystem framework to address assessment integrity challenges posed by Generative AI. The method integrates continuous multi-source evidence collection (iterative artefacts, design logs, peer collaboration) with AI-enabled learning analytics for formative feedback. Grounded in assessment-redesign scholarship, the approach shifts focus from AI detection to authentic competence demonstration through process-oriented evaluation. An implementation scenario demonstrates institutional adoption potential, though limitations and academic risks are noted.
generative aiassessment integritylearning analyticsauthentic assessmentformative feedback
Prior Audit-Repair Context Shifts LLM Verifier Thresholds Toward Leniency
The study demonstrates that prior audit-repair context in LLM verification pipelines systematically reduces false alarm rates by 2.8-11.5 percentage points (9-25% relative reduction) across 15 model-wording combinations on ProcessBench traces. Using signal-detection analysis on human-verified correct samples, researchers found this effect stems from threshold shifts (surviving correction in 13/15 cases) rather than discrimination changes (d' remained stable). Contrary to accumulated-message literature predictions, error-containing audit episodes further reduced false alarms. Component analysis revealed complementary effects of repair content and audit verdict across model families.
llm verificationfalse alarm reductionsignal-detection theoryprocessbenchthreshold shift
From Sequence to Structure: Relational Uncertainty Propagation for LLM Agents
The paper introduces RUPA (Relational Uncertainty Propagation for Agents), a trajectory-level uncertainty quantification framework for LLM agents that models long-range dependencies in execution histories. RUPA constructs a directed trajectory graph connecting reasoning states, tool interactions, and environment feedback via temporal and semantic edges, then propagates uncertainty across this structure to capture cumulative execution risk. Evaluated on τ-2, Terminal-Bench-2, and GAIA benchmarks with 6 open-source LLMs, RUPA outperforms existing methods in uncertainty estimation accuracy, early failure detection, and uncertainty-guided execution.
uncertainty quantificationllm agentstrajectory graphdependency propagationfailure detection
MUPA$^{2}$E: Multimodal Unified Perception with Asymmetric Attention for Emotion Assessment
MUPA$^{2}$E introduces a unified multimodal framework for emotion assessment, processing facial video and EEG through a shared asymmetric-attention backbone. The method represents video via axis-folded frame tokens and EEG as raw waveforms or spatial projections, evaluated on DMER under subject-independent protocols. With merged EEG fusion at stride 30, it achieves 70.07% test accuracy, though duration-controlled cropping to 20s reduces performance to 62.71%, revealing duration-related dataset biases.
multimodal fusionasymmetric attentioneeg processingaffective computingunified perception
ALPS: Measuring Valid Creativity in Large Language Models with Mathematical Construction
The paper introduces ALPS (Austin-Law Proof-Synthesis), a benchmark for measuring valid creativity in large language models through mathematical construction tasks. Each instance requires generating either an infinite mathematical structure satisfying a given equational law or proving its nonexistence, with solutions verified by automated proof checking. Evaluation on 4,141 laws shows leading automated provers resolve only 2.2% of cases, while the strongest reasoning model achieves 14% success on proofs but fails on constructions. The benchmark includes a public generator for unlimited novel instances and full verification infrastructure.
mathematical constructionautomated proof checkingequational lawsvalid creativitybenchmark design
A Scalable Pipeline for LLM-Teacher Distillation Labeling: Work-Stealing Job Scheduling and Memory-Aware GPU Concurrency
The paper presents a scalable pipeline for LLM-teacher distillation labeling, addressing workload skew and GPU utilization. Key innovations include a work-stealing ring pool with atomic task claims via SQLite, memory-aware GPU concurrency control, and a relabeling benchmark methodology using public gold-labeled data. The system achieves 3.4× higher throughput than static sharding under skewed loads, maintains zero task loss during worker failures, and provides measurable quality-cost tradeoffs for instruction-tuned LLMs on irony and sentiment tasks. All experiments use commodity hardware and open datasets.
llm-teacher distillationwork-stealing schedulergpu concurrencyatomic task claimsrelabeling benchmark
CM-MAE: A Physics-Guided Cross-Modal Self-Supervised Learning Framework for Vision-Wireless Applications
CM-MAE introduces a self-supervised vision-wireless pretraining framework for cross-scenario representation transfer, using synchronized RGB frames and 64-beam received-power vectors without auxiliary labels. The method employs a soft contrastive alignment loss that avoids false negatives by building target distributions from beam-power similarities, alongside a masked joint decoder for modality-dropout reconstruction. Evaluated on DeepSense 6G, it achieves 29.49% linear-probe transfer accuracy (vs 24.88% baseline) and 77.38% Top-1 accuracy after fusion fine-tuning, demonstrating robust cross-modal representation learning.
self-supervised learningcross-modal alignmentcontrastive lossrepresentation transferwireless vision
LLMs Get Smarter from Targeted Synthetic Multilingual Data
The paper introduces HOTFIXR, a data generation framework that improves multilingual reasoning in language models by targeting their weaknesses. HOTFIXR probes a student model's multilingual deficiencies and generates synthetic training data to address them, avoiding the trade-offs of existing approaches (English-only routing or language-balanced training). Evaluations show HOTFIXR improves in-distribution task performance by 6.2%, reduces catastrophic forgetting on out-of-distribution tasks by 3.7%, and enhances out-of-distribution language performance by 7.1%.
language-specific competencysynthetic data generationmultilingual reasoningcatastrophic forgettinghardness optimization
Solvable Sokoban Without a Solver via Diffusion
A transformer-based discrete diffusion model achieves 77.4% solvability rate on Sokoban puzzles despite being trained solely on tile completion without solvability labels or solver access. The model, adapted from MD4 and trained on DeepMind's Boxoban dataset, learns $p(c_k \mid \text{any subset})$ via masked diffusion, enabling non-autoregressive generation that better captures Sokoban's non-local constraints. 94.5% of failures become solvable by removing one wall, demonstrating that local training can implicitly learn global solvability.
discrete diffusionsokobannon-autoregressivemasked trainingpspace-complete
Navigation-Informed Embeddings: Dense-Retriever Adaptation from Agent Search Traces
The paper introduces Navigation-Informed Embeddings (NIE), a method for adapting dense retrievers using agent search traces without additional labeling. NIE-Stop treats stopping documents as soft positives, while NIE-Path incorporates preceding path documents with ordinal constraints and geometric decay. Evaluations show that NIE improves Recall@20 from 72.2 to 78.0 on a target benchmark, with NIE-Path achieving 55.4 on long paths versus 46.7 for the baseline. The method also enhances nDCG@10 by 1.9 points on BEIR HotpotQA, demonstrating cost-effective adaptation for retained trajectories.
dense retrieversearch tracesembedding adaptationordinal constraintsrecall@20
Ask to Be Sure: Informative Interactions for Confident Multi-Turn LLM Recommendation
We propose a novel method for improving multi-turn conversational recommender systems by quantifying interaction effectiveness through entropy reduction over recommendations, used as a reward signal for LLM fine-tuning. The approach avoids reliance on ground-truth recommendations by measuring uncertainty reduction, enabling strategic interaction generation. Supervised fine-tuning and direct preference optimization were applied to the INSPIRED and ReDial datasets, demonstrating improvements in both recommendation quality and conversational efficiency compared to existing methods.
entropy reductionconversational recommender systemssupervised fine-tuningdirect preference optimizationmulti-turn interactions
Augmenting Text to Increase Translation Difficulty
We introduce Adversarial Translation Optimization (ATO), a method to augment machine translation benchmarks by increasing translation difficulty without requiring LLM prompting, human curation, or task-specific model training. ATO combines adversarial optimization with a differentiable translation difficulty estimator, using gradients from a combined difficulty and fluency objective to iteratively replace tokens via Beam Search. The modified benchmark reduces average translation quality (xCOMET) from 0.93 to 0.82, outperforming paraphrasing (0.88) and zero-shot baselines (0.86). Human evaluation confirms the augmented texts are harder to translate while remaining reasonably grammatical and plausible. We release two datasets of 350 English texts each and the accompanying code.
adversarial translation optimizationdifferentiable estimatorbeam searchxcometmachine translation
UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations
UI-Mate introduces a foundation GUI agent addressing challenges in automating digital tasks through three contributions: a scalable environment-grounded training stack for closed-loop task generation and reinforcement learning, in-context demonstration learning for multimodal workflow transformation and re-planning, and the OSWorkerBench benchmark for evaluating long-horizon office tasks. The agent, UI-Mate-27B, achieves state-of-the-art performance on general computer-use benchmarks (77.0% on OSWorld-Verified, 66.2% on WindowsAgentArena) and outperforms its Qwen3.6-27B base on OSWorkerBench by 17.7 and 24.5 points in strict success and progress, respectively. In-context demonstrations significantly enhance long-horizon task reliability, raising strict success from 17.2% to 35.4%.
gui agentin-context learningenvironment-grounded traininglong-horizon tasksmultimodal demonstrations
Unified Pedestrian Path Prediction Using Inverse Reinforcement Learning
This paper introduces a unified pedestrian path prediction framework by adapting the Spatial-Temporal Graph Attention Network (STGAT) with specific state and action definitions. The framework supports deterministic and stochastic policies, one-time and sequential decision-making, and reinforcement-learning algorithms such as REINFORCE and proximal policy optimization. Compared to standard supervised-learning formulations, the proposed approach demonstrates improved prediction performance across benchmark datasets. The results suggest that reformulating the decision process and training objective can enhance advanced pedestrian trajectory prediction architectures and potentially benefit other graph-based prediction models.
spatial-temporal graph attention networkreinforcement learningproximal policy optimizationpedestrian path predictionstochastic policies
Information Geometry of Message Passing
The paper introduces natural-gradient message passing (NGMP), a novel variational inference scheme derived from the Bethe free energy on Forney-style factor graphs. NGMP constrains edge marginals to exponential families, ensuring that at stationary points, the natural parameter equals the sum of two projected messages from incident factors. This method improves upon variational message passing by retaining the exact message components representable by the receiving family, enhancing accuracy in scenarios with persistent uncertainty. Experiments on Poisson smoothing, heteroskedastic regression, and hourly ETTh forecasting demonstrate NGMP's superior uncertainty calibration.
natural-gradient message passingbethe free energyforney-style factor graphvariational inferenceexponential family
Noesis: Bidirectional Graph-RAG with Adaptive Parallelism and Cross-Knowledge-Base Semantic Discovery
Noesis introduces a decoupled Graph-RAG architecture addressing limitations in Retrieval-Augmented Generation over knowledge graphs. It employs four algorithms: Bidirectional Graph Traversal with Graph-Feedback Context Resolver for semantic coherence, AIMD Concurrency Controller for adaptive scaling, Moesis for domain-aware selective quantization, and Mesh for cross-KB semantic routing. Evaluated on HotpotQA, Noesis achieves 59.5 EM / 74.7 F1, outperforming GraphRAG by +27.8 EM while using a 35B on-premises model. Source text verification confirms 90% precision on long-range causal edges.
graph-ragbidirectional traversalselective quantizationsemantic routingknowledge graphs
Pre-training Visual Dexterity in Simulation
We introduce Simulation Pre-training for Dexterity (SPD), a framework for dexterous manipulation pre-training using entirely simulated data collected via VR teleoperation. SPD employs five operators to gather 75 hours of multi-task dexterous manipulation trajectories in simulation, used to pre-train a causal transformer on a sequence modeling objective. Fine-tuning on 1-2 hours of physical demonstrations on a 56-DoF bimanual setup shows SPD outperforms behavior cloning from scratch, validating simulation teleoperation as a viable pre-training source. Ablation studies highlight the benefits of history conditioning and short action chunks for reactive control.
dexterous manipulationsimulation pre-trainingcausal transformervr teleoperationbehavior cloning
Comprehensive Benchmarking of Deep Learning Architectures for Lung Cancer Histopathology
This study introduces a two-stage deep learning framework for lung cancer histopathology, combining multi-class tissue classification and pixel-level region segmentation. Six classification models (including VGG16, DenseNet, and YOLO11) are evaluated on 39,000 images from LC25000 and LungHist700, with YOLO11 achieving 98.38% accuracy. Four segmentation models (U-Net, DeepLabV3+, etc.) are tested on GlaS, where DeepLabV3+ scores 0.80 IoU and YOLO11-seg achieves comparable performance with 14x fewer parameters. The integrated framework provides an efficient baseline for automated analysis.
histopathologytissue classificationregion segmentationyolo11deeplabv3+
Layers Matter: Why Continual Learning Regularization Should Be Layer-Adaptive
The paper introduces a layer-adaptive approach to continual learning regularization, demonstrating that per-parameter regularization methods like Elastic Weight Consolidation (EWC) inadequately capture layer-wise sensitivity due to missing top Hessian eigenvalue information. Under a block-diagonal Hessian assumption, the authors prove that forgetting decomposes into per-layer terms weighted by each layer's top Hessian eigenvalue, and that diagonal-Fisher weights fail to recover this eigenvalue. They propose a simple recipe: strongly protect early layers while allowing deeper layers to adapt. Applying this to EWC and Supervised Lifelong Classification Adaptation (SLCA) yields improved average performance and reduced forgetting metrics.
continual learninghessian eigenvalueelastic weight consolidationlayer-adaptive regularizationforgetting metrics
Breaking and Defending LLM-Powered Social Media Bot Detection Systems
We introduce LSABRE, a multi-LLM defense architecture for robust social media bot detection, addressing adversarial attacks targeting LLM-based classifiers. Two novel adversarial strategies exploit semantic and contextual weaknesses, reducing detection accuracy by up to 48%. LSABRE integrates multiple LLMs to enhance robustness, maintaining 86% detection accuracy under adaptive adversarial conditions. The methodology generalizes to LLM-powered cybersecurity applications, including phishing detection and fraud analysis. This work bridges offensive and defensive aspects of LLM-powered threat-specific cybersecurity systems, offering insights into mitigating adversarial exploitation of LLM reasoning mechanisms.
adversarial learningllm-based classifierssemantic analysisbot detectioncybersecurity
Bounded Agents: Delegation Security for Multi-Agent AI Systems
The paper introduces Agentic Principal Chain (APC), a delegation-security framework for LLM-based multi-agent systems that dynamically tracks and restricts authority across sessions. APC employs six authorization checks against accumulated session state, enforcing scope/budget constraints and preventing prohibited action combinations via composition closure. Theoretical guarantees include Blast Radius Monotonicity and Composition Soundness under complete restriction sets. Evaluation on 3,154 instances (InjecAgent, AgentDojo, ASB) showed APC reduced exfiltration from 75-100% to 0%, data theft to 0/544 cases, and intent-violation rates by 34.6-78.4 percentage points, with 0.24ms 99th-percentile latency overhead.
delegation securityauthorization architectureagentic principal chaincomposition closureblast radius monotonicity
Dear Algo: A Precision-First Agentic Intent Layer for Unified Search and Recommendation
The paper introduces Dear Algo, an agentic intent layer unifying search and recommendation by compiling explicit, inferred, negative, and compound user intent into executable retrieval plans. The system employs precision-first evaluation, combining conventional retrieval with optional semantic/multimodal reranking while maintaining intent-to-retrieval contract independence. Evaluations show 94.4% exact-Relevant precision in a blinded audit (n=296) and 1.11 more qualified candidates per 20 slots versus an LLM-derived-query baseline (72 request clusters). A serving-path study demonstrated 1.97-point reduction in Irrelevant content share and 2.24-point increase in Exact-Relevant share.
agentic intent layerprecision-first evaluationmultimodal rerankingintent-to-retrieval contractllm-as-a-judge
Beyond Visual CoT: Internalized Visual Thinking for Proactive Video Reasoning
The paper introduces Internalized Visual Thinking (IVT), a post-training framework for multimodal large language models that internalizes visual reasoning during training to avoid explicit intermediate image generation at inference. IVT jointly optimizes textual prediction and next-embedding prediction over unlabeled videos, capturing motion, object transitions, and latent intent. Evaluations across six settings show IVT outperforms direct-answer fine-tuning and matches Visual CoT performance while reducing latency by 5x, suggesting pixel-space generation may be unnecessary for proactive video reasoning.
multimodal reasoningvisual chain-of-thoughtlatent representationspredictive world modelingproactive video reasoning
CoupVisor: Strategy Optimization by Round and Challenge Decision Support
CoupVisor introduces a decision-support system for the hidden-information game Coup, optimizing turn actions and challenge decisions through a unified game event representation. The system combines role probability estimation with card-counting to assess claim veracity, correcting early-game false positives. Evaluations across rule-based, learned, and heuristic players show that win-oriented reward policies outperform baselines, with reward structure (short-term vs. ultimate victory) determining optimal learning approaches.
decision-support systemhidden-information gamebelief trackinglearning-based policiesreward optimization
Feasible and Novel Synthetic Population Generation with Tabular and Sequential Travel Attributes
The paper proposes a regularized two-stage framework for generating synthetic populations with both tabular socio-demographic attributes and sequential travel behaviors. Stage 1 uses a Wasserstein GAN with gradient penalty augmented by three regularization terms (IGP, LDR, CLAP) to improve feasibility, diversity, and novelty in tabular synthesis. Stage 2 employs Transformer and LSTM-Attention models to generate sequential travel attributes conditioned on tabular profiles. Results show regularization improves feasibility by 2.1-3.7pp and novelty by 6.6-10.0pp, with LSTM-Attention matching trip-length distributions and Transformer achieving 90.6% sequential F1.
synthetic population generationwasserstein ganregularization termssequential attribute modelingactivity-based travel demand
Scaling Manual-Grounded Appliance Manipulation with Data Synthesis and Unified Planning
The authors propose MAGE, a scalable data synthesis pipeline using Hierarchical Appliance Graphs (HAGs) to automatically generate manual-grounded appliance manipulation data, addressing the lack of diverse task-oriented datasets for long-horizon planning. They construct UseAppliance, the first large-scale dataset (22 categories, 89K+ part annotations, 53K+ tasks, 33K+ adjustment steps) for this domain, and develop AppliancePlan, a 7B-parameter end-to-end model. AppliancePlan outperforms baselines by 10× on open-loop planning in RealAppliance-Bench and demonstrates effective sim-to-real transfer in real-robot experiments with six household appliances.
hierarchical appliance graphlong-horizon planningdata synthesis pipelinesim-to-real transfermanual-grounded manipulation
RAGas: Retrieval-Augmented Gas Optimization for Smart Contracts with Continuous Knowledge Integration
RAGas introduces a retrieval-augmented generation framework for optimizing Gas usage in Ethereum smart contracts by continuously integrating knowledge of Gas inefficiency patterns. The method analyzes syntactic and semantic constructs to identify six high-level categories and twelve fine-grained antipatterns, then employs a large language model for detection and automated fixes. Evaluations on deployed contracts show RAGas reduces Gas consumption by up to 11% while maintaining high precision and recall in detecting wasteful code snippets.
retrieval-augmented generationgas optimizationsmart contractsethereumknowledge integration
Characterising cardiac tissue properties with graph neural networks
A graph neural network framework was developed to characterize cardiac tissue properties from sparse intracardiac measurements, targeting ablation therapy for premature ventricular complexes. The method processes synthetic electrogram signals on 2D flat surfaces, identifying fibrosis, rapid depolarization, and high excitability with average precisions of 0.96, 0.97, and 0.95, respectively. The model generalizes to 2D curved surfaces with few-shot fine-tuning, demonstrating clinical potential for PVC ablation.
graph neural networkelectrogram signalscardiac ablationfew-shot fine-tuningpremature ventricular complexes
Schema-Agnostic Graph Reasoning Agent for Hybrid Knowledge Graphs
The paper introduces GRA, a schema-agnostic Graph Reasoning Agent that navigates hybrid knowledge graphs (textual concepts and relational tables) using seven generic tools, discovering domain-specific information at runtime. GRA operates with primitives analogous to file system tools (ls, cat, grep) for graph traversal, achieving 88.4% accuracy on the UFK-M industrial benchmark (258 analytical questions), outperforming a full-context agent by 5.1 percentage points while using <1/3 of input tokens. Ablation shows gains stem from selective agentic access rather than graph topology, requiring reliable tool-use capability.
knowledge graphsgraph reasoning agenthybrid knowledge representationtool-calling llmselective navigation
The Authority Resolution Framework: A Five-Domain Ontology for Governing Who and What Decides, at Scale
The Authority Resolution Framework (ARF) introduces a five-domain ontology for representing and resolving authority in autonomous AI systems, addressing the gap between technical capability and contextual authorization. ARF models authority through the Authority Relation (AR) primitive, comprising actor, action, object, context, justification chain, and a DNA-Coefficient measuring divergence between formal and practiced authority. The framework provides machine-interpretable JSON-LD representations and knowledge-graph query patterns, positioning authority resolution as a knowledge-representation problem at the intersection of ontology engineering, semantic AI, and AI governance.
authority resolutionontology engineeringknowledge-graphautonomous agentsai governance
CardiacMamba: Fair and Robust RGB-RF Fusion for Remote Heart Rate Estimation via State Space Modeling
CardiacMamba introduces a fair and robust RGB-RF fusion framework for remote heart rate estimation, addressing RGB-only limitations via state space modeling. The method combines optical facial cues (RGB) and radio-frequency cardiac motion (RF) through three novel components: a Temporal Difference Mamba Module (TDMM) for RF temporal variation enhancement, a bidirectional SSM-based interaction mechanism for RGB-RF alignment, and a Channel-wise Fast Fourier Transform (CFFT) module for spectral refinement. On EquiPleth, it achieves 0.96 bpm MAE, 3.06 bpm RMSE, and 0.97 Pearson correlation, reducing skin-tone bias (0.26 bpm MAE gap) while maintaining robustness to RGB degradation and RF-missing conditions.
remote photoplethysmographystate space modelingrgb-rf fusiontemporal difference mamba modulechannel-wise fast fourier transform
A Cognitively Motivated Multidimensional Framework for Evaluating Metaphor Explanations
The paper introduces a cognitively motivated multidimensional framework for evaluating metaphor explanations, addressing limitations of holistic quality ratings. Through a dense annotation study (11,200 ratings), the authors demonstrate that explanation quality is genuinely multidimensional, annotator disagreement is systematic, and six dimensions form a shared cluster plus two independent judgment axes. An exploratory study shows standard automatic evaluation pipelines can partially recover this structure, predicting discriminative dimensions well while errors correlate with human (dis)agreement.
metaphor explanationmultidimensional evaluationannotation studyautomatic evaluationhuman judgment
RLCascadeRouter: Quality-Estimator-Free Cascade Routing via Reinforcement Learning
RLCascadeRouter introduces a reinforcement learning framework for cascade routing of LLM queries, eliminating the need for independent quality estimators by directly optimizing performance-cost trade-offs. The method formulates routing as a Markov decision process with a Cascade Policy Network that models candidate complementarity and remaining-action value. Evaluated on ten LLMRouterBench benchmarks with thirteen LLMs, it outperforms baselines, adapts to unseen models without retraining, and achieves superior efficiency.
cascade routingreinforcement learningmarkov decision processlarge language modelsperformance-cost trade-off
Pricing the Risk of Runtime Compression: Anytime-Valid Admission and a Served-Output Law for Compressed Serving State
The paper introduces an anytime-valid admission control framework for runtime compression in serving systems, replacing union-bound risk budgeting with a physically accounted ledger that maintains soundness guarantees. Key innovations include a machine-checked design law (TV ≤ tanh(a_q w_thr)) for served-TV targets, a three-layer audit localizing a 1064x gap to operating points, and exchangeable extrapolation for non-vacuous risk bounds. Evaluated on 352,333 live admission calls, the method halves exact-fallback rates (0.30 → 0.14) while maintaining Lean 4-verified probabilistic kernels (228 theorems).
anytime-validruntime compressionserved-tvexchangeable extrapolationunion-bound
KV-Rescue: Recovering Reasoning Language Model KV Eviction Loss via Stepwise Interleaving
KV-Rescue is a training-free inference framework that mitigates accuracy loss from KV-cache eviction in reasoning tasks by interleaving steps from a base model (with eviction) and a lightweight full-context helper. The method detects incoherent or repetitive generations via entropy and compressibility metrics, terminating them early. Evaluated on five math benchmarks with Qwen2.5-Math 7B/72B, it recovers 87% of eviction-induced accuracy loss at budget B=64 while reducing base-model tokens by 43% by preventing runaway degeneration.
kv-cache evictionreasoning language modelrunaway degenerationentropy detectioninterleaved inference
Routing Divergence Is Not Evidence of Behavioral Influence in Same-Weight MoE Self-Distillation
The study investigates routing divergence in same-weight Mixture-of-Experts (MoE) self-distillation, where identical weights produce different expert routing paths. Using a blockwise decomposition, the authors separate routing and content terms across seven open-weight checkpoints and two domains. Results show the routing term's residual-stream exposure is 3.2× larger than its block output fraction, influenced by the routed block's residual share. Behavioral impact is limited: routing changes affect outputs less than half of natural context effects, with content terms being more direction-specific. The findings caution against interpreting routing divergence as behavioral influence without measuring exposure.
mixture-of-expertsself-distillationrouting divergenceresidual-stream exposurebehavioral influence
Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay
The paper introduces SDF (Supersession-Decay Filtering), a dual-filter framework for mitigating stale recommendations in large-scale content platforms. SDF addresses two staleness mechanisms—supersession (emerging updates obsoleting prior content) and relevance decay (natural informational value decline)—via complementary learned models: a relational staleness model for pairwise supersession detection and a predicted traffic ratio (PTR) model forecasting decay from content features. Deployed in Google Discover (hundreds of millions daily users), SDF prunes stale candidates pre-ranking, reducing serving costs. Online experiments show reduced stale content and improved engagement; over two years, user staleness reports declined by 54.9% versus baseline.
recommender systemsstaleness mitigationsupersession detectionrelevance decaylearned filters
ALKEMIE Agent: an autonomous platform for computational materials design
The ALKEMIE Agent platform addresses fragmentation in computational materials workflows by integrating retrieval-augmented generation, a materials knowledge base, and tool-calling iteration within an autonomous framework. It combines AI-assisted structure modeling, bounded task execution, and error diagnostics to coordinate multi-scale simulations like phonon calculations, LAMMPS, and Ab Initio Monte Carlo sampling. Demonstrated applications include materials recommendation, active-learning screening, and interatomic potential training, showcasing unified workflow automation. Future challenges for agentic materials design platforms are outlined.
retrieval-augmented generationmulti-scale modelingtool-calling iterationactive-learning screeninginteratomic potential
Broken Symmetry in LLM Refusal: Answer Release Is More Local Than Refusal Restoration
The study reveals a causal asymmetry in large language models (LLMs) between answer release and refusal restoration, termed broken symmetry. Using bidirectional activation patching in a controlled withhold setting, the authors demonstrate that correct answers remain linearly recoverable from hidden states even during refusal, with answer release requiring only single-position patching. In contrast, reimposing suppression demands multi-position interventions, and refusal sequence generation proves more complex. Results show that refusal is not a symmetric switch, and probe recoverability overestimates behavioral control, complicating safety auditing.
activation patchinglinear recoverabilityrefusal restorationcausal asymmetrybehavioral control
Temporal Graph Prototype-conditioned Conformal Prediction for Fraud Detection
The paper introduces ProtoCP, a conformal prediction framework for edge-level fraud detection on temporal graphs that addresses inefficiencies in existing methods. ProtoCP leverages learned prototypes to filter benign-dominated noise and employs neighborhood-relative scoring with temporal score diffusion for stable calibration under class imbalance. Evaluated on four benchmarks (YelpChi, S-FFSD, FTFD, BankSim), ProtoCP achieves target coverage with smaller prediction sets than state-of-the-art baselines.
conformal predictiontemporal graphsfraud detectionprototype learninguncertainty quantification
TinyCast: Probabilistic Zero-Shot Forecasting with Computed Periodicity
TinyCast introduces a parameter-efficient zero-shot forecasting model that computes periodic structure rather than learning it, using only 146,505 parameters. The method employs a zero-parameter spectral detector to identify dominant periods, folds the context on their phase, and utilizes a dilated convolutional encoder with a block-autoregressive quantile decoder. It achieves state-of-the-art probabilistic accuracy on the size-accuracy frontier, outperforming larger models on benchmarks like Chronos-ZS and fev-bench. Notably, it is the only sub-1.4M parameter model emitting a predictive distribution without test-data leakage. Its architecture enables efficient INT8 export and embedded device deployment.
zero-shot forecastingdilated convolutionspectral detectorblock-autoregressivepredictive distribution
Intent-Driven Situation Tracking for User-Centric Multi-Turn Agents
The paper introduces Intent-Driven Situation States (IDSS), a training-free framework for user-centric multi-turn agents that maintains an explicit situation state alongside dialogue. IDSS parses tool returns into provenance-aware entities, tracks user intents and constraints, and propagates facts to update action executability, enabling agents to avoid infeasible actions and reuse information efficiently. Experiments on three benchmarks with eight LLMs demonstrate improved task completion, preference elicitation, and interaction efficiency, particularly in multi-entity coordination and constraint-aware replanning tasks.
intent-driven situation statesmulti-turn agentsprovenance-aware entitiesconstraint modelingcontext management
Propaganda Forensics: Recovering the Generation Pipeline of an AI-Driven Influence Campaign
The study conducts a forensic analysis of an AI-driven influence campaign by introducing PROPAGIA, a corpus of 2,646 propagandist French articles from the Storm-1516/CopyCop campaign, compared to human-written SIPA articles. Using topic modeling, vagueness, and sentiment analysis, the authors identify persuasion techniques, finding PROPAGIA exhibits higher vagueness, subjectivity, and negativity with fewer cited sources. Prompt instruction leaks on 50 of 84 PROPAGIA websites reveal editorial specifications explaining these traits. Rewriting-based detection supports attribution to Llama 3 and suggests Mistral-family model involvement.
propaganda forensicstopic modelingsentiment analysisprompt instruction leaksrewriting-based detection
Toward AI-Friendly Cartography: Understanding How Color Design Influences Foundation Model Spatial Reasoning on Sequential Choropleth Maps
The study investigates how cartographic design choices affect foundation models' spatial reasoning on sequential choropleth maps, revealing that conventional human-centric principles largely apply to AI. Using a benchmark of 5,760 maps and 28,800 questions across five task types, the authors evaluate 21 multimodal foundation models. Results indicate that hue choice has minimal impact, while disrupted sequential ordering and reduced lightness contrast significantly impair performance, particularly in comparison and ranking tasks. Fine-tuning with LoRA improves accuracy but preserves these sensitivities, suggesting errors stem from color-legend decoding, spatial reasoning, and attribute-spatial integration.
choropleth mapsfoundation modelsspatial reasoninglightness contrastmultimodal evaluation
Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification
The study evaluates Concept-based Explainable AI (CXAI) methods for diagnosing model behavior in multi-label classification, using VGG16 and ResNet50 trained on 20 MS-COCO labels. Applying Concept Relevance Propagation (CRP) and Concept Recursive Activation FacTorization (CRAFT), the analysis reveals that CXAI exposes DNN learning weaknesses (1), shows reduced label/concept confusion with higher concept distinctiveness (2), and identifies dataset-induced biases via environmental concepts (3). Results demonstrate CXAI's utility for model interpretability and bias diagnosis.
concept-based xaimulti-label classificationconcept relevance propagationconcept distinctivenessdataset bias
FirstDiff: One-Step Diffusion-Based Anomaly Detection for Multivariate Time Series via Initial Noise Prediction
FirstDiff introduces a diffusion-based anomaly detection framework for multivariate time series that predicts anomalies from initial reverse-diffusion noise instead of completing full denoising. The method employs a Diffusion Transformer to model temporal and inter-sensor dependencies, validating noise distributions for single-step inference. Evaluated on five benchmarks, FirstDiff achieves state-of-the-art performance while reducing computational cost by eliminating iterative denoising.
diffusion modelsanomaly detectionmultivariate time seriesdiffusion transformernoise prediction
PLeDO: Pain Level Detection for Osteoarthritis from EMR Data
The study introduces PLeDO, an integrated pain level detection tool for osteoarthritis (OA) that analyzes both structured medication data and unstructured chart notes from Electronic Medical Records (EMR). The method combines SPaDe, a synonym-based NLP tool for pain expression classification, with medication and pain scale data to categorize patients into mild or moderate-to-severe pain levels. Evaluation on human-labeled gold standard data demonstrates effective pain level detection, potentially improving primary care quality for OA patients.
osteoarthritiselectronic medical recordsnatural language processingpain level detectioninformation extraction
Beyond Single Object: Learning 3D Relations with Large Language Models
The paper introduces a framework for multi-object 3D relational reasoning, addressing limitations in existing 3D-LLMs that focus on single-object descriptions. The method comprises three components: MO3D, a dataset for fine-grained multi-object comparison; Multi-3DLLM, featuring a Patch-Interaction Transformer (PIT) to model inter-/intra-object relationships while preserving local geometry; and two application benchmarks (Shape Mating, Change Captioning). Results show Multi-3DLLM outperforms baselines on MO3D and demonstrates positive transfer to single-object classification, highlighting improved geometric reasoning capabilities.
3d-llmsmulti-object reasoningpatch-interaction transformergeometric understandinginstruction dataset
HyMem: Hierarchical Context Management for Long-Horizon Agents via Information Isolation
HyMem introduces a hierarchical context management framework for LLM agents to improve long-horizon task performance by isolating functional context layers. It separates high-level planning from execution via distinct modules: an isolated reasoning component for subtasks and a memory management system for structured summaries. Evaluated on GAIA and Browsecomp-plus with DeepSeek-V4, HyMem achieves Pass@1 scores of 66.7% and 61.3%, outperforming baselines by 6.1 and 4.7 percentage points, respectively, while controlling context growth.
hierarchical contextlong-horizon agentsinformation isolationmemory managementreasoning traces
Adaptive Mixing of Policies from Searching and Policies from Learning
Flexer introduces an adaptive policy mixing method that dynamically combines neural network policies with Monte Carlo tree search (MCTS) policies based on policy imitation error and environment model variance. The approach reduces search depth proportionally to the quality of policy network priors, optimizing computational efficiency. Experiments on three toy symbolic problems demonstrate Flexer's superiority over AlphaZero, DQN, and ADP variants.
policy distillationmonte carlo tree searchadaptive mixingreinforcement learningpolicy imitation error
RRFC: Recursive Refinement via Feedback Conditioning for Iterative Image-to-Image Generation
The paper introduces Recursive Refinement via Feedback Conditioning (RRFC), a framework for iterative image-to-image generation that enables models to refine outputs by conditioning on their own previous predictions. RRFC modifies existing generators' conditioning interface without architectural changes, feeding back the last prediction as auxiliary input channels. Evaluated across 18 architecture-task combinations (6 baselines, 3 tasks), RRFC shows significant improvements in 7 cases (primarily reconstruction-fidelity/identity tasks) but degrades performance in 7 others (concentrated on semantic-layout tasks), demonstrating task-dependent utility of feedback-based refinement.
iterative refinementfeedback conditioningimage-to-image translationgenerative modelsoutput adaptation
Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deployment
The paper empirically analyzes edge AI deployment tradeoffs across compression techniques, revealing task-dependent performance characteristics. Through systematic evaluation of quantization, pruning, and LoRA on GPU/CPU/Raspberry Pi for question answering (Qwen3.5 0.8B) and segmentation tasks, it demonstrates quantization preserves 93.85 SQuAD F1 but pruning reduces segmentation model size by 80% with minimal mIoU loss. Key findings include pruning-induced 3.4x latency spikes from k-quant misalignment and LoRA's hidden accuracy collapse despite 71% BoolQ performance. Neural-flow graphs and latency decomposition provide mechanistic explanations, yielding task-specific deployment guidelines.
model compressionedge deploymentquantizationstructured pruninglatency decomposition
Adding Voice Cloning to Text-to-Audio-Video Models with a Single Zero-Initialised Layer
The paper demonstrates that text-to-audio-video (T2AV) models can be augmented with voice cloning capabilities by adding a single zero-initialized linear layer to the audio backbone, fine-tuning briefly, and conditioning on reference recordings. The method injects reference signals via diffusion latents prepended to the audio stream and a global speaker embedding modulating target audio tokens. Evaluated on 674 speaker-text pairs (30 speakers), the enhanced 5B-parameter model achieves statistically significant improvements in speaker-encoder cosine similarity (SECS) over five text-to-speech baselines across three verification networks (ECAPA-TDNN, WavLM-SV, Resemblyzer). The architecture also enables audio-only inference, yielding a 30× speed-up over full diffusion.
text-to-audio-videovoice cloningzero-initialized layerspeaker embeddingdiffusion latents
Integrating Persuasion Theory into the Epidemiological Modelling of Health Misinformation Spread on Social Media
The study introduces ELM-SIRMMM, a hybrid epidemiological-behavioral model for health misinformation spread on social media, extending the SIR framework with three misinformed compartments (MS, MI, MR) and integrating Elaboration Likelihood Model (ELM) signals (sentiment, engagement, cognitive effort) to modulate transmission rates. Evaluated on FibVID, MC-Fake, and Monant datasets, the model reduces RMSE by 5.5%, delays misinformation peaks (e.g., from day 150 to 160), and increases peak prevalence (6% to 7%), demonstrating superior dynamic realism and predictive accuracy when psychological inputs vary significantly.
epidemiological modelingelaboration likelihood modelmisinformation spreadsirmmm frameworkbehavioral dynamics
THESIS-MoE: Trainable Hierarchical Extraction and SteerIng of Sycophancy in Mixture-of-Experts
The paper introduces THESIS-MoE, a method for trainable hierarchical steering to mitigate sycophancy in Mixture-of-Experts (MoE) language models while preserving knowledge retention. It localizes sycophantic behavior via causal search across MoE blocks, experts, attention blocks, and heads, comparing unconditional subtraction with conditional alternatives: analytic projection-based subtraction and learned per-token gating. Evaluations on three MoE models show up to 90% reduction in belief-induced sycophancy without compromising general knowledge or reasoning benchmarks, demonstrating precise behavioral steering in identifiable subcircuits.
sycophancymixture-of-expertsactivation steeringcausal searchbehavioral intervention
Robo-Dopamine 2.0: History-Conditioned and OOD-Aware Process Reward Modeling for Robotic Manipulation
Robo-Dopamine 2.0 introduces a history-conditioned and OOD-aware process reward model for robotic manipulation, addressing temporal ambiguity and OOD robustness in VLA policy refinement. The method combines (1) history-conditioned pairwise rewards using reference panels for synthetic OOD queries and observed rollout history, and (2) an OOD-aware signed progress space. A Signed-Hop Curriculum with transition-aware replay enables coarse-to-fine progress learning. Evaluations show improved visual order consistency (VOC) from 0.967 to 0.986 and OOD-robust VOC from 0.906 to 0.958. Downstream RL achieves 86.8% RoboTwin success and 71/80 real-world insertions.
vision-language-action modelsprocess reward modelingout-of-distribution robustnesspairwise predictionsigned-hop curriculum
PL-Guard: Probabilistic Logic Reasoning for LLM Guardrails
PL-Guard introduces a neurosymbolic architecture for LLM guardrails that separates semantic grounding from policy reasoning to improve safety and auditability. The method employs a symbolic policy interface with predicates and ProbLog rules, where a local LLM grounds prompt-response pairs into predicate probabilities using token scores, and ProbLog performs probabilistic rule inference. On XSTest, PL-Guard with a hand-curated policy reduced unsafe compliance from 22.0% to 0.5% compared to the base model, though with higher over-refusal (14.4% vs. 5.2%) than an LLM-as-a-judge baseline.
neurosymbolicguardrailsproblogpredicate probabilitiesxstest
A Responsible Artificial Intelligence Framework for Groundwater Modeling
The paper proposes a Responsible AI framework for groundwater modeling, introducing six principles: transparency, technical robustness, privacy governance, fairness, accountability, and sustainability. Using multi-source hydrometeorological data from the Heihe River Basin, the authors develop and compare LSTM and Transformer time-series models, evaluating them via post-hoc interpretability, Monte Carlo simulation, and scenario analysis. Results indicate Transformer outperforms LSTM in accuracy, robustness, and interpretability, demonstrating the framework's practical utility for sustainable water management under climate change and anthropogenic pressures.
responsible aigroundwater modelingtransformerlstminterpretability
Q-based Variational Inverse Reinforcement Learning
Q-based Variational Inverse Reinforcement Learning (QVIRL) introduces a novel Bayesian IRL method that infers reward functions from expert demonstrations by learning a variational distribution over optimal Q-values. Unlike prior approaches, QVIRL combines scalability with uncertainty quantification, crucial for safety-critical applications and active learning. The method demonstrates strong performance in apprenticeship learning across diverse tasks, including gridworlds, Lunar Lander, the Highway Environment, and ATARI games, using both static expert data and active learning. QVIRL is the first Bayesian IRL method capable of training from raw pixel observations.
bayesian irlvariational distributionq-valuesactive learninguncertainty quantification
Spectral Gaps of Hit-and-Run and Coordinate Hit-and-Run
The paper establishes a spectral gap of Ω(1/(n² C_PI)) for Hit-and-Run Markov chains on convex bodies containing a unit ball, where C_PI is the Poincaré constant of the uniform distribution. This refines prior bounds by Lovász and Vempala (2004) by replacing dependence on outer radius R with C_PI, yielding improved O(n² log n log(M/ε)) complexity for nearly isotropic bodies via connections to the KLS conjecture. The analysis employs functional isoperimetric constants and dual certificates, diverging from conductance-based proofs. Similar techniques improve Coordinate Hit-and-Run mixing to O(n³ C_PI log(M/ε)).
spectral gaphit-and-runpoincaré constantmarkov chainisoperimetric constant
An Analytical-Prior Framework for Data-Efficient Prediction of Sound-Reduction Frequencies in Rectangular Side-Branch Helmholtz Resonators
This study introduces an analytical-prior learning framework to enhance data efficiency in predicting sound-reduction frequencies for rectangular side-branch Helmholtz resonators under limited high-fidelity simulation budgets. The framework employs two approaches: explicit analytical-to-simulation discrepancy learning when the analytical model is available at inference, and analytical-prior pretraining followed by calibration when a self-contained predictor is needed. Evaluated on 86 simulation-labelled and 8,998 analytical-only geometries, residual SVR achieved a 0.426 Hz MAE (vs. 3.375 Hz for direct SVR), while analytical-prior pretraining reduced MLP error to 0.371 Hz (vs. 1.109 Hz for direct MLP). Both methods consistently outperformed direct learning across training budgets of 20-70 simulation-labelled cases.
analytical-prior learninghelmholtz resonatorsdata efficiencysimulation discrepancyresidual adaptation
Data-Efficient and Interpretable Classification of Circulating Tumor Cell Phenotypes in Microfluidic Devices via Deep Learning
The authors propose an interpretable deep learning framework for classifying circulating tumor cell (CTC) phenotypes from microfluidic trajectory data, addressing data scarcity and interpretability challenges. Their method introduces Subsequence (SubSeq), a targeted augmentation strategy that extracts informative local trajectory segments during training, combined with Gradient Weighted Class Activation Mapping for model interpretability. Experiments show SubSeq improves classification accuracy over baselines while revealing that localized trajectory segments contain sufficient biophysical information, suggesting redundancy in full-length trajectories. The framework treats microfluidic geometries as physical encoders of cellular properties, offering insights for diagnostic device design.
circulating tumor cellsmicrofluidic devicesdata augmentationinterpretable deep learningbiophysical classification
Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
The paper proposes Censored Non-crossing Quantile (CNQ), a deep learning framework for distributional survival prediction that jointly estimates multiple conditional survival quantiles while guaranteeing non-crossing curves by construction. The method employs Kolmogorov-Arnold and Transformer architectures to model flexible covariate effects across quantile levels, with theoretical guarantees including a finite-sample excess-risk bound. Evaluated across 27 simulations and 6 real-world cohorts, CNQ outperforms quantile-, hazard-, and tree-based methods in pinball loss under asymmetric distributions and achieves better interval coverage, demonstrating clinically interpretable quantile-varying effects in breast cancer (METABRIC) and mortality (FLCHAIN) datasets.
quantile regressionsurvival analysisnon-crossing constraintsright-censored datadistributional prediction
The canonical facets of multi-separator polytopes
The paper initiates a polyhedral study of the graph multi-separator problem, proposed as an alternative to the lifted multicut problem for image segmentation. Using an integer linear program (ILP) formulation, the authors characterize all facets of the multi-separator polytope via efficiently-decidable graph-theoretic conditions, strengthen these inequalities, and describe additional facets. For paths with all vertex pairs considered, they provide a totally dual integral description. The work also relates the multi-separator polytope to the boolean quadric polytope and the lifted multicut polytope, showing projection relationships between them.
multi-separator polytopeinteger linear programtotally dual integralboolean quadric polytopelifted multicut
zLend: A Dual-Scope Cash-Flow Reconstruction Framework for On-Chain Credit Underwriting
zLend introduces a dual-scope cash-flow reconstruction framework for on-chain credit underwriting, addressing decentralized lending's lack of traditional credit bureaus. The method reconstructs daily balance histories from raw token transfers, analyzing both stablecoin-only and all-fungible-transfer views to derive liquidity coverage, cash-flow volatility, drawdown-recovery statistics, and recurring-counterparty detection. Results show tier assignment is loan-size dependent, with distinct criteria (coverage vs. drawdown) binding on different wallets. The system achieves numerical tolerance 1e-9 in cross-language validation and is deployed in production via third-party APIs.
on-chain creditcash-flow reconstructionliquidity coveragestablecoin baskettier assignment
Time-Aware Validation of Machine Learning Fuel Consumption Models: Evidence from 1\,Hz Operational Data, CCGS \textit{Sir Wilfrid Laurier}
The study identifies temporal leakage as a critical flaw in conventional validation of ship fuel consumption (SFC) models and proposes time-aware evaluation methods. Using 3.88M 1Hz operational records from CCGS Sir Wilfrid Laurier, it compares six regression models and a physics baseline under Time Series Cross-Validation (TSCV) and Blocked TSCV (BTSCV) schemes with three feature configurations. Results demonstrate that traditional random splits yield optimistic performance estimates, while time-aware validation better reflects real-world deployment conditions.
ship fuel consumptiontemporal leakagetime series cross-validationoperational dataregression models
GEO-Flag: Detecting and Measuring GEO-Optimized Web Content
The paper introduces GEO-Flag, a method for detecting GEO-optimized web content that manipulates generative search engine rankings. It presents GEOFlagBench, a benchmark of 3,200 webpages across 400 queries, and evaluates baseline detection methods (best F1=0.880), revealing weaknesses like authorship bias. The proposed Intervention-Paired Training (IPT) improves ModernBERT's F1 to 0.944 and worst-group accuracy to 0.883. A GEO-gated Agent audits citation verifiability, and real-world deployment estimates 8.90% GEO prevalence (16.36% in 2026-modified pages).
generative engine optimizationintervention-paired trainingsource tier auditingcitation verifiabilitygeoflagbench
Unsupervised Learning of Cell Instances with Generative Routing Pyramids
The authors propose an unsupervised method for cell instance segmentation and phenotypic classification from unlabeled microscopy images, eliminating the need for manual annotations. Their approach reconstructs images using a coarse-to-fine routing pyramid that associates pixels with spatially sparse latent sources, simultaneously generating instance masks (via pixel-to-latent associations) and encoding cell morphology (via source latents). The method demonstrates competitive performance in instance segmentation across diverse cell morphologies and imaging modalities, and enables generative modeling of cellular phenotypes under perturbations.
unsupervised learninginstance segmentationgenerative modelingrouting pyramidcell morphology
Beyond $L_2$: Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architectures
This work generalizes Abductive Latent Explanations (ALE) to support non-Euclidean prototype architectures, addressing a critical gap in formal explanation methods. The authors systematically derive methods to map diverse geometric variants (spherical metrics, Gaussian densities, dimensional projections) to existing bounds or construct novel bounding algorithms. Validation is performed by computing subset-minimal formal explanations on fully trained image classifiers. The framework enables rigorous cross-architecture comparison of interpretability, unifying diverse models under a single formal explanation paradigm.
abductive latent explanationsnon-euclidean architecturesformal explanationsprototype-based networksinterpretability
On the Principles Behind Neural Network Optimizers
This work provides a principled analysis of Adam's optimization dynamics, revealing a batch-size-dependent phase transition between convergence and divergence regimes. Through Hessian structure analysis, it demonstrates that Adam's diagonal preconditioner becomes effective in Transformers due to evolving near-block-diagonal Hessian matrices with strong block heterogeneity, traced to consecutive multiplications of large matrix variables. The insights yield Adam-mini, a memory-efficient variant reducing Adam's footprint by 50% without performance loss, while also advancing understanding of matrix-based nonconvex optimization and recent optimizers like Muon.
adam optimizerhessian structureblock-diagonal preconditioningrandom matrix theorytransformer optimization
Le Critique: Privileged Value Functions for LLM Reinforcement Learning
The paper introduces Privileged Value Functions (PVF) and TETHER to enhance value function-based reinforcement learning for Large Language Models (LLMs). PVF injects task-relevant token-level signals without biasing policy objectives, while TETHER dynamically interpolates between group-relative and value baselines based on value function accuracy. Evaluated on reasoning tasks, these methods outperform standard value function baselines and match or exceed mean-baseline GRPO performance, addressing gradient variance and throughput limitations in LLM RL pipelines.
privileged value functionstetherreinforcement learninglarge language modelsgradient variance
The Ethical Decision Head: Operationalizing Normative Ethics in Autonomous Vehicles via Reinforcement Learning from Human Feedback
The paper introduces the Ethical Decision Head (EDH), a deep reinforcement learning framework for encoding ethical reasoning in autonomous vehicles via differentiable reward signals. Using Proximal Policy Optimization (PPO), EDH instantiates Utilitarian and Kantian normative frameworks, trained on human preference annotations from 200 collision-imminent scenarios in CARLA. Results show humans reward self-sacrifice over casualty minimization, revealing a divergence between theoretical ethics and learned behavior through RLHF.
autonomous vehiclesreinforcement learningnormative ethicsproximal policy optimizationhuman feedback
Learning to Price with Persuasion
The paper introduces a learning-theoretic model for pricing with persuasion, addressing scenarios where sellers leverage user profiles to optimize revenue through both mechanism and information design. It extends Bergemann et al. (2022) by relaxing the assumption of known buyer beliefs, studying sample complexity in batch and online query settings, and providing the first FPTAS for revenue-maximizing schemes. Results include theoretical guarantees for learning optimal signaling schemes under partial information, resolving an open problem in asymmetric information settings.
mechanism designinformation designsignaling schemesample complexityfptas
Hide&Seek: Learning to Explain in an End-to-End Differentiable Network
Hide&Seek introduces an end-to-end differentiable model for instance-wise feature selection, jointly learning feature selection and prediction under a single objective to prevent information leakage. The method reformulates feature removal as a differentiable operation by replacing a proportion of each feature rather than discrete removal, and employs a parsimony-weight annealing framework to stabilize training. Experiments demonstrate that Hide&Seek outperforms state-of-the-art models across various benchmarks and achieves faster training times.
instance-wise feature selectiondifferentiable operationinformation leakageparsimony-weight annealingend-to-end learning
Random Quadratic Form with random forcing: Metastable synchronization by noise
The paper demonstrates that random Brownian forcing induces full synchronization in the Random Quadratic Form (RQF) on a sphere, contrasting with partial synchronization in the unforced case due to intrinsic symmetries. Analyzing the small-forcing regime, the authors identify a two-stage multiscale behavior: initial convergence to an anti-polar configuration followed by symmetry-breaking cluster merging. The study connects to continuous-time machine learning models, elucidating the role of bias initialization scale in Neural ODEs and transformer formulations.
random quadratic formbrownian forcingsynchronizationneural odessymmetry breaking
Turning spectra into images improves plant trait retrieval with 2D-CNNs
The study demonstrates that transforming 1D hyperspectral plant spectra into 2D images improves trait prediction accuracy using 2D-CNNs. Evaluating nine transformations on the GreenHyperSpectra dataset (7,897 spectra, eight traits), a simple Reshape operation achieved R²=0.684, outperforming 1D-CNN baselines by +0.097. Pretraining a 2D masked autoencoder (MAE-2D) on 139K unlabeled spectra yielded R²=0.646 via linear probing, surpassing all 1D self-supervised methods. Cross-dataset evaluation showed limited generalization. Integrated Gradients revealed wavelength importance aligned with PROSAIL simulations for protein (r=0.45) and water (r=0.33), but not carotenoids (r=0.06).
hyperspectral reflectance2d-cnnmasked autoencoderplant functional traitsintegrated gradients
The Working Set of a Coding Agent: Coherence Debt in Repository-Scale Tasks
The paper introduces the concept of 'coherence debt' in repository-scale coding tasks, where missing facts from context or parametric memory lead to systematic failures. The authors model coding as reconstructing a coupled-fact graph and evaluate seven models across five harnesses by manipulating fact availability. Results show that failure modes are consistent across models when facts are missing, with agents fabricating incorrect solutions rather than abstaining. Harness efficiency varies tenfold in token usage, and stale conventions incur higher costs than missing files. The study highlights that success in benchmarks like SWE-bench may overestimate real-world performance due to parametric memory effects.
coherence debtcoupled-fact graphparametric memoryrepository-scale codingswe-bench
Variational Outlier-Robust Gaussian Process Regression with Generative Modeling
The article introduces a generative Gaussian process regression (GPR) model robust to outliers by capturing observation-specific contamination and adaptively mitigating their influence. The method employs a variational generalized expectation-maximization procedure to learn latent variables and GPR parameters. Evaluations on synthetic and real datasets under various contamination settings show the proposed method achieves competitive or superior prediction accuracy compared to robust GPR baselines, while maintaining cubic computational complexity.
gaussian process regressionoutlier robustnessvariational inferencegenerative modelingexpectation-maximization
Learning Generalizable Reconstruction of High-Dimensional Neural Dynamics
The authors propose PCA-DMD, a scalable operator-theoretic framework for reconstructing high-dimensional neural dynamics from local field potentials (LFPs). The method segments LFPs into overlapping windows, projects them into a compact PCA space, learns linear Koopman evolution in the latent space, and reconstructs signals via inverse projection and overlap-add aggregation. On 200,000-sample hippocampal recordings, PCA-DMD outperformed Classical DMD, SpDMD, MrDMD, and HODMD (KLD=0.0761, HD=0.0847), achieving 0.9504-0.9800 cross-subject correlations without fine-tuning. The framework demonstrated stable zero-shot reconstruction up to 900,000 samples (mean correlation ~0.965-0.968) and validated on 93-channel Neuropixels data (mean correlation 0.7427).
koopman operatorlocal field potentialsdimensionality reductionzero-shot generalizationneural dynamics
Supervising the Path to Fine Scales: GalerkinFlow for Scientific-Field and Image Super-Resolution
The paper introduces GalerkinFlow, a super-resolution framework that supervises the entire reconstruction path between coarse and fine scales rather than just the final output. The method predicts coarse-to-fine residual velocities at intermediate states, using a pseudo-endpoint loss derived from a time-dependent weight, and incorporates additional supervision via coarse-anchor points and finite-difference constraints. GalerkinFlow combines convolutional features with scale-conditioned Galerkin operator mixing, achieving state-of-the-art errors on Navier-Stokes and Darcy Flow benchmarks while remaining competitive on DIV2K without requiring physical equations or metadata.
super-resolutiongalerkin operatorresidual velocityintermediate supervisionfinite-difference objective
One Residual with Three Reuses: A Wristband Front End for Gesture Sensing
The paper presents a wristband front-end design for continuous gesture sensing that shares a single on-chip residual generator across three functions: classifier wake-up gating, mmWave/IMU routing, and EKF measurement reweighting. The system combines a MEMS IMU with 60 GHz FMCW radar for occlusion robustness, implemented on an edge MCU (14.4 KB program memory, 278 B state, 110K MACs/frame). Evaluated on four datasets (IPN Hand, SHREC 2021, MiliPoint, EAT-Radar), it achieves PD=0.72/0.80 at 1% FAR, reduces classifier energy by 47% at 90% recall, and improves tracking RMSE 4.6× under bias drift versus an adaptive Kalman baseline.
residual generatorfmcw radarinertial measurement unitedge mcugesture sensing
Automating Learner Assessment: Benchmarking Machine Learning and Deep Learning Models for EEG-Based Familiarity Prediction
This study benchmarks 15 machine learning and deep learning models for EEG-based familiarity prediction across two cognitive domains (faces and mathematical equations), establishing realistic performance metrics for educational technology applications. Using continuous EEG data from 23 participants, spectral features were extracted across six frequency bands and evaluated with both stratified cross-validation and trial-independent Group K-Fold validation. Results show that while stratified validation yields artificially high performance (up to 0.9853 F1-score with CNN), trial-independent validation reveals more realistic metrics (peak 0.6038 F1-score with CNN), significantly above chance. Feature importance analysis identifies temporal and frontal Gamma and Beta oscillations as key biomarkers for familiarity.
eeg-based familiarity predictiontrial-independent validationspectral feature extractiongamma oscillationsbeta oscillations
Data-Driven Reconstruction of Spatially Resolved Electron and Ion Energy Distributions from Macroscopic Plasma Quantities with Deep Neural Networks
The work demonstrates a deep learning framework for reconstructing spatially resolved electron and ion energy distribution functions (EEDFs/IEDFs) from macroscopic plasma observables in low-temperature plasmas. Using paired datasets from 2D-3V PIC-MCC simulations, three architectures (U-Net, FNO, MeshGraphNet) learn the inverse mapping, with FNO achieving best performance. Reconstructed EDFs accurately recover density, temperature, and rate coefficients, validating their physical consistency and enabling surrogate kinetic modeling.
eedf/iedf reconstructionpic-mcc simulationsinverse mappinglow-temperature plasmasphysics-based validation
LLMs for Zero-Shot Threat Detection via Structured Risk Indicators
The paper proposes a two-stage LLM framework for zero-shot threat detection that first generates structured risk indicators from security logs before temporal classification. The method models user activity timelines with retrieval-augmented generation (RAG) for behavioral context and evaluates on CERT r5.2 and PicoDomain datasets. Results show 11.40-31.50 percentage point F1-score improvements over prior LLM approaches (GABM), with retrieval benefiting weaker models more and optimal LLM assignment varying by dataset.
retrieval-augmented generationzero-shot detectionstructured risk indicatorstemporal classificationinsider threat detection
Density-Reweighted Entropic Optimal Transport: Decoupling Geometry from Sampling Density
The authors propose density-reweighted entropic optimal transport (EOT) to address geometric distortion in dataset alignment caused by sampling density disparities. Their method decouples geometric proximity from density effects by introducing a reweighting parameter that controls density influence, spanning from standard EOT to purely geometry-driven alignment. Theoretical analysis shows convergence to population-level plans with explicit density dependence, while simulations demonstrate improved geometric correspondence under substantial density differences compared to existing EOT approaches.
entropic optimal transportdataset alignmentsampling densitygeometric proximitytransport plan
When Tool-Backed Skill Retrieval Fails: Source-Style Collapse in Executable Capability Retrieval
The paper identifies source-style collapse, a failure mode in tool-backed skill retrieval where fine-tuned retrievers perform poorly on source-specific slices despite lexical overlap. It proposes ToolScout, a source-aware routing method using TF-IDF fingerprints to detect mismatches, improving coverage from 22.3% to 86.1% on a 4,996-query benchmark. Results show consistent failure patterns across API-schema and skill-card representations.
tool-backed retrievalsource-style collapseexecutable skillstf-idf routingcapability coverage
Improved Regret Analysis for Parallel Gaussian Process Bandit Optimization
The paper improves regret analysis for parallel Gaussian process (GP) bandit optimization by eliminating the need for an initial uncertainty sampling phase in GP batched Thompson sampling (GP-BTS). It demonstrates that existing multiplicative regret bounds with respect to batch size $Q$ can be avoided without this phase, contrary to prior analyses. Additionally, it establishes superior regret upper bounds in noiseless settings compared to noisy settings, mirroring results from sequential GP bandit optimization.
gaussian processbandit optimizationregret analysisthompson samplingbatch size
Pallas: A Proactive KV Cache Migration Framework for LLM Inference in AI-RAN
Pallas introduces a proactive KV-cache migration framework for LLM inference in AI-RAN, addressing service interruption during cellular handover. The method partitions token sequences into stable prefixes (reconstructed via local prefill) and evolving suffixes (streamed as KV blocks), enabling parallel preparation at the target base station before handover. An online scheduler optimizes the prefetching window using mobility predictions. Evaluated on three LLMs with 100–500 Mbps inter-gNB links, Pallas reduces service interruption time by 2.28–89.68× versus target-side recovery and lowers inter-token latency by 16.0–50.0% compared to source-side forwarding.
kv-cache migrationllm inferenceai-raninter-token latencyprefetching window
Reference-free logged energy-oracle recovery for neural approximations of symmetric coercive variational problems: conforming Riesz reconstruction and archive-level selection
The paper introduces a reference-free selection rule for neural approximations of symmetric coercive variational problems, based on minimizing a computable conforming Riesz monitor. The method leverages exact residual-energy identity and conforming projection to provide unconditional lower bounds and, under saturation, computable upper estimates, forming a lower-upper bracket. Key findings include archive selection's order-sensitivity and uniform recovery for finite archives, ensuring convergence to logged-oracle error. Experiments on diffusion and elasticity problems demonstrate energy-scale calibration and oracle-level selection with modest post-processing costs.
neural pdeconforming riesz monitorvariational problemsenergy-error scalearchive selection
Self-Supervised Noise2Noise-Enhanced Denoising for Continuous-Scan Air-Plasma THz Spectroscopy
The authors propose a self-supervised denoising method for continuous-scan air-plasma THz spectroscopy, combining a reference-supervised 1D residual U-Net with a Noise2Noise approach to reduce measurement time. The method learns from either noisy-reference pairs or pairs of noisy traces without clean targets, achieving a 5.4× trace-reduction factor when combined (versus 4.9× for Noise2Noise alone and 3.2× for Wiener filtering). Results demonstrate improved signal-to-noise ratio from single-scan traces, enabling faster THz-TDS without hardware changes.
terahertz time-domain spectroscopynoise2noiseresidual u-netcontinuous-scanself-supervised learning
Localized TabICLv2: Scaling Tabular In-Context Learning through k-NN
Localized TabICLv2 reduces the inference cost of TabICLv2 by employing k-NN retrieval in the model's Stage 2 row-representation space, avoiding full-context attention. The method maintains accuracy through additional Stage 2 and Stage 3 fine-tuning without architectural modifications. On TabArena classification tasks, it retains 98.64% of full-model accuracy while achieving a 2.18× median batch inference speedup and 249× median single-query speedup.
tabular in-context learningk-nearest neighborsrow-representation spaceattention costbatch inference
Evolving Executable Pipeline Programs for AutoML with Language Models
LACE introduces a novel AutoML framework that evolves executable Python pipeline programs through an evolutionary loop with a large language model (GPT-5.4-mini) as variation operator, enabling search over complete scikit-learn-compatible classes rather than predefined component spaces. Evaluated on 68 OpenML classification tasks under leakage-controlled protocols, LACE matches AutoGluon's performance while outperforming auto-sklearn and H2O, offering full benchmark coverage and editable pipelines without framework constraints. The approach uniquely combines evolutionary search with LLM-generated code, providing practitioners with inspectable, reusable pipelines and extensibility through prompt engineering rather than framework modification.
automlevolutionary searchlanguage modelsscikit-learntabular classification
TRACE-CASH: Trial-History-Conditioned Reinforcement Learning for Adaptive Configuration Exploration in Time-Series CASH
TRACE-CASH introduces a trial-history-conditioned reinforcement learning method for adaptive configuration exploration in time-series combined algorithm selection and hyperparameter optimization (TS-CASH). The approach combines grouped actor-critic candidate generation with fixed rules for model coverage, validation-guided exploitation, and exploration after stalled progress. Evaluated against six alternatives across 41 dataset-frequency tasks, TRACE-CASH achieves the lowest mean rank on MASE and WQL metrics, with the lowest window-averaged test-MASE rank in predefined full and late windows.
time-series cashactor-critichyperparameter optimizationmodel coveragevalidation-guided exploitation
SoftModel: A Neural Model That Grows Its Own Topology -- Governed Structural Growth for Continual In-Service Learning
The paper introduces SoftModel, a neural architecture with lifelong structural plasticity governed by an algebra of audited growth operators (width, hierarchy, composition, etc.) and a reality gate for change adoption. The system enables continual in-service learning by dynamically growing topology based on demand, with stability ensured through governance rather than fixed structure. Evaluated on standard continual-learning benchmarks, the approach maintains learning capability over long task sequences, though pre-registered experiments reveal limitations in predicting marginal capacity value ex-ante.
continual learningstructural plasticityreality gategoverned growthcapacity demand
POI Recommendation with LLM-Augmented Multi-Graph Learning and Contrastive Alignment
The paper proposes LLM-MGCL, a multi-graph neural network for POI recommendation that augments LightGCN with semantic and geographic item-item graphs derived from LLM-generated text and spatial data. The model propagates embeddings over three parallel graphs (collaborative, semantic, geographic), fuses them additively, and aligns representations via bidirectional InfoNCE contrastive learning. On Yelp Multimodal Recommendation Dataset, LLM-MGCL improves Recall@20 by 52.0% and NDCG@20 by 64.8% over LightGCN, with cross-view contrastive alignment identified as the primary performance driver.
multi-graph learningcontrastive alignmentpoint-of-interest recommendationcold-start problemlightgcn
Mint-Agent: Introducing Finance-Native Agentic Foundation Models
The paper introduces Mint-Agent, a family of finance-native agentic foundation models designed for reliable and executive financial intelligence. The approach combines three components: a data engine for task construction, MintHarness for environment interaction and evidence trails, and a training recipe integrating SFT, OPD, and RLVR to develop specialized experts merged via model merging and multi-teacher distillation. The resulting models, Mint-Cu (9B) and Mint-Ag (27B), achieve 98.33% on RFC-Bench and 76.00%/60.49% on FinanceAgentBench v1.1/v2, outperforming GPT-5.6-Sol and Claude-Opus-4.8 by 3.66 and 3.00 points respectively.
agentic modelsfinancial intelligencemodel mergingmulti-teacher distillationlong-horizon execution
FETERS: Few-Shot Early Time-Series Classification via Effective Ratio Selection
FETERS introduces a few-shot early time-series classification framework that eliminates the need for training a sample-level stopping module by selecting a dataset-level stopping ratio via class-wise leave-one-out evaluation on the support set. The method combines Rocket-based features with frozen Chronos representations and employs a penalty-based reward function to balance accuracy and earliness. Evaluated on 69 datasets across 14 domains, FETERS achieves state-of-the-art performance in the 5-shot setting, with the highest average harmonic mean and top performance on 38 datasets, while remaining competitive in full-shot scenarios.
early time-series classificationfew-shot learningstopping ratiorocket-based featureschronos representations
Self-Routed Tensor Adapters for Parameter-Efficient Universal Visual Adaptation
The paper introduces Self-Routed Tensor Adapters (SRTA), a parameter-efficient framework for multi-domain visual adaptation of frozen foundation models. SRTA projects inputs into a low-rank space, computes routing weights via a learnable domain matrix, and blends slices of a shared Tucker core to produce sample-specific adaptation matrices without external gating. A progressive depth-weighted routing objective supervises pathway learning across layers. Evaluated on five multi-domain classification benchmarks, SRTA achieves comparable or better accuracy than MoE-style PEFT baselines (e.g., 2.77M vs. 9.52M parameters at rank 64 in 4-domain settings) while using significantly fewer parameters.
parameter-efficient fine-tuninglow-rank adaptationtucker decompositionmulti-domain learningvisual foundation models
Architecture-Dependent Causal Transfer of Activation States Across Large Language Models
The study demonstrates architecture-dependent causal transfer of activation states between large language models (LLMs) via learned projections, bypassing natural language intermediaries. Using four diverse models (Qwen2-0.5B, Phi-3-mini, Mistral-7B, FLAN-T5-base), the authors evaluate representational alignment via mutual k-nearest-neighbor metrics, cross-model retrieval (45-50% top-1 accuracy for decoder-only pairs), and causal transfer through activation injection. Results show significant causal effects only for Qwen2-0.5B to Phi-3-mini transfer (23.3% output similarity, p=0.047), suggesting transfer operates on representational vehicles rather than semantic content, with strong architectural constraints.
activation transferrepresentational alignmentcausal inferencellm architecturescross-model retrieval
Task-Anchored Representation Shaping for Pre-Trained Model-Based Continual Learning
The paper proposes Task-Anchored Inference Latent Shaping (TAILS), a lightweight post-PTM module for continual learning that resolves cross-task ambiguity at the representation level. TAILS uses fixed task anchors as persistent references, interprets sample features relative to these anchors, and composes evidence into latent recall to correct representations before prediction. Experiments across multiple PTM-based continual learning paradigms show TAILS improves classification and task-inference performance with minimal parameter overhead and negligible inference cost.
continual learningpre-trained modelsrepresentation shapingtask anchorslatent recall
LiD-GLM: Lipschitz-constrained Deep Generalized Linear Models
LiD-GLM introduces a hybrid model combining generalized linear models (GLMs) with invertible residual networks (i-ResNets) to balance interpretability and flexibility. The method enforces Lipschitz constraints on i-ResNets to control deviation from traditional GLMs while enabling nonlinear parameter estimation and distributional correction. This approach preserves stochastic monotonicity and allows user-specified trade-offs between model interpretability and flexibility, supported by post-hoc orthogonalization for identifiability.
generalized linear modelsinvertible residual networkslipschitz constraintstochastic monotonicitypost-hoc orthogonalization
Beyond Binary Priorities: Multi-Tier SLA Scheduling for Large Language Model Serving
The work extends Llumnix's LLM serving scheduler from binary to multi-tier priority support for heterogeneous SLOs, implementing tier-aware dispatch and headroom management in Vidur's simulation framework. Evaluation against INFaaS, vLLM, Orca, and Sarathi-Serve shows optimal performance at four priority tiers, achieving 8.3x prefill speedups and 3.1x P99 latency improvements over INFaaS with 46-68% cost-per-latency reductions, while maintaining SLO differentiation. The system scales to 10 tiers without tail latency degradation, with overheads localized to prefill phases.
llm servingslo schedulingpriority tiersvidur simulatorlatency optimization
Transfer Learning of Keystroke Dynamics for Cross-Device User Authentication
The paper proposes a transfer learning approach for cross-device user authentication using keystroke dynamics, addressing distribution drifts between devices with different form factors. The method employs inductive transfer learning to adapt dynamics from a primary device, combines them with limited secondary-device training data, and trains a binary classifier using an extended feature set. Evaluated on the BBMAS dataset, the system achieves a 14.2% equal error rate, outperforming existing methods.
keystroke dynamicstransfer learninguser authenticationcross-devicebiometric modality
LaGSplat: Inferring Physics-Governed Interactive Simulation from Monocular Video Using Latent Lagrangian Gaussian Splatting
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Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior
This work introduces a framework for proactive misinformation mitigation by ranking users via behavioral archetypes—amplifiers, super-spreaders, and coordinated accounts—each exhibiting distinct dissemination patterns. The authors develop archetype-aligned ranking models, demonstrating that super-spreader traits dominate top ranks (precision not quantified), while lower ranks show multi-archetype interplay. Temporal dynamics prove critical, with methods reducing required observation windows for forecasting. Explainable AI (XAI) techniques integrate archetypal traits into an interpretable unified model, aiding moderation strategies. Results highlight actionable tools for identifying high-risk users, though specific performance metrics are unspecified.
misinformation propagationuser ranking modelsbehavioral archetypestemporal dynamicsexplainable ai
Advancing Open and Reproducible Relational Learning: RelArena-$α$, TabPFN-Rel and RPI
The authors introduce RelArena-$\alpha$, TabPFN-Rel, and RPI as open-source tools for advancing reproducible relational learning research. RelArena-$\alpha$ standardizes evaluation on RelBench v1 through unified data loading, protocols, and tuning regimes, while TabPFN-Rel extends TabPFN-3 with relational capabilities, currently leading RelArena-$\alpha$ rankings. The model-agnostic RPI interface enables problem definition and model application across relational databases. Results demonstrate that flattened table approaches remain competitive with specialized relational architectures.
relational learningreproducible researchtabular benchmarksmodel-agnostic interfaceevaluation framework
Correlation Clustering with Random Partial Information
The paper addresses the gap in approximation guarantees for correlation clustering between complete and general graphs by analyzing randomly subsampled complete signed graphs. It introduces a model where each edge is independently deleted with probability $q$, and provides improved approximation bounds for both min-disagreement and min-max objectives. Theoretical results show these bounds depend on $q$ and significantly outperform general graph guarantees, with experimental validation confirming proximity to complete graph performance.
correlation clusteringmin-disagreementmin-max objectiveapproximation guaranteesrandom subsampling
SCALE: State-Calibrated Latent Embeddings for JEPA Planning in the Right Geometry
The paper introduces SCALE (State-CAlibrated Latent Embeddings), a method to improve planning in joint-embedding predictive architectures by aligning latent geometry with task-relevant state information. SCALE regularizes pairwise distances in the learned latent space to correlate with distances in a standardized state space, preserving LeWorldModel's encoder while inheriting DINO-WM's favorable geometric properties. Experiments across five tasks, three planners, and five compute budgets show consistent improvements over baseline LeWM, with no planning-time overhead. Results demonstrate that planning performance depends on both information presence and its geometric organization in the latent space.
joint-embedding predictive architecturelatent geometryanti-collapse regularizationstate-calibrated embeddingsrepresentation learning
Efficient Coreset Selection via K-Nearest Neighbor Graphs
The paper introduces KNNG-CS, an efficient coreset selection method that uses K-nearest neighbor graphs to reduce training costs while preserving model accuracy. By leveraging local neighborhood structures to estimate data importance and avoiding dense pairwise distance computations, KNNG-CS achieves linear storage complexity. Experiments on four datasets demonstrate comparable accuracy to gradient-approximation baselines, with 2.3×-41.2× faster selection and 0.3%-7.5% of baseline memory usage.
coreset selectionk-nearest neighbor graphgradient-approximationmodel trainingmemory efficiency
Domain-Agnostic Neural Topic Modeling with Contextual Token-Level Semantic Graph Representation
The paper introduces DARTopic, a domain-agnostic neural topic modeling framework that improves topic interpretability on specialized corpora by leveraging token-level semantic graphs from frozen PLM embeddings. The method constructs contextual token-level graphs and jointly trains a GNN encoder with topic inference, preserving document-local context that word-level representations discard. Evaluated on general, biomedical, and legal domains, DARTopic outperforms baselines in topic coherence and document clustering without encoder fine-tuning, while maintaining robustness to PLM choice and computational efficiency.
neural topic modelingtoken-level semantic graphdomain-agnosticgnn encodertopic coherence
CoM$^3$eT: A foundation model for medical image analysis through federated, multidimensional context integration
CoM$^3$eT introduces a medical vision foundation model unifying pathology and radiology across sparse/dense predictions and 2D/higher-dimensional inputs via multidimensional attention. The model integrates federated learning with parameter-efficient adaptation (<2.5% trained), outperforming specialized medical foundation models in a 12-dataset benchmark spanning tomographic, whole-specimen, and 2D modalities. It achieves pooled-data performance in federated settings using consumer hardware, demonstrating cross-specialty generalization with efficient fine-tuning.
medical foundation modelmultidimensional attentionfederated learningparameter-efficient adaptationcross-modality generalization
SAUL: Sharpness-Aware Augmented-Lagrangian Unlearning
SAUL introduces sharpness-aware augmented-Lagrangian unlearning for LLMs, formulating knowledge removal as a constrained optimization problem with explicit forgetting criteria. The method employs an adaptive Lagrangian controller to regulate forget-side pressure, sharpness-aware updates for stability, and a dual-optimizer design. Evaluations on TOFU, WMDP, and MUSE benchmarks show superior forgetting-utility trade-offs versus sharpness- and perturbation-based baselines. The Lagrangian controller also improves existing methods' utility when applied as a modifier.
machine unlearningaugmented-lagrangiansharpness-awareconstrained optimizationllms
The Trade-off Between Covariate Dependence and Latent Structure in Representation Learning
The work introduces a supervised framework for disentangled representation learning that balances latent-covariate dependence with structural constraints, revealing a fundamental trade-off between latent independence/exclusive alignment and covariate alignment. It proves ordered disentanglement regimes with closed-form latent space transformations, applicable both post-hoc (e.g., to CLIP, DINOv2, ViT) and within informed factor analysis (iFA). Experiments on simulated and multi-omics data demonstrate improved controllability of structured latent representations via post-hoc alignment and iFA.
disentangled representation learninglatent-covariate alignmentinformed factor analysispost-hoc transformationmulti-omics data
Convolution-Free Holistic Multivariance Decomposition Layer for Efficient Hyperspectral Image Classification Tensor Networks
The paper introduces the Holistic Multivariance Decomposition (HMD) framework, a novel neural network layer for hyperspectral image classification that replaces traditional convolutions. HMD decomposes features into independent single-mode variations and higher-dimensional interactions via learnable matrix-valued supports, with HMD-0, HMD-1, and HMD-2 variants optimized jointly with a classifier. Evaluations on three benchmark datasets show HMD-1 and HMD-2 outperform classical tensor decompositions (Tucker, Canonical Polyadic, Tensor Train) in accuracy while matching 2D/3D-CNNs' generalization with fewer parameters, demonstrating parameter efficiency and training stability.
holistic multivariance decompositionhyperspectral image classificationtensor networksparameter efficiencymatrix-valued supports
Optimizing Multi-Market Participation of Battery and Electrolyser Systems Based on Field Performance
This work presents a price-driven optimization framework for multi-market participation of battery energy storage systems (BESS) and Power-to-X (PTX) electrolysers, incorporating real hardware behavior such as ramp limits and efficiency. Using experimental data from a 55 kW/79 kWh BESS and three 2.4 kW electrolyser units, the model evaluates participation in Danish electricity and ancillary service markets with 2022-2025 data. Results show optimized daily profits of 1,749.27 DKK (BESS) and 289.46 DKK (electrolyser), demonstrating the importance of hardware-aware modeling for revenue stacking.
battery energy storage systempower-to-xancillary servicesrevenue stackinghardware-aware optimization
A Privacy Study of Sparse Collaborative Inference
The study demonstrates that sparse collaborative inference (CI) poses significant privacy risks despite reducing communication costs, as sparse activation positions alone enable high-fidelity input reconstruction and re-identification. The authors decompose sparse activations into retained values and their positions, then evaluate leakage from each component across natural-image and face datasets. Results show that sparsification reduces leakage far less than transmission cost, with positions remaining a persistent privacy risk even at low utility or cost, necessitating careful auditing.
collaborative inferencesparse activationsprivacy leakageinput reconstructionre-identification
Beyond Peak Backlog: Conditional Energy and Temporal Geometry in Capacity-Constrained Delayed Bandit Optimization
The paper introduces a scheduler-side conditional-energy interface for capacity-constrained delayed bandit optimization, addressing the gap between limited feedback tracking and unlimited tracking scenarios. The method separates rate adaptation from perturbation filtration and manages dependent importance weights via randomized admission. Results show an untuned learner achieves delay complexity $O(\sqrt{E_C d_{\mathrm{tot}}})$ under semi-clairvoyant oracles, with explicit restart factor $E_C$, and demonstrates polynomially different regret for delay vectors with identical aggregate summaries under strong convexity. The framework requires $C \ge \ln T+1$ but lacks a complete capacity minimax characterization.
bandit convex optimizationdelay complexityconditional-energy interfacesemi-clairvoyant oracleminimax regret
Quantifying the Gap Between Laboratory Battery Test Patterns and Field Duty Profiles
This paper quantifies the discrepancy between laboratory battery test patterns and field duty profiles by analyzing six evidence sources, including controlled cycling, drive-cycle testing, and real-world electric-vehicle charging traces. The study employs metrics such as usage frequency, usage intensity, usage C-rate, and a duty-structure index (DSI) based on normalized current dispersion and ramping. Results reveal significant differences: DSI ranges from 0.630 for field traces to 2.936 for laboratory tests, while usage C-rate varies from 0.14 to 2.00. Long-term ageing also differs, with 80% retention occurring at 351 cycles for NASA versus 6292 for Oxford. Field operations show a median use intensity of 137.2 km/day and 56.9% of charges ending at or above 95% SOC.
battery performanceduty-structure indexusage c-rateelectric-vehicle chargingageing metrics
Conditional Evaluation of Language Models with Cheap Auxiliary Signals
The paper introduces LACE (Local Augmented Control-Variate Evaluation), a semi-supervised estimator for conditional evaluation of language models using cheap auxiliary signals. The method employs local centering to subtract conditional means of auxiliary signals (e.g., LLM-judge scores, confidence scores) within target profile regions, ensuring unbiased estimation via linear augmentation with local ridge control variates. Theoretical guarantees include calibration-free identification, unbiasedness, and local oracle optimality. Empirical evaluation spans eight benchmarks (MATH-500, ScienceQA, MMLU, etc.), demonstrating efficiency gains governed by a population local R² metric quantifying cheap signal utility across profile values.
conditional evaluationcontrol variatelocal centeringsemi-supervised estimationperformance profiling
Multi-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis
The paper introduces MGSI, a multi-granularity sentiment integration framework for LLM-based multimodal sentiment analysis (MSA). MGSI encodes audio and visual inputs at short-, medium-, and long-range temporal scales, refines non-text features via text-guided alignment, and applies polarity- and intensity-aware enhancement before compressing them into pseudo-tokens for frozen LLM conditioning. Evaluated on four benchmarks, MGSI outperforms frozen-LLM baselines and matches strong multimodal methods, with ablations confirming the benefits of multi-scale modeling and adaptive sentiment calibration.
multimodal sentiment analysistemporal scalingtext-guided alignmentsentiment calibrationfrozen llm
Decorrelation Is Not Complementarity: Skill, Not Lineage, Governs Trusted-Monitor Ensembles
The study demonstrates that decorrelation in trusted-monitor ensembles is not equivalent to complementarity, with ensemble performance governed by individual monitor skill rather than pretraining lineage diversity. Analyzing 24 open-weight monitors across nine lineages on backdoored code detection (pAUC 0.028–0.803), the authors decompose agreement into signal (positively correlated with ensemble gain, Spearman +0.53) and error components (negatively correlated, -0.25). Lineage-based decorrelation shows no significant benefit (permutation p = 0.13), while panel gain inversely correlates with member skill (-0.66 at k=2). Results hold across six attacker models.
trusted monitoringensemble diversitybackdoor detectiondecorrelationpauc
Demystifying Oversmoothing in Sheaf Neural Networks: An Index-Theoretic Criterion
The paper introduces an index-theoretic criterion to precisely characterize anti-oversmoothing capacity in Sheaf Neural Networks (SNNs), addressing limitations of prior analyses based solely on harmonic space dimension. By analyzing stalk transportation and global sheaf structure, the authors establish a geometric comparison criterion distinguishing genuine harmonic space enrichment from trivial inflation. They further propose GyroSheaf, a non-linear extension using gyrovector-space stalks, validated via local tangent-space linearization. Experiments across ten models confirm the criterion's predictive power: compliant models maintain depth-stable representations while violating models collapse despite index jumps.
sheaf neural networksoversmoothingharmonic spaceindex-theoretic criteriongyrovector-space
RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localization
RadioVIL introduces an anomaly-aware diffusion framework for joint radio map inpainting and zero-shot vehicle localization in 6G ISAC systems. The method employs a two-stage approach: a Denoising Diffusion Probabilistic Model (DDPM) learns environmental structural priors, while a Diffusion-based Mediating Intermediate Layer Optimization (DMILO) algorithm isolates vehicle scattering anomalies via L1-regularized sparse deviation optimization. Experiments show RadioVIL achieves superior perceptual quality (LPIPS: 0.0587) and zero-shot localization performance (75.20% Recall, 3.31m average error), outperforming conventional reconstruction baselines that erase scattering signatures.
radio map inpaintingdenoising diffusion probabilistic modelzero-shot localization6g isacanomaly detection
REFLEX: Reflexive Equilibrium Fixed-point Learning for Endogenous eXchanges
REFLEX introduces a stability framework for machine learning-based quote-setting in over-the-counter bond markets, where retraining on endogenous trades creates feedback loops. The method derives a retraining modulus from three measurable dealer behaviors (volume response, objective curvature, informed flow sensitivity) to predict convergence or divergence. Simulations show 8% prediction accuracy, 1.74-3.16x instability from competition, and 4.3-4.4x stability reduction in crisis regimes versus calm markets. The framework converts abstract performative stability conditions into actionable pre-deployment metrics.
performative predictionendogenous feedbackretraining modulusquote-settingmarket stability
Asymptotics-guided learning and symbolic regression for dispersive resonances
The paper introduces an asymptotics-guided learning approach for predicting resonances in dispersive media, formulated as nonlinear spectral problems for volume integral operators. The method leverages asymptotic analysis to construct feature spaces for data-driven corrections, using subwavelength expansion features including two-dimensional logarithmic scales. Results demonstrate improved accuracy for single-resonator and dimer predictions, with symbolic regression yielding compact, interpretable formulas for the learned residuals. The approach shows that asymptotic features can enhance both accuracy and interpretability of data-driven corrections.
dispersive medianonlinear spectral problemsasymptotic analysissymbolic regressionsubwavelength expansion
Multi-Feature Riemannian Hypergraph for Online Test-Time Adaptation of Motor Imagery Brain-Computer Interface
The paper proposes Multi-feature Riemannian Hypergraph (MRieHy), a framework for online test-time adaptation in motor imagery brain-computer interfaces (MI-BCI) that addresses cross-day transferability challenges. MRieHy combines Riemannian geometry for covariance matrix alignment with hypergraph construction over both Riemannian and deep feature spaces, using adaptive fusion weights. Evaluated on a private ECoG dataset and two public EEG datasets, it demonstrates superior performance compared to state-of-the-art baselines in online MI-BCI decoding.
riemannian geometryhypergraphtest-time adaptationmotor imageryeeg decoding
Coded Hankel Polynomial Chaos: Spectral Identification of Dominant Polynomial-Chaos Modes
The paper introduces Coded Hankel Polynomial Chaos (CH-PC), a spectral method for identifying dominant polynomial-chaos modes by reformulating the problem as a low-rank Hankel matrix recovery task. The approach uses a finite generating transform to encode PCE coefficients into a coefficient-generating polynomial, evaluated along geometric phase orbits to produce exponential sums. Spectral nodes and model order are extracted via low-rank Hankel factorization, with phase shifts enabling multi-index recovery. The method demonstrates exact recovery, noise robustness, and unknown-order identification in sparse Legendre benchmarks and a stochastic Darcy problem, outperforming traditional sparse regression.
polynomial chaos expansionhankel matrixsparse recoveryspectral identificationgenerating transform
EMS Coreset: An Efficient Expectation-Maximization Algorithm for Sinkhorn Coreset
The paper introduces EMS Coreset, a scalable Sinkhorn-based coreset method that enables efficient Expectation-Maximization (EM) updates for entropically regularized Optimal Transport (OT) coupling by relaxing uniform weight constraints. The approach generalizes k-means through soft assignments while maintaining asymptotic consistency and Lipschitz stability guarantees. Experiments on synthetic and real-world datasets demonstrate competitive approximation quality with significantly reduced runtime compared to Wasserstein- and standard Sinkhorn-based coreset methods, particularly at large scales.
coresetsoptimal transportsinkhorn algorithmexpectation-maximizationentropic regularization
Unifying Graph Neural Networks Through a Common Layer Equation
The paper introduces a unified framework for graph neural networks (GNNs) through a common layer equation with seven components: update domain, channel set, propagation bank, per-channel message maps, channel-fusion operator, ego/residual map, and update map. This factorization separates information propagation (where) from message content (what), enabling explicit reductions of canonical layers across seven architectural families. The framework organizes 200+ architectures, reveals component-level theoretical insights (e.g., operator support bounds dependencies), and connects propagation choices to graph learning challenges like oversmoothing and heterophily. It also formalizes the inverse problem of mapping graph/task properties to validated components.
graph neural networkslayer equationpropagation bankmessage mapsheterophily
Representation Is Not Enough: Body-Localized Thermal Evidence for Contactless Stress and Craving Sensing in Opioid Use Disorder
FABLE-Therm introduces a weakly supervised architecture for contactless stress sensing in opioid use disorder (OUD) by preserving localized evidence across body regions, time, and encoder-specific representations. The method fuses frozen foundation-model encoders at the embedding level, leveraging thermal video to achieve 0.938 AUROC on held-out participants and demonstrating transferability to self-reported craving. Results reveal that improving representation alone is insufficient for equitable deployment, as cohort gaps persist due to person-to-person heterogeneity. The study provides the first cohort-structured contactless thermal OUD benchmark, emphasizing the importance of localized evidence for accurate sensing and failure analysis.
weakly supervisedthermal videofoundation-model encoderslocalized evidenceopioid use disorder
Towards Reasonable Molecular Structure Elucidation from Infrared Spectroscopy with Chemical Feedback
The paper introduces Formula- and IR-Matched Preference Optimization (FIRMPO), a chemical feedback-driven preference optimization framework for molecular structure elucidation from infrared (IR) spectra. FIRMPO incorporates preference signals based on exact molecular formula matching and IR spectral consistency to guide reasonable structure predictions, remaining model-agnostic and compatible with existing prediction models. Experiments on three IR datasets demonstrate that FIRMPO significantly improves top-ranked prediction accuracy over baselines by prioritizing chemically plausible structures.
infrared spectroscopymolecular structure elucidationpreference optimizationchemical feedbackmodel-agnostic
DeepOHeat-v2: Self-Improving Operator Learning for Fast and Trustworthy Thermal Optimization in 3D-IC Design
DeepOHeat-v2 introduces a self-improving operator learning framework for thermal optimization in high-contrast 3D-IC designs, addressing two key limitations of prior work. The method employs a discretized physics loss to handle discontinuous conductivities, reduces loss-Hessian conditioning via energy form, and uses matrix-preconditioned optimization. A trust gate identifies hotspot placements for reference solver refinement, enabling incremental retraining. Results show a 56× speedup over solve-at-every-step optimizers while reducing the peak temperature error gap from 1.12 K to 0.11 K on a multi-die benchmark.
operator learningthermal optimization3d-ic designphysics-informed lossself-improving framework
GOD: Enhancing Generalization via Deep Grafting for Sequential Recommendation
GOD (Graft-Oriented Distillation) introduces component-level distillation for sequential recommendation by grafting frozen teacher components with trainable student counterparts to form hybrid models. This framework evaluates student embeddings via the teacher encoder and vice versa, isolating component effects to improve generalization without inference overhead. Experiments on three real-world datasets show GOD outperforms state-of-the-art baselines by up to 13.92%.
sequential recommendationknowledge distillationcomponent-level graftinggeneralizationhybrid models
Structured Prediction for Scalable Spreadsheet Table Understanding: From Cell Types to Table Ranges (Extended Version)
The paper introduces a two-stage pipeline for spreadsheet table understanding, combining Cell-Type Classification (CTC) via LightGBM with structured features and a pairwise CRF for spatial consistency, followed by deterministic Table Detection (TD). The method is evaluated on StatSheets, a new multilingual benchmark of 737 annotated sheets. Results show CTC achieves 0.937 Mean File-Macro F1, within 0.6 points of TUTA Transformer but more efficient, while TD outperforms region-based baselines and matches LLM-based approaches like SpreadsheetLLM.
cell-type classificationtable detectionstructured predictionlightgbmspatial consistency
AdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving
AdROD introduces a hypernetwork-based adversarial defense for camera-based object detection in autonomous vehicles, using low-rank hypernetworks (1.6% parameter footprint) to generate stochastic detector ensembles per frame. The method incorporates functional diversity via weight updates and input transformations, offering two modes: AdROD-I for continuous protection via inter-detector disagreement and AdROD-II for on-demand activation via kinematic triggers. Evaluations on synthetic benchmarks, physical patches, and OpenCDA co-simulator show superior robustness over five baselines while maintaining real-time performance.
adversarial robustnesshypernetworksobject detectionautonomous drivinglow-rank approximation
Group ICA 2.0: Closing the Gap Between Subjects and Group Latent Decomposition with Copula-Linked Group ICA (CoLiG-ICA)
CoLiG-ICA introduces a novel group ICA framework that jointly estimates template-linked, cohort-only, and subject-only brain networks to address intersubject heterogeneity in fMRI data. The method combines ICA-based spatial decomposition, copula-based dependence modeling, and deep learning optimization, preserving template-constrained ICA interpretability while enabling free components. Evaluated on UCLA-CNP resting-state fMRI data, CoLiG-ICA outperformed MOO-ICAR with lower intercomponent spatial dependence (p<0.05), reduced motion-related variance in template-linked components, and identified three additional resting-state networks in schizophrenia patients beyond 53 template-linked NeuroMark components.
group independent component analysiscopula modelingfunctional mrineuroimaging heterogeneityspatial decomposition
Breaking the Compression Barrier: Cross-Architecture Compression Boundary Learning via Reverse Regrowth
Proposes BRIDGE, a boundary-learning reverse regrowth framework that reformulates model compression as a constructive boundary-search problem. Unlike forward pruning, BRIDGE first drives models to extreme sparsity to expose collapse regions, then hierarchically regenerates critical structures via layer-wise and parameter-wise selection. Demonstrates architecture-agnostic recovery from collapse, achieving performance improvements of 1.49% (unstructured) and 4.77% (structured pruning) on CNNs and Transformers. Enables stable compression beyond traditional sparsity thresholds.
model compressionreverse regrowthsparsity collapsehierarchical regenerationboundary-search
Retrieval-guided Twin Fusion with Similarity-aware Contrast for Molecule-Text Alignment
The paper introduces RISEN, a novel method for molecule-text alignment that enhances joint representation learning by constructing latent twin molecules via cross-modal retrieval. RISEN retrieves relevant textual descriptions and similar molecules for each substructure, aggregates their representations via attention pooling, and fuses them with the original substructure. It also employs similarity-aware contrastive learning with soft thresholding. Experiments on benchmark datasets demonstrate RISEN's superiority over existing baselines in downstream tasks like molecule search and property prediction.
molecule-text alignmentcross-modal retrievalattention poolingcontrastive learningsubstructure representation
Toward Optimal Second-Order Path-Length Guarantee for Adversarial Multi-Armed Bandits
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Learning Varying Physical Therapist-Patient Interactions for Robot-mediated Upper Limb Task-Specific Training
The study proposes a Learning-from-Demonstration framework using Task-Parameterised Gaussian Mixture Models (TPGMM) to model personalized physical therapist-patient interactions during Task-Specific Training (TST) for upper limb rehabilitation. The method maps patient joint kinematics to therapist-applied torques from few demonstrations and generalizes to new task variations. Evaluated on 14 mock therapist-patient pairs across three tasks with six variations each, TPGMM slightly outperformed a Look-Up Table benchmark, with both methods achieving closer approximations to actual interactions as task complexity increased.
task-specific traininggaussian mixture modelslearning-from-demonstrationrehabilitation roboticskinematic mapping
Operator-Theoretic Generalization Bounds for Multitask Deep Learning
The paper develops operator-theoretic generalization bounds for deep multi-output networks by modeling layers as Koopman operators on vector-valued reproducing kernel Hilbert spaces (RKHS). Using Sobolev and Brownian RKHS frameworks, it derives Rademacher complexity bounds that decouple task coupling (via task matrix trace) from layerwise operator norms, Sobolev symbols, and activation properties. For Brownian RKHS, bounds scale with weight magnitudes ($|W_l|^{1/2}$) and activation derivatives ($\|σ_l'\|_\infty^{1/2}$). The work also formulates shared operator learning, proves a representer theorem, and provides target-transfer analysis, with empirical validation on synthetic and MNIST data.
koopman operatorsvector-valued rkhsrademacher complexitymulti-task learninggeneralization bounds
Whose Gold? Annotator-Pool Disagreement Is Large at the Item Level, and Hidden by Small Leaderboards
The study reveals significant annotator-pool disagreement in preference benchmarks, demonstrating that expert and crowd annotators assign different majority labels to 23.6% of items in MultiPref and 30.5% in MT-Bench, despite leaderboards remaining bit-identical (Kendall tau = 1.00). Through item-level bootstrap analysis, the authors show that leaderboard stability is fragile, with a 28% chance of model displacement in resamples and near-certain disruption for larger leaderboards. The work challenges assumptions of annotator consensus, showing LLM judges align with crowd over expert preferences, and provides pre-registered decision rules for reproducibility.
preference benchmarksannotator disagreementleaderboard stabilitybootstrap analysisllm judges
Fiber Fingerprints of Hidden Learning-State Dynamics
The paper introduces fiber fingerprints as a formalism to characterize hidden learning-state dynamics where execution states indistinguishable by present behavior exhibit divergent future training responses. The method employs prefix-compatible finite probes to construct predictive quotient functors and canonical predictive fibers, with decomposition into visible, mode-reuse, and irreducible-new sectors under finite-dimensional Hilbert realization. Empirical studies with Qwen2.5-7B and Mistral-7B-v0.3 reveal local action backbones and output-range reuse, though unresolved limitations include re-anchored transport and context-dependent revelation.
fiber fingerprintspredictive quotient functornerode-type representationhodge chronologyadam moment section
The Limits of Binding in Dual Encoders
The paper analyzes the binding limitations in dual-encoder models like CLIP, which struggle with compositional tasks (e.g., distinguishing 'a red car and a blue dog' from its swapped variant). Using the ideal-encoder framework, it proves three fundamental obstructions: (1) Depth: recursive role-binding codes exhibit exponentially diminishing swap margins with nesting depth, limiting resolvable depth to single digits at CLIP scale. (2) Objective: contrastive training provides vanishingly small rewards for binding at web scale. (3) Geometry: a smoothness-binding trade-off constrains achievable margins. Empirical validation across 18 text encoders shows deployed models operate at 25-35% of their theoretical ceiling, with failure attributed to incentive and code-structure limits.
dual-encoder modelsbinding failurecontrastive learningswap marginideal-encoder framework
A Banach-Space Theory of Markovian Halpern Iteration for Non-Expansive Maps
The paper introduces a variance-reduced Markovian PAGE-Halpern method for stochastic approximation of fixed points of non-expansive operators in Banach spaces. The method leverages Poisson equation analysis for refresh and same-state difference blocks, extending Hilbert-space cocoercivity results to general finite-dimensional Banach spaces. Key results include an $ ilde O(ε^{-3})$ sample complexity in the original non-expansiveness norm and high-probability guarantees using auxiliary smooth norms, with applications to non-smooth sup and block-sup geometries via norm smoothing.
markovian halpern iterationnon-expansive mapspoisson equationsample complexitybanach space
Beat the Counter First: A Baseline for Temporal-Graph Anomaly Detectors
The paper introduces SimpleCount, a parameter-free baseline for temporal-graph anomaly detection (GAD) that selects a single scalar feature from a fixed pool of counts, recencies, and derived transforms. Evaluated against SLADE and IsoForest on five public and one synthetic dataset, SimpleCount matches or exceeds SLADE on three datasets and outperforms IsoForest on all six, while requiring 23-133x less compute. Structural scores on synthetic datasets (Synth-Triangle, Synth-Quad) achieve AUC up to 0.955, whereas detector models perform near random. Results demonstrate dataset-dependent benefits of complexity and emphasize reporting gains against strong baselines with compute costs.
temporal-graphanomaly detectionparameter-freescalar featurecompute cost
Functional anatomy of Pythia-Herwig differences with Kolmogorov-Arnold networks
The work introduces a KAN-based framework for functional analysis of differences between high-energy event generators Pythia and Herwig. By tracking hard dijet events through three simulation stages (shower-only, hadronized, full-generator), the method decomposes classifier-derived log density ratios into interpretable one-dimensional observable responses. Results show stage-dependent discrepancy drivers: multiplicity dominates at shower level, jet mass/shape at hadronization, and mixed patterns in full simulation. Transport analysis reveals persistent multiplicity effects but non-persistent shape responses, while jet-mass factors suffer from statistical limitations.
kolmogorov-arnold networksevent generatorshadronizationjet massmultiplicity
ReliaGate: Reliability Routing for Low-Stakes Wearable Stress Prediction
ReliaGate introduces reliability routing for low-stakes wearable stress prediction, where a post-hoc gate surfaces or withholds classifier outputs based on correctness cues without label modification. The method combines confidence, signal quality, agreement, atypicality, and geometric features into a correctness score, evaluated via subject-disjoint folds and validation-selected routing. Experiments on WESAD, UBFC-Phys, and E4 datasets show mixed but generally favorable results, with WESAD favoring ReliaGate and UBFC-Phys showing primary coverage/risk improvements.
reliability routingpost-hoc gatingwearable stress predictionfixed-label routingcorrectness score
The Null Token Knows: Reducing Message-Free Hallucination in ASR and NMT
The study investigates message-free hallucination in encoder-decoder systems (ASR and NMT) by analyzing null-token scores as potential abstention signals. It audits native null-token scores, scalar logit shifts, and probes decoder states in Whisper, comparing supervised edits with external gates. Results show that null-token scores often provide a usable abstention signal, but standard decoding fails to utilize it effectively. Adjusting null-token scores can reduce fabrication but risks deleting valid content, highlighting a trade-off between suppression and deletion costs. The findings position the null token as a diagnostic tool for hallucination and advocate for balanced abstention evaluation.
null tokenhallucinationabstention signalencoder-decoderlogit shifts
$S^3$: A Smooth Simulation Surrogate for Optimizing Discrete Abstractions of Dynamical Systems
The paper introduces $S^3$, a smooth simulation surrogate for optimizing discrete abstractions of dynamical systems with black-box controllers. The method combines a differentiable approximation of the reverse simulation metric with Taylor model-based reachability, enabling gradient-based optimization of abstraction parameters while preserving soundness. Evaluated on three case studies, $S^3$ shows strong correlation with the reverse simulation metric, reduces conservatism, and improves computational efficiency compared to traditional approaches.
smooth simulation surrogatediscrete abstractionsreverse simulation metrictaylor model-based reachabilitygradient-based optimization
Crystal-structure design by agentic AI in a language of motifs
MatEvolve introduces an agentic-AI framework for crystal-structure design using an interpretable language of motifs, where each crystal is represented as a motif profile describing recurring geometric patterns. The agent edits these profiles to propose new candidates, validated by first-principles calculations. Applied to rare-earth-lean permanent magnets, MatEvolve (built on Claude Fable~5) discovers new structural prototypes 3× more often than generative models under equal validation budgets, while maintaining comparable on-target-magnet rates. The motif profiles also enable analysis of structure-property relationships.
agentic-aimotif profilecrystal-structure designfirst-principles calculationrare-earth-lean magnets
Resource-Efficient QUBO Formulation for Anchored Currency Arbitrage
The authors propose a resource-efficient QUBO formulation for anchored currency arbitrage that incorporates realistic constraints (starting from a held currency, per-transaction fees) while reducing logical variable counts compared to prior encodings. They introduce exact anchor-gauge reweighting to compress coefficient ranges for analog hardware compatibility and derive provable penalty weights. Classical simulated annealing tests demonstrate effective discovery of fee-adjusted profitable cycles, outperforming five prior QUBO encodings in recovering exact optima at matched sampler budgets.
qubocurrency arbitragesimulated annealingpenalty weightsanchor-gauge
Deploying Frontier Agentic Technology in MOOSEnger, a Multiphysics-Capable AI Assistant
MOOSEnger introduces an agentic AI framework for the MOOSE multiphysics simulation environment, integrating local model deployment with contextual knowledge retrieval, simulation validation, and persistent memory. The system employs a tool-enabled agent harness to automate workflows, demonstrated on a National Reactor Innovation Center Virtual Test Bed case. Evaluated across 8 physics categories (25 prompts each), MOOSEnger-GPT-5.2 achieves 90% success versus 76.5% for MOOSEnger-Gemma4, while baseline models (GPT-5.2: 5%, Gemma4: 0%) highlight the agentic harness's efficacy.
multiphysics simulationagentic aifinite-element frameworkcontextual retrievalpersistent memory
Large Language Models as Implicit Sociological Models: Reconstructing Voting Behaviour from Sociodemographic Profiles
The paper proposes a methodological framework for using large language models (LLMs) as implicit sociological models to reconstruct aggregate voting behavior from sociodemographic profiles. By conditioning LLMs on demographic descriptions and eliciting probabilistic turnout and party preferences, the authors demonstrate that contemporary LLMs can reproduce official election outcomes with low mean absolute error, recover political bloc structures, and align with known sociodemographic gradients. Using the 2021 Czech parliamentary election as a case study, the work highlights LLMs as compressed representations of social reality for computational social science, while acknowledging epistemic and ethical limitations.
large language modelssociodemographic profilesprobabilistic turnoutaggregate voting behaviorcomputational social science
TransfHAR: Self-Supervised Wrist Representations for On-Demand Activity Recognition
TransfHAR introduces a self-supervised framework for on-demand, fine-grained wrist activity recognition using IMU data, eliminating the need for task-specific labeled data. The method learns transferable motion priors by pretraining on coarse, unlabeled activities (e.g., sitting, walking) and transfers to fine-grained manipulative, gestural, and procedural tasks (e.g., snapping, stirring). Evaluations show TransfHAR matches or exceeds supervised baselines by 6.2 balanced-accuracy points on average, achieving 86.7% accuracy with five examples per class and 90.4% with one-minute recordings in user studies.
self-supervised learningimu dataactivity recognitiontransfer learningfine-grained classification
Geometry of Forgetting: Representation Flux in Continual Learning
The paper introduces representation flux, a geometric measure of sample-level latent representation displacement during continual learning, and demonstrates its strong association with catastrophic forgetting across SplitMNIST, SplitFashionMNIST, SplitCIFAR10, and SplitTinyImageNet. The proposed FlowLess-R method regularizes replay representations relative to stored references via a representation-matching term, integrating with ER, DER++, and ER-ACE to improve final average accuracy by constraining representation-space dynamics. Experiments show representation flux precedes performance degradation and correlates with confidence degradation, establishing it as an informative geometric marker for forgetting mitigation.
representation fluxcatastrophic forgettingcontinual learninglatent representationsgeometric regularization
Generalized Linear Bandits with Memory
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Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Trading
The paper proposes a self-supervised framework for automatic auxiliary task discovery to stabilize reinforcement learning in stock trading. The method employs two networks: a main network learning trading policies with auxiliary predictions, and a secondary network generating task definitions via learned cumulants and discount factors, updated via meta gradients. Evaluated on DJI, FTSE, Sensex, and TAIEX indices, the approach demonstrates improved robustness and trading performance over baselines through dynamically discovered auxiliary tasks.
reinforcement learningauxiliary tasksgeneral value functionsmeta gradientstock trading
How Many Samples Are Needed to Determine Causal Direction? Sharp Minimax Bounds for Bivariate LiNGAM
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Second-Moment Memory in Coordinatewise Adam
The paper analyzes the optimization cost of second-moment memory in Adam, demonstrating that it can suppress progress toward the optimum even under finite-variance stochastic gradients. Through a two-point oracle analysis, the authors derive an expected positive normalized update bound of $O(M_2^{-1/2})$, where $M_2=(1-β_2)^{-1}$ is the memory length. This bound translates to an average-stationarity lower bound of the same order on a smooth convex problem, revealing that long second-moment memory slows optimization despite finite gradient noise variance.
adam optimizersecond-moment memorystochastic gradientsconvex optimizationnormalized update
QuantumPhaseNet: A Gauge-Covariant Geometric and Quantum-Spectral Theory of Semantic Concept Hierarchies with Prototype Validation of a Classical Quantum-Inspired Model
QuantumPhaseNet introduces a gauge-covariant geometric and quantum-spectral extension of Transformer representations, modeling semantic states as complex amplitudes with covariant phase rates for conceptual scale. The theoretical framework ensures local gauge invariance, unitarity, and boundedness, while a classical quantum-inspired pipeline was validated synthetically (n=240). Results show improved wavelength-hierarchy correlation (Spearman 0.852 vs. 0.707), discourse alignment (0.933 vs. 0.589), and error detection (AUROC 0.854 vs. 0.634), but no quantum advantage (25.5% target probability vs. 70.7% classical).
gauge-covariantquantum-spectraltransformerphase ratehallucination-risk
KOALA: Koopman Operator Learning for WiFi-Based Anticipatory Hum
KOALA introduces a WiFi-based human motion prediction framework using Koopman operator learning to address error accumulation in autoregressive methods. The approach lifts noisy Channel State Information (CSI)-derived poses into a learned latent space where dynamics become linear, enabling multi-horizon prediction via matrix-vector products. Key innovations include a residual CSI-conditioned operator to mitigate identity attractor issues and a Koopman Anchored Latent (KAL) loss for dynamical consistency. Evaluations on MM-Fi and WiPose datasets demonstrate superior performance over baselines across short- and long-term horizons.
koopman operatorchannel state informationmotion predictionlatent spaceautoregressive error
A Pre-Specified Construction-Confirmation Test of Operation-Level Causal Transfer Across Finite Isomorphic Symbolic Domains
The study introduces a rigorous test for operation-level causal transfer in finite isomorphic symbolic domains, focusing on whether hidden-state differences between operations can be predictively transferred. Using a frozen Qwen2.5-7B-Instruct model, the method compares input-specific interventions with controls, isolating construction from confirmation splits. Results show one prompt route (integer_mod16--letters16 | successor->predecessor) passed both PyVene splits (p=0.000198, Holm-adjusted p=0.006943), replicated in NNsight 0.7.0 experiments (p=0.007141), though limited to specific conditions.
causal transferisomorphic domainshidden-state differenceoperation-level structureconfirmation split
PWLR: Pairwise Witness Local Rejection for Boundary-Aware Out-of-Distribution Detection
The paper proposes Pairwise Witness Local Rejection (PWLR), a boundary-aware out-of-distribution (OOD) detection method that leverages multimodal large language models (MLLMs) to extract discriminative local cues between in-distribution (ID) class pairs. PWLR screens these cues offline using ID-only data, then combines pairwise local verification with global class scores during inference. Experiments on ImageNet-100 benchmarks demonstrate consistent improvements over vision-language baselines across multiple backbones for far-OOD, near-OOD, and challenging OOD detection tasks.
out-of-distribution detectionmultimodal large language modelspairwise local verificationboundary-aware classificationvision-language backbones
Cross-Entropy Risk Estimation for Language Models: Inconsistency Must Be Dense, and the Holdout Method Is No Exception
The paper demonstrates that held-out per-token cross-entropy risk, a standard metric for comparing language models, cannot be consistently estimated due to fundamental topological limitations. Analyzing possible states (data-generating distributions paired with trained models), the authors show that finite and infinite risk states are arbitrarily close, making consistent estimation impossible even with bounded sequence lengths or full-support models. Two partial solutions are proposed: (1) bounding context windows to ensure finite risk under finite expected sequence lengths, or (2) thresholding risk reports to restore consistency while maintaining model selection utility.
cross-entropy risklanguage modelsconsistent estimationholdout methodtopological inconsistency
CrevasseSeg: A Label-Efficient UAV Crevasse Segmentation Framework
The paper introduces CrevasseSeg, a label-efficient framework for UAV-based crevasse segmentation, featuring 1,938 unlabelled and 200 labelled orthomosaic tiles from Borebreen, Svalbard. It benchmarks five self-supervised objectives (BYOL, JSD, Barlow-Twins, VICReg, BYOL-JSD) across three architectures (O-Net, O-Net++, DINOv3-initialised O-Net) with linear and non-linear (XGBoost) readouts. Key findings include DINOv3's performance inversion (weakest under linear probing, strongest under non-linear readout) and its fragmented feature space versus convolutional architectures' class-sorted manifolds. The DINOv3-ViT-L-Sat-O-Net-BYOL-JSD pipeline achieves 75.33 mDSC / 61.28 mIoU, outperforming RGB baselines using only 24 labelled images.
self-supervised learningbinary segmentationfeature space analysislabel-efficient learningremote sensing
Inferential Evaluation of Surrogate-Derived Models under Covariate Shift
The paper proposes cross-fitted estimators for evaluating surrogate-derived models under covariate shift, addressing scenarios with scarce gold-standard labels and differing covariate distributions across sources. The method combines source-specific density ratios with outcome-regression augmentation and kernel correction, enabling asymptotically linear inference for TPR/FPR, consistent ROC curve estimation, and normal inference for AUC. Theoretical guarantees are validated through simulations assessing bias/coverage and real-world applications (Chatbot Arena, ACS-Income), demonstrating robustness to bandwidth/sample size variations.
covariate shiftsurrogate labelsdensity ratiosroc curveasymptotic inference
Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learning
The paper proposes BRaG, a barycenter-based adversarial inverse reinforcement learning framework for stock trading that learns from multiple heterogeneous expert strategies. BRaG aggregates expert demonstrations via a performance-weighted Wasserstein barycenter to create a stable pseudo-expert representation, then pretrains a trading policy using adversarial imitation learning before refining it with reinforcement learning. The method incorporates control barrier functions to enforce risk constraints. Evaluated on four global equity markets (US, UK, Indian, Taiwanese), BRaG outperforms classical trading rules and deep reinforcement learning baselines while maintaining stable risk characteristics.
adversarial inverse reinforcement learningwasserstein barycentercontrol barrier functionsstock tradingrisk-aware decision-making
Global Simulation-Guided Dynamic Operator Scheduling for Efficient Multi-Tenant Model Serving
SliceScheduler introduces dynamic operator-level scheduling for multi-tenant model serving, addressing inefficiencies in container-granularity scheduling. The system employs a Global Mapping Graph (GMG) for cluster-wide execution state tracking, a global simulator for what-if reasoning, and an incremental scheduler to exploit idle GPU slices while maintaining SLAs. Implemented as a PyTorch backend, SliceScheduler achieves 1.10--2.29× higher token throughput than baselines with <9% SLA violations, demonstrating practical GPU utilization improvements for LLM serving.
operator-level schedulingglobal mapping graphmulti-tenant servinggpu utilizationsla constraints
Provenance, Not Behaviour: A Serialisation Artifact in Edge-IIoTset and a Leakage-Free Benchmark for Precision-Agriculture Intrusion Detection
The authors identify a critical serialization artifact in Edge-IIoTset, a benchmark for IIoT intrusion detection, showing that reported >99% accuracy stems from trivial one-hot encoding of categorical columns (e.g., '0' vs '0.0' in protocol fields) rather than behavioral modeling. They demonstrate that 5/6 classifiers achieve perfect accuracy (1.0000 ± 0.0000) due to this leakage, which persists across encoding schemes. To address this, they introduce AgriEdge, a leakage-free benchmark (1,276,122 rows, 5 devices) with uniform parsing, where the strongest model drops to 0.9503 ± 0.0011 macro-F1. Cross-device evaluation reveals a generalization gap (random forest falls from 0.9988 to 0.5083 balanced accuracy at the perception/actuation layer), with federated learning incurring minimal cost (≤0.0037 macro-F1).
serialization artifactone-hot encodingmacro-f1non-iidfederated learning
Machine Learning Approaches to Decoding Topological Quantum Codes
The article surveys machine learning approaches for decoding topological quantum error-correcting codes, addressing the challenge of achieving reliable, scalable, and real-time decoding in fault-tolerant quantum computing. It frames decoding as a learning problem, covering discriminative, generative, and reinforcement-learning paradigms, and discusses neural network architectures that balance expressivity, scalability, and latency. The review highlights recent progress in neural decoding benchmarks for memory experiments and identifies open challenges toward scalable quantum error correction.
quantum error correctiontopological codesneural decodersfault-tolerant computingreal-time decoding
Learning Auditable Classifier Models: Source-Disjoint Tree Ensembles
The paper introduces Residual Pattern Tree Ensemble (RPTE), a three-stage method for learning auditable classifier models that maintain competitive accuracy while ensuring full interpretability. RPTE employs (1) supervised symbolic feature construction, (2) source-disjoint tree growth where each raw variable appears in at most one tree, and (3) ℓ₁-regularized logistic regression over leaf indicators. Evaluated on 12 clinical benchmarks via 5-fold CV, RPTE matches opaque ensembles' performance while reducing inspection units 9×-87× versus XGBoost and maintaining lower audit complexity than EBM. RuleFit achieves comparable compactness on 3 datasets but lacks source-disjointness guarantees.
interpretable machine learningtree ensemblessource-disjointnessauditabilityclinical prediction
Continuous Quantum Feedback Control via Kraus-Parameterized Belief Reinforcement Learning
The paper introduces Kraus-Parameterized Belief Reinforcement Learning, a quantum feedback control method combining a Stiefel-manifold-constrained recurrent encoder for physically valid density-matrix estimation with a Proximal Policy Optimization (PPO) actor for continuous control. The encoder ensures positive-semidefinite, trace-normalized belief states by embedding quantum state geometry directly into learning. In simulated qubit control, the method achieves stable feedback with measurement-conditioned belief fidelity of 0.77-0.80, outperforming an LSTM-history baseline in return variance under nominal and out-of-distribution conditions, particularly during measurement inefficiency and dynamics switches.
quantum feedback controlkraus-parameterized beliefstiefel manifoldproximal policy optimizationdensity-matrix estimation
Deep learning-based computed tomography (CT) derived body composition classifier for colorectal cancer patients
The study demonstrates that optimized deep learning models can automate CT-derived body composition analysis for colorectal cancer patients, achieving mean percentage errors of 4.96% (GoogLeNet) for skeletal muscle area (SMA) and 8.12% (AlexNet) for skeletal muscle density (SMD). Four architectures (AlexNet, UNet, GoogLeNet, ResNet34) were systematically evaluated for predicting SMA, SMD, subcutaneous fat area (SFA), and visceral fat area (VFA), with GoogLeNet showing superior performance. Independent testing confirmed 80% classification accuracy, and a web application enabled clinical integration.
deep learningcomputed tomographybody compositioncolorectal cancerautomated regression
BERTopic-Virality Prioritisation: A Scalable Framework for Thematic and Comparative Analysis of COVID-19 and Monkeypox Misinformation on Twitter
The paper introduces BERTopic-VP, a virality-prioritized topic modeling framework combining BERTopic with a post hoc Virality Prioritization layer for analyzing health misinformation. The method integrates contextual embedding-based clustering, a hybrid misinformation detection module (supervised classifier + knowledge-base verification), and engagement-based ranking. Evaluated on COVID-19_FNIR, Monkeypox, and Constraint datasets, it achieves F1 up to 0.950 and ROC-AUC up to 0.989, effectively identifying high-impact clusters under top 1%, 5%, and 10% virality thresholds. The framework enables scalable comparative analysis of pandemic misinformation by surfacing low-volume, high-risk narratives.
topic modelingmisinformation detectionvirality prioritizationcontextual embeddingsaffective-linguistic profiling
Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search
The paper introduces Large Discovery Models (LDM), a recurrent architecture combining generative models with Bayesian non-parametric reward surrogates for open-ended scientific discovery. LDM iteratively proposes candidates via the generative component, evaluates them using uncertainty-aware surrogate predictions, and updates both models with new observations. Evaluations on neural-network training, antibody design, and molecular optimization demonstrate LDM's superiority, achieving 2.4× better validation BPB, 18.2% lower binding energy, and >60% multi-objective gains versus LLM-only or traditional search methods.
large discovery modelsbayesian non-parametricopen-ended searchgenerative modelsuncertainty quantification
SubZero+: Efficient Zeroth-Order LLM Fine-Tuning via Large Learning Rates
SubZero+ improves zeroth-order (ZO) fine-tuning of large language models through three innovations: (i) multi-query gradient estimation in layer-specific low-rank subspaces to reduce variance, (ii) a subspace Adam optimizer leveraging in-subspace gradient statistics, and (iii) sign-corrected QR-based subspace construction for Haar-distributed projections. Evaluated on models from 1.3B to 32B parameters across SuperGLUE benchmarks, SubZero+ outperforms prior ZO methods, expands stable learning-rate ranges, and approaches first-order performance with minimal memory overhead.
zeroth-order optimizationlow-rank subspacesgradient estimationhaar-distributed projectionssubspace adam
Sequential Multimodal Evidence Optimization for Product Media Ranking in E-Commerce
The paper introduces Sequential Multimodal Evidence Optimization (SMEO), a two-stage framework for customer-oriented ranking of heterogeneous product media in e-commerce. SMEO first learns a trajectory utility model to estimate how ordered media sequences aid purchase decisions, addressing position bias and variable-depth imbalance in logs. It then trains an autoregressive ranking policy with survival-weighted reward-to-go to prioritize decision-relevant information early. Offline evaluation on large-scale e-commerce sessions shows SMEO improves conversion by 5.5% and reduces required swipes by 15% versus baselines.
sequential multimodal rankingtrajectory utility modelsurvival-weighted rewardoff-policy estimationautoregressive policy
Adaptive Heterogeneous Compression for Resource-Efficient Federated Knowledge Distillation
The paper proposes ASCEND, an adaptive heterogeneous compression framework for federated knowledge distillation (FedKD) that addresses communication overhead in heterogeneous model settings. The method formulates compression strategy selection as a non-stationary stochastic multi-armed bandit problem, using an EMA-enhanced ε-greedy policy to balance exploration-exploitation. Experiments show ASCEND reduces communication costs and training time by 30-50% while maintaining accuracy within 2% of baselines on multiple datasets.
federated learningknowledge distillationheterogeneous compressionmulti-armed banditcommunication efficiency
On Stopping Rules and Spatial Adaptation for CART
The paper establishes the statistical role of stopping rules in CART by proving that the minimum impurity decrease (MID) rule enables spatial adaptation to heterogeneous and anisotropic smoothness. Under structural assumptions on the regression function and covariate distribution, CART with MID achieves pointwise minimax rates up to logarithmic factors across the domain. In contrast, the widely used minimum leaf size rule fails to provide spatial adaptation. These results theoretically justify CART's empirical success and clarify the importance of stopping rule selection.
cartstopping rulesspatial adaptationminimum impurity decreaseanisotropic smoothness
Generalised Transportability via Causal Abstractions
The paper introduces a model-level approach to causal transportability using causal abstraction theory, addressing limitations of query-level transportability when exact transport is impossible or target data are unavailable. By framing transportability as a same-level abstraction problem, the method provides exact alignment conditions for Markovian and semi-Markovian settings and derives certified query intervals via distributionally robust optimization when only approximate transport is feasible. Experiments on synthetic and real-world datasets demonstrate that the framework produces valid bounds for interventional queries, including non-transportable and target-agnostic cases.
causal transportabilitycausal abstractioninterventional querymarkovian settingdistributionally robust optimization
When Is Shallow Enough? Adaptive Split Federated Learning with Client-Specific Sufficiency Estimation
The paper proposes FedSGA, an adaptive split federated learning framework that addresses client heterogeneity through client-specific shallow sufficiency estimation. The method introduces private prompt tokens to track local adaptation dynamics, a shallow sufficiency estimator combining cross-client alignment and temporal stability metrics, and a split-compatible interface harmonization module for heterogeneous client projections. Experiments on heterogeneous benchmarks demonstrate FedSGA's superior performance over state-of-the-art methods while reducing client computation overhead.
split federated learningclient heterogeneitysufficiency estimationprompt tokensinterface harmonization
Sparse Prototype Code Underlies Classification and Prediction Across Modalities
The article demonstrates that classification tasks induce a universal representational geometry across state-of-the-art models in vision, audio, and language processing, characterized by structured within-class variability aligned with class centroids. The authors develop a mean-field theory that predicts classification accuracy using only centroid coordinates of the true class and its strongest rivals, revealing a sparse, centroid-aligned structure. This theory accurately forecasts performance across architectures and modalities, with geometric quantities scaling systematically with model size, linking to sparse-feature extraction methods like sparse autoencoders.
representational geometrymean-field theorycentroid coordinatessparse autoencodersclassification accuracy
In Defense of OCTA: The Reconstruction-Utility Gap in OCT-to-OCTA Synthesis
The study critiques OCT-to-OCTA synthesis evaluation by demonstrating a reconstruction-utility gap, where high-fidelity synthetic images fail to preserve clinically relevant biomarkers. Using a frozen OCTA segmenter as a probe, the authors evaluate two synthesizers (XOCT, TransPro) on downstream segmentation tasks. Results show significant Dice score drops for fine capillary networks (0.798→0.635, p<1e-3) compared to large vessels, with TransPro performing worse. A blur control confirms synthetic detail is fabricated. Retrained models also fail to reproduce neovascular lesions (n=3), highlighting the need for task-based evaluation.
optical coherence tomography angiographyimage synthesisdownstream-task fidelitydiabetic-retinopathysegmentation
Rotation-Invariant Multi-IMU Activity Recognition under Independent Per-Location Orientation Shifts
The paper introduces TRI-HAR, a rotation-invariant framework for multi-IMU human activity recognition that structurally handles independent per-location orientation offsets without requiring rotational augmentation or calibration pipelines. The method reshapes accelerometer and gyroscope streams into triaxial vectors, processes them with a shared SO(3)-equivariant backbone and invariant projection per IMU location, and fuses features for classification. Evaluated on four multi-IMU benchmarks, TRI-HAR maintains macro-F1 under fixed independent SO(3) rotations and outperforms rotation-augmented baselines.
human activity recognitioninertial measurement unitsrotation-invariantso(3)-equivariantmulti-imu
Benchmarking Quantum Machine Learning for Power-System Attack Detection: Evaluation Choices Decide the Outcome Before the Models Do
The study benchmarks quantum machine learning (fidelity-kernel SVMs and variational classifiers) against six classical models for power-system cyberattack detection, revealing that evaluation protocol choices dominate model performance differences. Using the Mississippi State/ORNL dataset, they test white-box, transfer, decision-based black-box, and poisoning attacks, finding that eight methodological choices (six in evaluation, two in tuning) reverse conclusions—e.g., row-level splits yield 0.905 macro-F1 versus 0.594 for file-level splits. Quantum models perform near chance (0.024 below classical) in matched-dimensionality regimes, while fidelity kernels degrade from 0.886 to 0.064 under direct attack. A seeded benchmark and positive controls are released.
quantum machine learningcyberattack detectionfidelity kernelvariational classifiermacro-f1
FluxBin: Flexible LUT-based Ultra-low-bit LLM Inference by Algorithm-Kernel Synergy
FluxBin introduces an algorithm-kernel co-design for ultra-low-bit LLM inference, combining Decoupled Row-Column Binary Decomposition with Hessian-guided saliency-aware hybrid bases to enhance representational capacity. The method employs a Lookup Table Building Approach with Scale Fusion and Virtual Columnar Mapping to optimize CUDA kernel execution. Evaluations show 5.92× speedup, 10.19× energy savings, and 4× memory reduction, enabling 70B-scale model deployment on a single A100 GPU while maintaining accuracy comparable to fine-tuned methods.
binary quantizationlookup tablecuda kernelhessian-guided saliencyultra-low-bit inference
Quantum Models with Multi-Stage Training for Compositional Concept Generalization
The paper introduces a quantum-based multi-stage training paradigm for Compositional Concept Generalization (CoCoGen), employing variational quantum circuits to separate noun and relation representations. The method first learns object embeddings from single-object image-caption pairs, then freezes these to train relational components as transformations over stable primitives, enforcing compositional factorization. Evaluated on CLEVR, the approach uses quantum encodings (amplitude/angle) for CLIP-derived image embeddings and tensor representations for text, achieving superior out-of-distribution generalization with fewer parameters than classical baselines. Nonlinear quantum encodings enhance separability, demonstrating quantum-structured representations' efficacy for multimodal CoCoGen.
compositional generalizationquantum circuitsmultimodal learningcontrastive encodingparameter efficiency
GraniKV: Asymmetric Granularity KV-Cache Paging for Multi-Agent Systems with Long Shared Prefix
GraniKV introduces asymmetric granularity KV-cache paging for multi-agent systems, addressing divergent storage requirements between long shared prefixes (requiring contiguity) and per-request suffixes (needing fine-grained allocation). The system allocates prefixes in a contiguous HOT pool and suffixes in a token-level COLD pool, using a per-step dispatcher to select optimal backends. Evaluations show throughput improvements of 2.16× (Llama-3.1-8B), 1.98× (Qwen-2.5-14B), and 1.57× (Qwen-2.5-32B) over baselines, with cascade attention contributing most at saturation and asymmetric storage adding 1.05–1.15× end-to-end.
kv-cachemulti-agent systemspaging granularitythroughput optimizationllm serving
SchurQuant: Groupwise Discrete Optimization for Layer-Wise LLM Quantization
The paper introduces SCHUROPT, a backpropagation-free post-training quantization (PTQ) method for large language models that addresses two limitations of existing optimizers: group decisions ignoring suffix corrections and fixed affine quantization grids. SCHUROPT analytically eliminates the suffix's optimal continuous response via Schur-complement curvature, alternating closed-form scale/zero-point refitting with integer code coordinate descent. Combined with additional techniques in SCHURQUANT, the method improves mean zero-shot accuracy on 2-bit Qwen3-4B by 11.88 percentage points (pp) over GPTQ and outperforms baselines by 9.65 pp across eight Llama/Qwen models at 2 bits.
post-training quantizationschur-complementcoordinate descentzero-shot accuracyaffine quantization
Amortised Post-Hoc Explanation with Exact Preservation for Dynamic Graph Anomaly Detectors
The paper introduces X-StrGNN, a post-hoc explanation layer for StrGNN dynamic graph anomaly detectors that provides dual structural and temporal attributions while preserving detection accuracy to machine precision (ΔAUC = 0.0000). The method employs multiplicative masks and achieves amortised attribution at 0.66 ms per edge, outperforming gradient attribution and per-instance optimization in stability (0.913) and cost efficiency (268x faster). Controlled experiments demonstrate superior temporal attribution (1.601 vs. random floor 0.973) and release full reproducibility materials.
post-hoc explanationdynamic graphanomaly detectionattribution maskamortised optimization
A Counterexample to the Tang Zhang Schatten Norm Conjecture and Sharp Positive Results
The paper refutes a conjecture by Tang and Zhang regarding the best constant $c_p(m)$ in Schatten norm inequalities for matrix sums, using explicit $2\times 2$ rank-one matrices at $p=3/2$ to demonstrate a counterexample. The proof relies on seven strict rational inequalities, showing the attained ratio exceeds $207/200$, contrary to the conjectured bound. Positive results include proving the conjecture for rank-at-most-one matrices when $2\leq p<\infty$, with full equality case classification, and establishing the sharp constant for $m=2, p=4$ with arbitrary complex matrices.
schatten normmatrix inequalitiesrank-one matricessharp constantscounterexample
RigidBench: Evaluating Rigid-Body Physics in Video Generation Models
RigidBench introduces a simulator-grounded benchmark for evaluating rigid-body physics in video generation models, addressing limitations of whole-frame metrics by separating motion, geometry, and other errors. The benchmark includes five tasks with varied objects, materials, and scenes, providing per-frame masks, depth, 6-DoF trajectories, and contacts for scoring. Evaluation of eight models on 100 examples reveals rankings vary by metric, with no model leading on all ten measurements, and shows a trade-off between SSIM and 3D trajectory error (r = 0.89). Fine-tuning Wan 2.2 TI2V-5B on 5,000 training videos reduces 3D trajectory error by 20% without affecting SSIM, while probes indicate object position is utilized throughout its diffusion transformer.
rigid-body physicsvideo generation6-dof trajectoriesdiffusion transformerssim
Spectral Saliency for Machine Unlearning
The paper introduces Spectral Saliency Unlearning (SSU), a gradient-based machine unlearning method that selectively updates model parameters by thresholding weak singular components. SSU employs spectral magnitude normalization to focus on confident unlearning directions, theoretically justified via a forgetting-retention trade-off analysis. Evaluations on image classifiers, diffusion models, and LLMs demonstrate its effectiveness in removing forget-set influence while preserving model utility.
machine unlearningspectral saliencygradient descentsingular componentsforgetting-retention trade-off
L3Cube-IndicQuest v2: A Large-Scale Multilingual Benchmark for Evaluating Factual Knowledge of Large Language Models Across Indic Languages
L3Cube-IndicQuest v2 introduces a multilingual benchmark with 69,420 question--answer pairs across 20 languages, including 19 Indic languages, for evaluating LLMs' India-specific factual knowledge. The dataset combines LLM-based question generation, semantic deduplication, and human verification for scalable, high-quality construction. Evaluation of six LLMs using LLM-as-a-judge and lexical matching protocols shows consistent rankings, with a commercial model leading and Gemma4 31B outperforming Indic-specialized Sarvam 30B across all tested languages.
multilingual benchmarkquestion-answeringllm evaluationsemantic deduplicationindic languages
DeltaLog: Deferred Materialization of Recurrent States for Linear Attention Decoding
DeltaLog introduces a deferred materialization scheme for recurrent states in linear attention decoding, reducing memory traffic without altering model semantics. The method represents states as a dense base plus a bounded log of compact updates, appending updates most steps and periodically merging them into the base. Implemented for GDN, KDA, and RWKV6, DeltaLog accelerates state updates by up to 1.86×, reduces write traffic by up to 7.83×, and achieves 1.05–1.20× end-to-end speedups over dense baselines.
linear attentionrecurrent statekv cachedeferred materializationserving stack
Guaranteed Adaptive Modality Acquisition: When the Policy Chooses Its Own Calibration Group
The paper introduces RouteCert, a method for guaranteeing conditional calibration in multimodal systems with adaptive input acquisition policies. It characterizes when pattern-conditional guarantees remain valid despite policy-induced grouping, proposing two finite-sample constructions: threshold-free routing with terminal-pattern calibration and simultaneous certification of policy-pattern pairs. On a clinical ECG task, the certified policy achieved 71.2% coverage with 7.4% diagnostic disagreement at 48.8% cost, while on masked multimodal benchmarks, it maintained worst-pattern selective risk at 0.034 versus 0.145 for pooled designs at comparable coverage (0.350 vs 0.342).
conditional calibrationadaptive acquisitionmultimodal systemsfinite-sample constructionsselective risk
UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity
UniFed-VLM introduces a federated instruction tuning framework for Vision-Language Models (VLMs) addressing multi-dimensional heterogeneity in tasks, modalities, and model architectures. The method combines Federated Compensated Subspace Aggregation (FedCSA) for subspace-aligned aggregation of parameter-efficient adapters and Two-stage Collaborative Distillation (TCoD) for cross-model knowledge transfer via a Mutual Distillation Adapter. Experiments on benchmark datasets demonstrate superior average performance across diverse tasks compared to existing FL methods.
federated learningvision-language modelsinstruction tuningparameter-efficient adaptersknowledge distillation
Temporal Logic Guided Universal Task Representations for Reinforcement Learning
The paper introduces LOTUS, a temporal logic guided universal task representation framework for reinforcement learning that addresses generalization and gradient dependency limitations in existing methods. LOTUS employs a novel architecture for modeling Linear Temporal Logic (LTL) formulas and introduces a policy-based update mechanism, enhanced by bisimulation metrics for theoretical guarantees on representation quality. Experiments demonstrate LOTUS improves convergence by 20% in single-task settings, achieves 15%-45% higher success rates in unseen manipulation tasks, and enhances generalization by 25% in complex multi-task environments.
temporal logictask representationreinforcement learningbisimulation metricltl formulas
Do Language Models Consistently Encode the Current Year?
The study investigates how language models encode the current year through two distinct probing tasks: an associative task (inferring year from verb tense) and a declarative task (direct year query). For instruction-tuned models, both tasks estimate years within one year of post-training cutoff, while base models' associative task predictions align with pre-training cutoffs (10-month average error). Mechanistically, the associative task resembles factual recall, whereas the declarative task lacks consistent causal pathways. Updating methods (prompting, SFT, weight editing) fail to synchronize both representations—prompting shifts declarative years (94.6% success) but not associative (1.7%), highlighting inconsistent temporal encoding.
temporal reasoninglanguage modelsin-context learningweight editingcausal mechanisms
PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification
The paper introduces PERO, an efficient robust post-training framework for encrypted traffic foundation models that addresses high-risk tail events overlooked by standard objectives. PERO employs a lightweight proxy to estimate sample-wise risk, selecting high-risk subsets for model updates, thereby decoupling risk estimation from costly large-model optimization. Experiments on encrypted traffic datasets demonstrate PERO's competitive robustness and average performance versus existing methods, with significantly reduced computational and memory overheads.
encrypted traffic classificationrobust optimizationfoundation modelspost-trainingrisk estimation
QSMP: finding representative time series subsequences through Quick Shift+Matrix Profile
QSMP introduces a density-guided clustering method for identifying representative waveforms in long time series by combining Quick Shift, a mode-seeking algorithm, with Matrix Profile, a similarity-search data structure. This novel integration adapts Quick Shift for subsequence clustering with improved space complexity over state-of-the-art approaches. Experiments on synthetic and real datasets demonstrate QSMP's effectiveness in summarizing and visualizing long time series through representative waveform extraction.
quick shiftmatrix profiletime series clusteringsubsequence extractiondensity-guided clustering
Measuring Structured Predictability in Neural Training Dynamics: A Cross-Regime Study
The paper introduces short-horizon predictability as a diagnostic for analyzing temporal structure in neural network training dynamics. Using displacement-direction, subspace-residual, and predictor-based probes with null-calibrated group-level readouts, the study examines multi-pass vision training on CIFAR and Pythia pretraining checkpoints. Results reveal that auxiliary parameters (e.g., normalization, biases) exhibit simpler dynamics than bulk parameters (matrix-like weights), with predictable behavior localized in time-varying pockets. Architecture and training recipes systematically modulate this structure, as demonstrated in controlled CIFAR comparisons and Pythia-70M case studies.
short-horizon predictabilitytraining dynamicsauxiliary parametersbulk parametersnull-calibrated probes
Optimal Lower Bounds for Networked Information Aggregation
The work resolves an open problem in networked information aggregation by establishing a tight Ω(1/√D) lower bound on mean squared error (MSE) for learners on directed acyclic graphs, matching the prior O(1/√D) upper bound. The analysis exploits structural invariances in learned predictors and generalizes to convex losses (e.g., logistic loss) satisfying strong convexity and positive correlation conditions. For Gaussian instances in the constructed worst-case family, the ℓ-error is similarly lower bounded by Ω(1/√D) relative to the ideal predictor, closing gaps in prior work by Kearns et al. (2026) and Bateni et al. (2026).
networked information aggregationdirected acyclic graphmean squared errorconvex losslower bound
Population Structure Analysis of an Inbred Population using Quantitative Shape Phenotyping from Stereo Retinal Photographs
The study analyzes population structure in an inbred Norfolk Island cohort (n=781) using quantitative 3D optic nerve head (ONH) shape phenotypes derived from stereo retinal fundus photographs. A multi-scale stereo matching algorithm reconstructs ONH morphology, followed by hierarchical feature extraction via deep neural networks for self-taught learning. Selected features achieve discriminant power in stratifying descendants of Bounty mutineers (n=318) from others through cross-validated clustering (k=2-7), enabling phenotypic heritability analysis of founder effects from English and Polynesian ancestry for ONH-related disease risk.
optic nerve headstereo matchingself-taught learningpopulation structureheritability analysis
High-Dimensional Nonparametric Change-Point Detection via Low-Rank Degree-Three Density Projection
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Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labeling
The study addresses IoT device identification under concept drift by proposing a budget-aware traffic labeling strategy that separates sample selection from labeling rate determination. Through a two-year longitudinal analysis of 3.8M IPFIX flow records from 21 IoT types, the authors (1) characterize behavioral evolution across device classes, (2) develop a conformity-based drift detector with feature-level explanations, and (3) demonstrate that uniform sampling with drift-adjusted labeling rates outperforms detector-guided selection, maintaining F1-score within 5% of confidence-based methods while reducing labeling effort.
iot device identificationconcept drifttraffic labelingconformity-based drift detectionipfix flow records
📰 Industry Media (10)
We still don’t know how people are really using AI
The AI Observatory project addresses the lack of independent data on generative AI usage by aggregating 85,633 conversational turns from 24,521 real-world interactions with models like ChatGPT, Claude, and Gemini across seven datasets (2023-2025). Analysis reveals significant disparities in usage patterns compared to company reports: 48% of conversations were non-work-related (e.g., health, adult content), model-specific trends emerged (e.g., Grok for politics, Claude for coding), and temporal shifts occurred (longer conversations, reduced safeguards). The findings highlight biases in proprietary datasets and enable independent research on sensitive AI applications.
generative aiconversational turnssensitive behaviorsproprietary datasetsmodel-specific trends
AI’s recursive self-improvement might not come so quickly after all
A Princeton-led study challenges near-term timelines for AI recursive self-improvement, demonstrating current limitations in open-ended research capabilities. Researchers evaluated Anthropic's Claude Opus 4.8 through shadow evaluation, requiring it to produce NeurIPS-worthy papers on unpublished research questions involving LLM weight editing and spreadsheet-model reliability detection. Despite engineering competence (literature review, experiment execution), agents failed in research judgment—committing prematurely to hypotheses, mismanaging resources, and producing substandard papers (both rejected by original authors). The gap highlights deficiencies in creative problem-solving and methodological flexibility, suggesting current reinforcement learning paradigms may not suffice for autonomous AI research advancement.
recursive self-improvementshadow evaluationllm personasreinforcement learningopen-ended research
What Flock’s defenders are missing
Flock, a police-tech company operating 120,000 automatic license plate readers in the US, implemented new safeguards to prevent misuse by officers, such as requiring case numbers for searches. However, these measures lack verification, allowing officers to bypass restrictions. The article critiques Flock's design choices regarding data collection, retention (often exceeding the recommended 7 days), and nationwide sharing, arguing for narrower surveillance parameters aligned with specific use cases like Amber Alerts. Despite Flock's $8 billion valuation, cities are reconsidering contracts amid civil liberties concerns, prompting debates on balancing crime-solving efficacy with privacy protections.
automatic license plate readersmass surveillancedata retentionamber alertscivil liberties
Meet SAM (Sovereign Agent Mesh): A Zero-Config, Zero-Trust P2P Network for AI Agents
Sovereign Agent Mesh (SAM) introduces a zero-configuration, zero-trust peer-to-peer network for autonomous AI agents, enabling secure tool sharing across heterogeneous environments without exposing internal APIs. The system employs a three-component architecture (control plane, router, node) using libp2p for transport and Biscuit tokens for offline authorization via OIDC-to-Datalog translation. SAM supports production deployment via Go binaries, Docker images, and Kubernetes, with strict default-deny policies and granular access control through capability facts.
peer-to-peerzero-trustbiscuit tokenslibp2pmodel context protocol
Cartesia Ships Sonic-3.6: A Streaming TTS Model That Now Leads Both Artificial Analysis Speech Arenas
Cartesia introduces Sonic-3.6, a state-space-model-based streaming TTS system achieving state-of-the-art performance on Artificial Analysis benchmarks (1,283 Elo for Provider Voice, 1,123 Elo for Controlled Voice). The model features sub-90ms time-to-first-audio latency and supports production-oriented features like inline expression tags, instant voice cloning from 10s samples, and custom pronunciation dictionaries. It outperforms ElevenLabs Eleven v3 (1,123 vs. lower Elo) at half the cost ($49/1M characters). The closed-source model is available via API with tiered pricing but lacks self-hosting options.
text-to-speechstate space modelselo ratingtime-to-first-audiovoice cloning
Nous Research Ships Bot Mode for Hermes Agent, Turning Agent Profiles Into a Roster of Named Bots
Nous Research introduces Bot Mode for Hermes Agent v0.20.3, enabling multiple named agent profiles with isolated memory, skills, and model configurations. Each bot operates as a distinct Hermes profile (~/.hermes/profiles/), communicating via CLI handoffs to a persistent Agent Inbox and supporting @mention workflows. The feature supports group chats (2-6 bots), avatar generation via image.generate RPC, and cron job automation. Bundled as a default plugin in the MIT-licensed desktop client, it targets individual developers and small teams without enterprise management features.
agent profilescli handoffscron jobsavatar generationmodel pinning
ByteDance Seed and Tsinghua AIR Introduces CUDA Agent: A Large-Scale Agentic RL System for CUDA Kernel Generation
ByteDance Seed and Tsinghua AIR introduce CUDA Agent, an agentic RL system that optimizes CUDA kernel generation via in-environment PPO training. The system trains the proprietary Seed1.6 MoE model (23B active/230B total params) within a sandboxed CUDA development environment, using discrete rewards and anti-hacking measures. On KernelBench, it achieves a 98.8% pass rate and 2.11× geomean speedup over torch.compile, outperforming Claude Opus 4.5 and Gemini 3 Pro by 40 points on Level-3 tasks. The work releases CUDA-Agent-Ops-6K dataset and SKILL.md spec but not model weights.
agentic rlcuda kernelmoe modelppo trainingkernelbench
MiniMax Releases MiniMax-Music3: An Open-Weights Music Model Generating Complete Five-Minute Songs From Lyrics and a Structured Caption
MiniMax released MiniMax-Music3, an open-weights text-to-music model capable of generating complete 32 kHz, 16-bit stereo songs up to five minutes long from structured lyrics and captions. The architecture combines an 8B Global LLM (Qwen3/3.5-8B) for semantic structure and a 0.6B Local LLM for acoustic detail, with a continuous synthesis stack using flow matching and a 123M Flow-VAE. The model achieves inference via three deployment paths: SGLang-Omni (2 GPUs), diffusers (<24GB VRAM), and ComfyUI (8GB with offloading), licensed for commercial use with attribution under revenue limits.
hybrid-lmflow-vaeresidual vector quantizationflow matchingautoregressive stack
Developing an End-to-End Document Intelligence Pipeline with docTR for OCR, Layout Analysis, KIE, Benchmarking, and Searchable PDFs
The tutorial presents an end-to-end document intelligence pipeline using docTR, integrating OCR, layout analysis, and key information extraction (KIE). Methodologically, it demonstrates synthetic document generation, GPU-accelerated OCR with configurable detection-recognition architectures (e.g., db_resnet50 + crnn_vgg16_bn), and performance benchmarking across 4 model combinations (0.1-1.0s/page). Results include 92-98% word accuracy on synthetic invoices, geometric reconstruction of tabular data, and export to searchable PDFs with 150dpi fidelity. The pipeline handles document rotation (13° tested), low-confidence words via two-pass recognition, and structured field extraction.
optical character recognitiondocument understandinglayout analysiskey information extractionsearchable pdf
DeepSeek AI Releases DeepSeek Harness in Developer Preview: An MIT-Licensed Agent Harness Where Everything is a Plugin
DeepSeek AI introduces DeepSeek Harness v0.1, an MIT-licensed developer preview for assembling agent runtimes via a plugin-based architecture. Built on the Cordis meta-framework, the harness modularizes models, tools, skills, sessions, and UI components, enabling configuration-driven customization without source code modification. It supports four runtime modes—Standard, Code, Minimal, and Creator—and logs every context injection in an append-only session log for traceability. The system is provider-agnostic, integrating APIs from DeepSeek, Anthropic, OpenAI, and others. Designed for AI-native startups and enterprises, it targets applications in coding agents, model evaluation, and agent observability.
plugin-based architecturecordis meta-frameworkappend-only session logprovider-agnosticruntime modes
Generated automatically at 2026-08-18 20:01 UTC. Summaries and keywords are produced by an LLM and may contain inaccuracies — always consult the original article.