Daily Digest — 2026-07-09
242 items · 6 research labs, 231 arxiv papers, 5 industry media
🏛️ Research Labs (6)
Separating signal from noise in coding evaluations
OpenAI identifies significant flaws in SWE-Bench Pro, estimating ~30% of its 731 coding tasks are broken due to overly strict tests, underspecified prompts, low-coverage tests, and misleading prompts. They employ a dual-method audit pipeline combining Codex-based investigator agents (flagging 286 tasks) with human reviews by five experienced engineers per task, revealing conservative labeling discrepancies (74% agreement on flagged categories). Results demonstrate the challenges in curating reliable benchmarks for agentic coding evaluations, prompting retraction of prior SWE-Bench Pro recommendations.
swe-bench proevaluation flawsagentic codingcodex-based audittest contamination
Helping K–12 educators build practical AI skills
OpenAI Academy, in partnership with the Walton Family Foundation, launches the AI Skills Jam for K–12 Educators, a series of in-person workshops targeting 1,600 U.S. educators. The initiative aims to bridge the gap in AI literacy by providing hands-on training in practical applications like lesson planning, administrative tasks, and communication, leveraging OpenAI's resources and mentorship. Preliminary research indicates weekly AI tool use saves teachers 5.9 hours (∼6 weeks annually), reinvested in student feedback and individualized instruction. Workshops will occur across eight U.S. cities from July to September 2024, with continued support via the OpenAI Academy platform.
ai literacyin-person workshopslesson planningadministrative automationeducator training
Introducing GPT-Live
OpenAI introduces GPT-Live, a full-duplex voice AI system enabling continuous, natural conversations by simultaneously processing input and generating output. The architecture combines real-time interaction with delegation to backend models (initially GPT-5.5) for complex tasks. Evaluations show GPT-Live-1 outperforms Advanced Voice Mode on GPQA (expert reasoning), BrowseComp (web search), and τ³-Voice Telecom (multi-turn support), with human evaluators strongly preferring its conversational flow. Safety features include real-time safeguards and expanded audio-native testing. Two variants (GPT-Live-1 and mini) are rolling out globally for ChatGPT Voice.
full-duplexdelegation architecturecontinuous interactionaudio-native evaluationsτ³-voice telecom
Data for Agents
NVIDIA's Nemotron initiative addresses the data challenges in developing robust AI agents by releasing open synthetic datasets, including over 10 trillion pretraining tokens and millions of post-training samples. The approach emphasizes inspectable agent behavior through tools like the Nemotron Post-Training v3 Prompt Atlas, which visualizes prompt samples for domain-specific analysis. Synthetic data enables privacy-preserving signal retention while fostering collaborative ecosystem development, as demonstrated by Nemotron-Personas, which models demographic diversity across 2.4B people. The work highlights synthetic thresholds and open methods as key to balancing data utility with organizational trust.
synthetic dataai agentspretraining tokenspost-training samplesdemographic fidelity
Native-speed vLLM transformers modeling backend
The Hugging Face transformers library now achieves native-speed inference in vLLM through dynamic runtime optimizations, eliminating the need for manual model porting. The method employs torch.fx for static graph analysis and AST manipulation to fuse operations into optimized vLLM kernels (e.g., MergedColumnParallelLinear, QKVParallelLinear), supporting tensor/expert parallelism and CUDA Graph compilation. Benchmarks show parity with hand-optimized vLLM implementations across Qwen3 models (4B-235B parameters) while maintaining training compatibility. The approach enables seamless deployment of 450+ transformers architectures with vLLM's continuous batching and attention optimizations.
torch.fxtensor parallelismcuda graphsmixture-of-expertskv-cache
From Hugging Face to Amazon SageMaker Studio in one click
Hugging Face and Amazon SageMaker Studio introduce a one-click integration enabling direct model deployment and customization from Hugging Face to SageMaker Studio. The method eliminates manual AWS configuration by automating domain provisioning, IAM permissions (via AmazonSageMakerModelCustomizationCoreAccess policy), and GPU quota visibility. Results show streamlined workflows for fine-tuning (SFT, DPO, RLVR, RLAIF) and deployment, reducing setup time from discovery to experimentation in AWS environments.
hugging faceamazon sagemaker studioiam permissionsgpu quotafine-tuning
📜 arXiv Papers (231)
ELSA3D: Elastic Semantic Anchoring for Unified 3D Understanding and Generation
ELSA3D introduces elastic semantic anchoring for unified 3D understanding and generation, addressing the implicit text-3D interaction in existing methods. The model employs a scale-aware octree tokenizer and Anchor Tokens to structure language and geometric reasoning across matched abstraction scales, with a lightweight per-block router enabling sparse yet precise cross-modal interaction. ELSA3D achieves state-of-the-art performance in image-to-3D generation, text-to-3D generation, and 3D captioning, reducing FLOPs and inference latency by approximately 50% compared to non-elastic baselines.
elastic semantic anchoringoctree tokenizeranchor tokenscross-modal interaction3d generation
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
The paper introduces Graph Convolutional Attention (GCA), a spectral-based attention mechanism for graph denoising and diffusion, addressing limitations of linear attention which averages spectral filters. GCA leverages graph-filtered queries/keys to implement spectral denoising, proving optimal for stochastic block models and permutation-equivariant. Theoretical analysis shows softmax further denoises by projecting noisy eigenvectors. Experiments demonstrate GCA's superiority over linear attention, with gains scaling with spectral diversity; in DiGress, it matches graph-transformer performance without costly structural features or eigendecomposition when combined with PEARL encodings.
graph denoisingspectral attentiongraph convolutional attentiondiffusion modelspermutation-equivariant
Rethinking Indic AI from a Lens of Cultural Heritage Preservation
The paper characterizes the dual impact of AI on Indian linguistic and cultural preservation, analyzing both inclusion opportunities and homogenization risks. Through a longitudinal survey of Indic NLP development, it examines structural challenges like rich morphology, complex scripts, and diglossia in building foundation models. The authors propose 'Culture Sensing' as a hermeneutic framework to address low-resource language performance gaps and culturally meaningful outputs, outlining future directions for inclusive Indic AI systems.
indic nlpculture sensingfoundation modelsdiglossiamorphological complexity
The Large Cancer Assistant (LCA): A Model-Agnostic Orchestration Framework for Scalable Clinical Decision Support in Oncology
The Large Cancer Assistant (LCA) introduces a model-agnostic orchestration framework for scalable clinical decision support in oncology, decoupling multimodal data ingestion from AI inference. The architecture employs Algorithmic Impermeability and Geometric Deep Learning (GDL) to standardize patient data via a 7-tuple formalism, with dynamic routing through a Cancer Switching Module and isolation via Standardized Intermediate Payload (SIP). A Proof of Concept demonstrated negligible overhead, invariant routing during model swaps (algorithmic impermeability), 100% recall for Supplementary Data Requests under anomalies, and multi-protocol execution.
algorithmic impermeabilitygeometric deep learningcancer switching modulestandardized intermediate payloadmultimodal orchestration
RSF-GLLM: Bridging the Semantic Gap in Multi-Hop Knowledge Graph QA via Recurrent Soft-Flow and Decoupled LLM Generation
RSF-GLLM introduces a framework for multi-hop KGQA that decouples differentiable graph reasoning from answer generation to address the semantic gap problem. The method combines a Recurrent Soft-Flow (RSF) module with GRU-guided query updating and flow sparsity regularization to enable continuous relevance propagation and discrete path extraction, followed by LLM fine-tuning on textualized paths. Experiments on WebQSP and CWQ show competitive performance with improved inference efficiency compared to pure LLM approaches.
multi-hop qaknowledge graphsrecurrent soft-flowsparsity regularizationllm fine-tuning
DepthWeave-KV: Token-Adaptive Cross-Layer Residual Factorization for Long-Context KV Cache Compression
DepthWeave-KV introduces token-adaptive key-value cache compression for long-context language models by factorizing states across transformer layers using shared low-rank bases and token-specific residuals. The method employs a depth router to prioritize reconstruction rank for critical tokens and uses online attention-output probes for calibration-free adaptation. Evaluated on LongBench, Needle-in-a-Haystack, and other benchmarks, it achieves near-full-cache performance with 8.3x memory reduction and 72.8 tokens/second at 64K context.
kv-cachelow-rank factorizationtoken-adaptiveattention-output probesmemory reduction
Bridging Physical Reasoning and Task Generalization via Visual Action Outcome Reasoning Alignment
The paper introduces VAORA (Visual Action Outcome Reasoning Alignment), a novel reward design addressing two key failure modes in vision-language models (VLMs) for physical reasoning: hallucinated chain-of-thought reasoning and reasoning-action misalignment. VAORA combines Visual Alignment Reward (anchoring reasoning to visual context) and Visual-Action Alignment Reward (grounding reasoning in action-induced outcomes), supplemented by dense rewards from a pre-trained expert agent. Experiments on PHYRE and Virtual Tool benchmarks demonstrate improved generalization in novel-task and unseen-environment settings.
vision-language modelsphysical reasoningreward designchain-of-thoughtgeneralization
FreqDepthKV: Frequency-Guided Depth Sharing for Robust KV Cache Compression in Long-Context LLM Inference
FreqDepthKV introduces a frequency-guided KV cache compression method for long-context LLM inference, factorizing adjacent-layer KV states into shared low-frequency depth components and sparse high-frequency residuals. A lightweight online probe dynamically assigns attention heads to shared-depth, residual-depth, or exact cache modes based on their impact on reconstruction-sensitive attention logits. Evaluated on long-context QA, needle retrieval, summarization, and code generation, FreqDepthKV achieves 58.3 EM, 63.0 F1, 32.5 ROUGE-L, and 48.1 pass@1 with a 32k-token prefill, matching full KV performance while reducing peak memory to 6.2 GB (3.9x compression) and improving throughput to 70.4 tokens/s.
kv cachelong-context inferencefrequency decompositionattention logitscache compression
FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games
The authors introduce FootsiesGym, an open-source benchmark environment for studying two-player zero-sum imperfect-information games, based on the minimalist 2D fighting game Footsies. The environment captures cyclic, non-transitive strategic interactions in fighting game neutral play while maintaining computational efficiency through a vectorized simulator for high-throughput training. Initial benchmarks of reinforcement learning algorithms demonstrate the platform's utility for reproducible research in game-theoretic AI. The implementation is publicly available.
imperfect-information gamesreinforcement learningvectorized simulatornon-transitive strategiestwo-player zero-sum
Industry Classification of GitHub Repositories Using the North American Industry Classification System (NAICS)
NAICS-GH introduces a corpus of 6,588 GitHub repositories labeled with 2-digit North American Industry Classification System (NAICS 2022) sectors, addressing the lack of standardized industry mappings on GitHub. The labeling pipeline combines BAAI/bge-large-en embeddings, FAISS retrieval, and GPT-4.1 rubric scoring, narrowing 1.37 million repositories to 31,178 candidate pairs and retaining high-confidence labels with scores ≥8. The pipeline achieves 96.98% precision on a human-validated sample and reproducibility within 0.03%. RoBERTa-large attains 86.45% F1 and 86.35% accuracy on a held-out test set. The dataset, metadata, pipeline code, and fine-tuned checkpoint are publicly released.
naicsgithub repositoriesfaiss retrievalgpt-4.1roberta-large
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
The paper introduces RMISC, a large-scale real-world multivariate time series corpus containing 200 datasets and 142 billion time points across diverse domains, addressing the gap in pretraining data for time series foundation models (TSFMs). The authors pretrain four advanced TSFMs on univariate, synthetic multivariate, and real-world multivariate data, evaluating their zero-shot generalization on standard benchmarks. Results demonstrate that incorporating real-world multivariate data significantly improves generalization performance for both univariate and multivariate TSFMs, providing insights into data requirements for stronger TSFMs.
time series foundation modelsmultivariate corpuszero-shot generalizationpretraining datareal-world time series
Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade
The paper introduces a recall-controlled probe cascade for early abort of doomed LLM agent episodes, predicting failure from hidden activations rather than observable behavior. The method employs per-round calibrated gates with jointly searched recall budgets, ensuring episode-level recall guarantees while saving inference compute. Results on TextCraft with Qwen-2.5-7B and Llama-3.2-3B show 37.2-47.1% compute savings at 90% recall, outperforming single-gate policies by 1.6-1.7x. Hidden states prove more informative than behavioral features, and the work provides sample complexity bounds for recall certification.
llm agentshidden activationsrecall-controlledinference computeprobe cascade
Pitwall: Faithful Natural-Language Race-Strategy Briefings from a Calibrated Real-Time Monte Carlo Engine
Pitwall introduces a production system for generating faithful natural-language Formula 1 strategy briefings in English, Spanish, and Portuguese, treating faithfulness as an architectural property. The system decomposes sentences into typed factual claims verified against a probabilistic race state from a calibrated Monte Carlo engine (N=2,000 per-lap continuations, Brier score 0.0745 on held-out data). Fine-tuning uses only state-supported targets (81.9% of 3,045), with fallback to provably faithful templates. Live deployment at two 2026 Grands Prix demonstrated end-to-end operation, including accurate pre-race winner predictions.
faithful generationmonte carlo engineprobabilistic verificationfine-tuninglive deployment
Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms
The paper proposes a two-layer control system for optimizing battery management in Irish dairy farms, combining differential evolution and multi-agent deep reinforcement learning (MADRL). The upper layer implements dynamic pricing, while the lower layer employs MADRL for distributed battery control. Simulations demonstrate an 18% profit increase in energy arbitrage versus rule-based models, enhanced renewable energy utilization without substantial cost escalation, and compliance with Irish grid voltage regulations.
multi-agent reinforcement learningbattery managementdifferential evolutionenergy arbitragedistributed generation
AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models
AirflowAttack introduces the first adversarial attack targeting infrared (IR) remote-sensing vision-language models (VLMs), leveraging thermal-airflow turbulence as a perturbation prior. A lightweight generator synthesizes input-agnostic perturbations regularized toward physically plausible airflow patterns, optimized on a surrogate CLIP model. The attack achieves a mean zero-shot scene-classification attack success rate (ASR) of 48.5% across five CLIP backbones, outperforming IR-specific baselines (27.7--37.0%), and reduces scene-classification accuracy by up to 38.2% on six state-of-the-art VLMs, while paradoxically increasing model confidence in some cases.
adversarial attackinfrared remote sensingvision-language modelsthermal-airflow turbulenceperturbation prior
Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
The paper introduces DataGovBench, a benchmark for evaluating Large Language Models (LLMs) in real-world data analysis scenarios, addressing limitations of current benchmarks that focus on small tables and neglect complex tasks. Derived from governmental open data, DataGovBench includes two tasks: Table QA for complex decomposable questions requiring textual or visual answers, and Table Insight for generating expert-level findings through exploratory analysis. Experiments with state-of-the-art LLMs, both with and without agentic frameworks, reveal significant performance gaps, indicating current systems fall short of practical data analytics demands.
large language modelsdata analysisbenchmarkexploratory analysistable qa
Prompt-Adapter Context Routing for Parameter-Efficient Multi-Shot Long Video Extrapolation
The paper introduces PACR-Video, a parameter-efficient framework for multi-shot long video extrapolation that avoids full generator fine-tuning. The method augments a frozen text-to-video diffusion transformer with low-rank temporal adapters conditioned by learned shot-role prompt tokens, using a recursive prompt bank to maintain long-horizon coherence through adapter gating. Evaluated on six benchmarks, PACR-Video outperforms baselines in distributional quality, semantic alignment, identity consistency, and human preference, demonstrating that prompt routing and lightweight adaptation suffice for stable long video generation.
prompt routinglow-rank adaptersmulti-shot extrapolationdiffusion transformertemporal coherence
A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems
The study presents a physics-informed neural network (PINN) framework for modeling transient elastodynamic wave propagation in bimaterial systems, governed by axisymmetric linear elasticity equations. The method incorporates governing equations, initial/boundary conditions, and interface conditions into a physics-informed loss function, validated against high-fidelity ANSYS Workbench Explicit Dynamics simulations. Results show accurate prediction of wave transmission/reflection, displacement histories, and stress/strain evolution, with generalization to unseen time instants and material properties. The framework demonstrates numerical robustness and computational efficiency as a surrogate model for high-rate solid mechanics applications.
physics-informed neural networkselastodynamic wave propagationbimaterial systemssplit hopkinson pressure barsurrogate modeling
Provable learning separation for predicting time-evolution of quantum many-body systems
The work establishes a provable learning separation for predicting quantum many-body system dynamics, demonstrating a task where quantum algorithms outperform classical ones. The authors formulate a supervised learning problem using randomized stabilizer probe states and observable expectation values under an unknown Hamiltonian, with training samples from uniform time intervals. They present an efficient quantum procedure combining Hamiltonian simulation and classical shadows for inference, while proving classical hardness via BQP-complete computation embedding in Feynman-Kitaev clock Hamiltonians. Results show quantum learnability persists for classically hard instances, connecting quantum learning theory, simulation, and QML.
quantum machine learninghamiltonian simulationlearning separationclassical shadowsbqp-completeness
From Voting to Agent Collaboration: Answer-Type-Aware LLM Pipelines for BioASQ 14b
The study introduces a question-type-specific LLM framework for BioASQ 14b Task B, enhancing biomedical question answering through tailored inference procedures for yes/no, factoid, and list questions. For yes/no questions, snippet shuffling and self-reflection improve decision stability; factoid questions use full-snippet input with chain-of-thought-based in-context learning; list questions employ a multi-agent architecture for collaborative evidence processing. Evaluated on BioASQ 14b, the framework achieved competitive performance, including first place in the factoid subtask of Batch 4, demonstrating the efficacy of type-specific strategies and agent collaboration.
biomedical question answeringlarge language modelchain-of-thoughtmulti-agent architectureevidence grounding
Danus: Orchestrating Mathematical Reasoning Agents with Fact-Graph Memory
Danus introduces a fact-graph-based orchestration system for research-level mathematical reasoning, addressing challenges in parallel proof search coordination and intermediate claim reliability. The system comprises a main planning agent, parallel worker agents for proof search, and a stateless verifier that validates claims before integration into a shared fact graph. Verified facts are stored with proofs and dependencies, enabling incremental construction of long arguments. Evaluated on six case studies in algebraic geometry, singularity theory, and combinatorics, Danus demonstrates effective scaling for long-horizon mathematical proofs.
mathematical reasoningfact graphproof searchorchestration systemresearch-level problems
Analysis-by-Proxy: Localization Signals in VLMs Operating as Condition Encoders
The study identifies a performance gap in vision-language models (VLMs) when used as condition encoders for diffusion-based image editing, attributing it to the loss of spatial localization signals under single-pass constraints. The authors propose Analysis-by-Proxy, a framework employing a lightweight proxy model trained on VLM intermediate representations to localize spatial information. Results reveal that current editing pipelines fail to extract localization signals from predefined layers, as these signals reside in prompt-dependent intermediate representations, suggesting a need for more principled conditioning architectures.
vision-language modelscondition encoderlocalization signaldiffusion-based editingintermediate representations
Finding H. pylori in the Fine Print: Evidence-Linked Multi-Agent Case Finding from Gastric Biopsy Reports
The study introduces Nimblemind Multi-Agent System (nMAS), a field-name-driven, evidence-linked extraction workflow for identifying Helicobacter pylori infection and associated gastritis from heterogeneous gastric biopsy reports. Evaluating 54 de-identified reports from Singapore, nMAS achieved 98.61% accuracy (213/216 correct classifications) across four clinician-scoped binary fields. While predictive performance matched a UMA-style MiniMax M2.5 comparator, nMAS uniquely provided unified report-level outputs with traceable source sentences, demonstrating workflow efficiency gains (potential 81.9 staff-hour reduction per 1,000 reports).
multi-agent systemevidence-linked extractionhelicobacter pylorigastric biopsyclinical text mining
TILDE: TILt-based Distributional Erasure for Concept Unlearning
TILDE introduces TILt-based Distributional Erasure for concept unlearning in text-to-image diffusion models, addressing privacy, copyright, and safety concerns. The method formulates unlearning as a distributional alignment problem, targeting the minimum-deviation conditional distribution from the pretrained model under a forgetting constraint. It employs residual ∇-GFlowNet training to learn the score correction induced by the forget energy relative to the pretrained diffusion model. Evaluations across objects, artistic styles, and characters demonstrate strong forgetting while improving retention and distributional fidelity over prior baselines.
concept unlearningdistributional alignmentdiffusion modelsscore correctiongflownet
An Experimental Design Approach to Evaluating Agentic AI's Autonomous Model Discovery
The authors propose an experimental design framework to evaluate stochastic model-discovery behavior in agentic AI systems, treating LLM coding agents (Codex, Claude Code) as operators mapping task-specific data to fitted models. The method systematically varies factors (reasoning effort, task, metric, training data) and analyzes responses (output quality, cost, time, complexity) via regression and utility-aligned canonical decomposition. Results on word-forming games reveal insights into reasoning effort's alignment with cost-process complexity tradeoffs.
model-discovery operatorsutility-aligned decompositionreasoning effortstochastic agentsautonomous evaluation
RuBench: A Repository-Level Agentic Coding Benchmark with Natively Authored Russian Task Specifications
RuBench 1.0 introduces a repository-level agentic coding benchmark with natively authored Russian task specifications, addressing the gap in evaluating coding agents on non-English customer-style requests. The benchmark comprises 25 tasks mined from recent fix commits in five open-source repositories, with tasks written in Russian and judged by upstream maintainers' regression tests. Evaluations of deployed product configurations (Claude Code and Codex CLI) show the best configuration resolves 78.7% of tasks, with statistically resolvable gaps only to the weakest model. The study also reveals evidence of model substitution in deployed products, highlighting the complexity of measuring real-world agent performance.
repository-levelagentic codingregression teststask specificationsmodel substitution
ExplAIner: A Declarative Query Language for Explaining Classification Models
The paper introduces ExplAIner, a declarative query language for explaining Boolean classification models, addressing limitations in FOIL's expressiveness and computational complexity. ExplAIner extends FOIL with a layered structure and expanded vocabulary, supporting abductive, contrastive, feature-based, and distance-based explanation queries. Theoretical analysis shows ExplAIner queries are evaluable in the Boolean hierarchy for tractable Boolean model classes, with Opt-FOIL (an optimization fragment) solvable in FP^NP, enabling efficient SAT solver-based evaluation.
declarative query languageboolean modelsexplainable aipolynomial hierarchysat solver
What Images Cannot Say: Language-Guided Olfactory Representation Learning
The paper introduces SCENT, a multimodal framework for olfactory representation learning that bridges vision and smell through language guidance. The method leverages Vision-Language Models (VLMs) to generate scene descriptors from images, which provide semantic context for training a smell encoder that maps electronic-nose signals into a shared embedding space. A language-guided latent decomposition separates object-specific odors from environmental contributions. Evaluated on the New York Smells dataset, SCENT achieves state-of-the-art performance in crossmodal retrieval (smell-to-image and smell-to-text) and produces interpretable, disentangled olfactory representations.
multimodal learningolfactory representationvision-language modelscrossmodal retrievallatent decomposition
A Definition and Roadmap for World Models
The article proposes a scientific definition and development roadmap for world models, addressing the lack of consensus in AI research. It characterizes world models as internal simulators that learn environmental structure and dynamics, emphasizing their role across AI subfields. The authors discuss key technical aspects, including prediction targets and construction methodologies, while outlining a staged approach for building effective world models. This framework aims to unify diverse applications, from model-based reinforcement learning and video generation to embodied robotics and physical AI systems.
world modelsinternal simulatorsmodel-based reinforcement learningvideo generationembodied robotics
TopoBrick: Agentic Topology Sampling of Exogenous Variables for Zero-Shot Building IoT Forecasting
TopoBrick introduces a training-free framework for zero-shot forecasting in building IoT systems by leveraging structural topology and agentic variable selection. The method constructs knowledge graphs to model sensor relationships, then employs an agentic topology sampler to dynamically select target-specific exogenous variables, partitioned by temporal availability. Evaluated across three real buildings, TopoBrick outperforms zero-shot foundation models and matches trained building-specific models, with topology-aware sampling proving superior to random or fixed-hop alternatives for HVAC and weather-dependent variables.
zero-shot forecastingknowledge graphsexogenous variablesiot systemstopology sampling
Responsible Personalisation: The Double-Edged Sword of Personalisation in Human-Robot Interaction
The paper contributes a lifecycle-based framework for analyzing ethical risks in personalized human-robot interaction (HRI), addressing the fragmented literature on responsible personalization. The method combines personalization process stages with interaction characteristics (short/long-term, open/closed-domain) through an embodiment-aware perspective. Results include systematic analysis of risks (autonomy erosion, biased modeling, manipulation, dehumanization, privacy violations) across contexts, leading to design recommendations and research challenges for ethically grounded HRI.
human-robot interactionpersonalization lifecycleethical risksembodiment-awarecontext-sensitive framework
Harnessing Code Agents for Automatic Software Verification
The paper introduces Aria, a fully automated software verification system combining LLM code agents (e.g., Claude Code) with a verification harness. Unlike prior LLM provers enforcing rigid proof strategies, Aria grants the agent autonomy in proof generation while ensuring soundness, completeness, and termination via harness constraints. Evaluations demonstrate: (1) 100% coverage on Iris separation logic (4,257 core lemmas + 217 Rust stdlib lemmas), (2) 318/318 proofs on reglang (vs. ~12.5% for prior work), and (3) generalization to Lean 4 (72 unported Iris lemmas). Results show state-of-the-art LLMs (Claude Opus 4.7) can automate verified software development.
formal verificationllm code agentsseparation logicautomated theorem provingverification harness
Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
The authors propose integrating unsupervised dictionary learning as a post hoc interpretability module for end-to-end autonomous driving models, enabling decomposition of driving behavior into semantically meaningful concepts. Their framework extracts and interprets these concepts, connects them to model outputs, and demonstrates causal influence on trajectory predictions. Targeted concept-level interventions correct erroneous behavior, improving driving performance by 12% on nuScenes benchmark, showcasing interpretability's role in reducing model opacity and enabling performance gains.
interpretabilityend-to-end learningdictionary learningautonomous drivingtrajectory prediction
Estimating Uncertainty from Reasoning: A Large-Scale Study of Multi- and Crosslingual MCQA Performance in LLMs
This study conducts the first large-scale evaluation of uncertainty estimation (UE) methods in multilingual settings, comparing nine open- and closed-box approaches across 22 languages using two human-curated QA datasets. The authors analyze UE performance across model sizes and architectures while eliciting long-form reasoning, avoiding LLM-as-a-judge and embedding-based scoring to reduce evaluation noise. Key findings include: English reasoning prompts improve UE performance in low-resource languages, demonstrating comprehension remains intact while generation reliability is the bottleneck; English reasoning closes the UE gap between low- and high-resource languages; UE method selection depends on model scale, with probability-based methods excelling at smaller scales and self-verbalized uncertainty at larger scales. The study also provides guidance on threshold selection for multilingual selective prediction.
uncertainty estimationmultilingualselective predictionreasoning promptslow-resource languages
DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail
DT-Guard introduces a reasoning-active training, reasoning-free inference paradigm for LLM safety guardrails, combining robust judgment with low-latency deployment. The method formulates safety as a progressive decision process (Intent → Category → Safety) and employs Rollout-Guided Progressive Hard-Case Optimization (RG-PHO) to improve hard-case robustness via multi-rollout consistency analysis. Experiments on prompt-side and response-side benchmarks show DT-Guard achieves average F1 scores of 0.886 and 0.870 respectively, outperforming 8B baselines with a 4B model while maintaining inference efficiency.
safety guardrailreasoning-active trainingrollout-guided optimizationintent-driven reasoninglow-latency moderation
Token-Based Dual-view Fusion and Adaptation of Large Vision Models for Breast Cancer Classification
The paper proposes a token-based dual-view fusion framework for breast cancer classification from mammography, addressing limitations of existing multi-view approaches that entangle view-specific and shared representations. The method introduces dedicated fusion tokens for bidirectional cross-view communication via cross-attention at multiple transformer depths, enabling progressive interaction while preserving view-specific structure within a frozen vision transformer backbone. Evaluated on VinDr-Mammo and CMMD datasets, the framework achieves 50.40% F1-score and 0.8090 AUC, outperforming linear probing and conventional fusion baselines by 0.10 AUC in binary classification.
token-based fusioncross-attentionmulti-view learningvision transformerbreast cancer classification
UI2App: Benchmarking Visual Interaction Inference in Executable Web Application Generation
We introduce UI2App, the first benchmark for evaluating interaction inference in executable web application generation from UI screenshots. The benchmark comprises 327 screenshots grouped into 45 state-coherent sets, with an end-to-end pipeline assessing executability, navigation reachability, visual fidelity, and interaction inference. The interaction inference score (IIS) evaluates functional correctness and state-management complexity, accepting any valid implementation. Experiments on six vision-language models reveal a significant gap between visual reconstruction and interaction realization, with the visual-fidelity leader scoring only 7.5 on IIS and high-complexity interactions remaining a pervasive bottleneck.
interaction inferenceexecutable web applicationsvisual fidelitystate-management complexityvision-language models
Designing Maintainable Hybrid Generative Systems: A Quantum-Inspired Approach to Automated Music Harmony Generation
The paper introduces a maintainable hybrid generative architecture for automated music harmony generation from melody, combining quantum-inspired candidate exploration with rule-based optimization. The system explores overlapping melodic contexts and applies explicit optimization to balance generative flexibility and structural control. Evaluation using reproducible metrics demonstrates that the approach preserves tonal structure and cadential behavior while enabling multiple valid harmonic realizations. The optimization layer enhances structural coherence, stability, and predictability without requiring a training corpus. The study highlights the systematic design and evaluation of transparent, controllable hybrid generative systems within Information Systems Development.
hybrid generative systemsquantum-inspired explorationrule-based optimizationmusic harmony generationstructural coherence
Task Decomposition-Guided Reranking for Adaptive Agent Skill Retrieval
The paper introduces SkillReranker, an inference-time reranking framework for adaptive skill selection in agent systems. The method decomposes tasks and skills into subtasks and execution states, constructs a directed acyclic execution graph to model task-skill correspondence, and uses a cross-encoder to score and select optimal skills. Evaluations on ALFWorld and ScienceWorld with three backbone LLMs demonstrate improved task performance, reduced interaction steps, and lower token consumption compared to baselines.
skill selectiontask decompositionrerankingexecution graphcross-encoder
From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
The paper proposes a theoretical framework for embedding application-layer cognitive protocols into native AI meta-architectures through three mechanisms: (1) Structural Tension, an endogenous loss function based on information-manifold conflicts; (2) Offline Recurrent Loop for self-processing without external input; and (3) Inference-time Plasticity enabling context manifold reconfiguration without weight modification. The framework enables heterogeneous model evolution while maintaining governance invariants like auditability and topological continuity, extending Structural Intelligence protocols to position governance as the primary architectural criterion.
structural tensionoffline recurrent loopinference-time plasticitycontext manifoldstructural intelligence
VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
We introduce VendorBench-100, a unified benchmark for evaluating deepfake image detection across commercial APIs, zero-shot vision-language models, and open-source detectors. The benchmark employs a 100-image adversarial corpus with eight edge-case families, using Matthews correlation coefficient (MCC) as primary ranking metric and ROC-AUC for threshold-independent assessment. Evaluation of 36 models reveals commercial APIs achieve strongest median performance, followed by vision LLMs and open-source detectors, though individual open-source models compete with top vision LLMs. Key finding is the consistent divergence between ROC-AUC and MCC, indicating strong score discrimination does not guarantee reliable default-threshold decisions. The benchmark framework and results are released for reproducibility.
deepfake detectionmatthews correlation coefficientvision-language modelsroc-aucadversarial corpus
Demonstrating TOFFEE: A Learned System for Synthesizing Data Agent Trajectories at Scale
TOFFEE introduces a system for synthesizing high-quality data agent trajectories to address generalization challenges in heterogeneous enterprise environments. The method employs Monte Carlo Tree Search (MCTS) with adaptive model selection and cross-task prefix reuse to generate scalable trajectory data for complex analytical tasks. TOFFEE supports downstream applications including supervised finetuning (SFT) for domain adaptation and in-context learning (ICL) demonstrations for general-purpose LLMs. The system framework includes task pool construction, trajectory explorer, and learned cost model, demonstrated through end-to-end scenarios of trajectory synthesis and demonstration-augmented reasoning.
monte carlo tree searchsupervised finetuningin-context learningdata agent trajectoriesadaptive model selection
Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows
The paper introduces Spider 2.0-AIFunc, a benchmark for evaluating text-to-SQL models on AI-native SQL functions (classification, sentiment analysis, etc.) in real-world databases. The benchmark comprises 465 verified instances across 125 Snowflake databases, constructed via an agent-based pipeline that rewrites conventional SQL tasks into AI-native form while refining natural language instructions. Evaluation of ten state-of-the-art models shows proprietary models achieve 67-70% execution accuracy, while the best open-source model reaches 58.1%, with errors primarily in predicate specification, schema grounding, and AI function parameterization. Agent frameworks for traditional text-to-SQL underperform in this setting.
text-to-sqlai-native sqlexecution accuracyschema groundingagent-based pipeline
Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents
The paper introduces Information Gain-based Rollout Policy Optimization (IGRPO), a reinforcement learning framework for optimizing multi-turn LLM agents by adaptively allocating rollout budget based on intermediate-state informativeness. IGRPO employs tree-structured rollouts where expansion prioritizes high-information-gain branches, inducing a teacher distribution over trajectories that guides policy optimization. Evaluated on seven search-augmented QA benchmarks, IGRPO outperforms baselines under equivalent budget constraints, demonstrating improved efficiency in long-horizon search tasks.
reinforcement learningrollout policy optimizationinformation gaintree-structured searchmulti-turn agents
A toy framework for single and multi-agent human-AI curiosity ecosystems
The paper proposes a conceptual framework for modeling curiosity as an ecosystem, addressing both single-agent and multi-agent scenarios. For single agents, it formalizes inquiry policies through dynamic weighting of uncertainty reduction, costs, delayed returns, and question-keeping value, with weights adapting through experience. The multi-agent extension introduces metrics for collective inquiry patterns including volume, topic diversity, frontier-directed exploration, redundancy, and knowledge reuse. This toy framework provides foundations for studying curiosity ecology and designing discovery-oriented multi-agent AI systems, complementing a neuroscience-focused companion paper under review.
inquiry policyuncertainty reductionknowledge landscapecuriosity ecologymulti-agent discovery
UBEP: Re-architecting Expert Parallelism Communication Library for Production Superpods
UBEP introduces a communication library optimized for Mixture-of-Experts (MoE) models on high-bandwidth superpods, addressing three bottlenecks: BSP-induced serialization, synchronization overhead, and load imbalance. The method rearchitects All-to-All primitives by leveraging unified global address spaces and high-bandwidth fabrics. Large-scale experiments demonstrate reductions of up to 52.4% in All-to-All latency and 11.1% in Time Per Output Token (TPOT) for MoE inference.
mixture-of-expertsall-to-allbulk synchronous parallelsuperpodtime per output token
TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
The TriA Pipeline introduces a large-scale automatic audio annotation system for generating labeled training data in specific audio classification scenarios where annotated data is scarce. The method processes raw audio from diverse environments into high-quality labeled datasets, demonstrated by constructing TriA (2130 hours, 431 classes) and its prior-knowledge-guided subset TriA$_{\mathrm{GK}}$. Experiments on domestic audio classification tasks show TriA$_{\mathrm{GK}}$ yields average relative gains of 3.97% accuracy and 3.35% Macro-F1 when combined with manual annotations.
audio classificationautomatic annotationprior-knowledge-guidedmacro-f1domestic environments
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
The study systematically investigates reward function design for reinforcement learning (RL)-based generation of BPMN process models by large language models (LLMs), addressing multi-dimensional quality optimization. Using Group Sequence Policy Optimization, researchers trained Llama~3.1 8B and Qwen~2.5 14B under 48 configurations with rewards derived from 38 automated metrics spanning syntactic, pragmatic, and semantic quality. Key findings show RL improves pragmatic and syntactic quality while maintaining semantic fidelity, equal reward weighting outperforms targeted weighting, and design choices interact non-trivially with model architecture, with effects comparable to applying RL itself.
reinforcement learningprocess model generationreward function designlarge language modelsautomated evaluation
When do prophets profit in prediction markets?
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X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
We introduce X-FEMR, the first token-level explainability approach for Electronic Health Records Foundation Models (FEMRs), addressing interpretability concerns in clinical AI. The method trains a Transformer-based surrogate model on FEMR input-output pairs across two prediction tasks, preserving temporal dynamics while approximating FEMR behavior. A novel clinical alignment metric evaluates correspondence between surrogate model key tokens and clinically validated features. Results demonstrate close approximation of FEMR predictions by the surrogate and strong alignment between token-level explanations and clinical knowledge, providing an interpretable framework for trustworthy clinical AI.
token-level explainabilityelectronic health recordsfoundation modelstransformer-based surrogateclinical alignment
LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
LongCrafter introduces a structured framework for synthesizing long-context supervised fine-tuning (SFT) data to enhance large language models' (LLMs) long-context understanding. The method employs a hierarchical task taxonomy (32 task types) and an evidence-grounded pipeline, constructing task-aligned contexts, decomposing them into evidence graphs, and generating instruction--response pairs grounded in evidence spans. Evaluated on LongBench, LongBench~v2, and LooGLE, models fine-tuned with LongCrafter data outperform SFT baselines and official post-trained models (Qwen2.5-7B, LLaMA-3.1-8B), particularly on high-difficulty tasks, while mitigating the 'lost in the middle' problem.
long-context understandingsupervised fine-tuningevidence graphstask taxonomyinstruction synthesis
LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability
This paper formalizes deliberative collaboration as a cooperative joint decision problem with partial and asymmetric observations, introducing a scalable benchmark across multiple task settings and domains. A reference scaffold and evaluation protocol are instantiated for deliberative agents, systematically evaluating representative LLMs. Results show that complex deliberative collaboration tasks remain challenging for state-of-the-art language models, with failures in both information alignment and complex reasoning, despite external mathematical tools. Diagnostic analysis indicates that deliberation can enable reflection and error correction, sometimes outperforming centralized baselines, providing insights into LLM-based multi-agent systems.
deliberative collaborationpartial observabilityjoint decision-makingllm agentsmulti-agent systems
Property-Driven Synthetic Data Engineering for Data-Scarce Software Systems: Reflections from the Breast Cancer Domain
The paper introduces property-driven synthetic data engineering as a framework for addressing data scarcity in sensitive domains like breast cancer treatment. Through collaboration with oncologists and experiments on intraoperative radiotherapy (IORT) datasets, the authors identify key challenges in requirements elicitation, validation, privacy preservation, and pipeline evolution. They advocate for automated methods to formalize and validate synthetic data properties in data-scarce software systems.
synthetic data generationrequirements elicitationprivacy preservationdata-scarce systemspipeline evolution
Self-Supervised Implicit CEST Reconstruction via Physics-Informed Lorentz Encoding
The paper proposes Lorentz Encoding (LE), a physics-informed self-supervised framework for reconstructing high-resolution Z-spectra from sparse CEST MRI data. LE formulates reconstruction as implicit continuous coordinate learning, projecting sparse coordinates into a physically constrained space via learnable Lorentzian basis functions. This enforces spectral consistency with physical models while reducing artifacts. On in vivo human brain data with 39-point sampling, LE achieves 57.58 dB PSNR and 0.9994 SSIM, outperforming state-of-the-art methods. The learned encodings yield geometrically ordered latent trajectories, enabling accurate metabolite mapping (APT, NOE, MT).
chemical exchange saturation transferimplicit neural representationslorentz encodingself-supervised reconstructionphysics-informed learning
Evaluating Fine-Tuning and Metrics for Neural Decompilation of Dart AOT Binaries
The study evaluates fine-tuning effectiveness and metric validity for neural decompilation of Dart AOT binaries, introducing the HumanEval-Dart benchmark and a Dart-adapted CodeBLEU. Six fine-tuned variants of three base architectures (4B-8B parameters) are assessed using CodeBLEU, compile@k, and pass@k on 154 tasks. Key findings include: no significant pass@k improvement from fine-tuning (with regression in Qwen3-8B), cross-lingual Swift interference significant at 4B but not 8B, and metric divergence between CodeBLEU/compile@k versus pass@k. Assembly sequence length strongly predicts task difficulty, with a capability cliff at 200 instructions.
neural decompilationdart aotfine-tuningpass@kcodebleu
Static Metrics Are Insufficient: Predicting Java Method Energy Usage with Execution Time
This study demonstrates that static source code metrics alone are insufficient for predicting Java method energy consumption, achieving near-zero R2 values. By profiling 2,786 Java methods and extracting 33 static features alongside execution time measurements, the authors train eleven regression models to show that incorporating execution time as a dynamic input significantly improves prediction accuracy (R2 up to 0.46). Key predictors include execution time, internal method calls, and cyclomatic complexity, highlighting the need for lightweight dynamic metrics in energy-aware software design.
energy predictionjava methodsstatic metricsexecution timecyclomatic complexity
x-Prediction Is All You Need:Training-Free Accelerated Generation via Endpoint Decodability
The paper introduces Truncated Jump Sampling (TJS), a training-free method to accelerate diffusion and flow matching models by leveraging endpoint decodability. TJS exploits the affine probability paths' inherent property that intermediate states and velocities yield clean-sample estimates via the minimum-MSE decoder $\mathbb{E}[x_0\mid x_t]$, enabling early ODE termination at time $t^*$. Evaluated on SDXL, SD3.5M, Z-Image-Turbo, and class-conditional benchmarks, TJS reduces neural function evaluations (NFEs) by 20–70% while maintaining output quality. The analysis demonstrates that endpoint prediction achieves acceleration without trajectory straightening or architectural modifications.
diffusion modelsflow matchingendpoint decodabilityminimum-mse estimatorode sampling
LLM-Guided Measurement Credibility Correction for Trustworthy Industrial Process Inference
The article proposes LLM-Guided Measurement Credibility Correction (MCC), a method that enhances industrial process inference by converting process document semantics into actionable numerical references for input measurement correction. MCC independently qualifies external measurements to resolve local conflicts before prediction, without relying on numerical correlation or explicit process equations. Evaluated on complex industrial tasks, +MCC reduces average relative MAE by 30.7% (real-test) and 80.3% (controlled-corruption), adding only 0.5–2.0k parameters with 0.089 ms/step latency.
measurement credibility correctionsoft sensingprocess inferencellm-guided semanticsindustrial forecasting
RoME: Robust Mixture of Low-Rank Experts against Multiple Adversarial Perturbations
The paper proposes Robust Mixture of Low-Rank Experts (RoME), a method to improve multi-perturbation adversarial training by addressing robustness trade-offs between different threat types. RoME employs low-rank additive updates to a shared backbone for threat-common features, while experts capture threat-specific information, and introduces dual-scale gating with local/global features plus threat-guided gating diversification to prevent threat-agnostic routing. Experiments show RoME outperforms state-of-the-art MAT methods in union robustness (+3.2% avg) and natural accuracy (+1.5%), while improving generalization to unseen threats.
multi-perturbation adversarial trainingmixture of expertslow-rank updatesdual-scale gatingthreat-specific routing
EcoVision: AI-Powered Drone Imaging for Salt Marsh Vegetation Monitoring and Dominance Mapping
The study presents EcoVision, an AI-powered drone imaging system for salt marsh vegetation monitoring, achieving pixel-level segmentation (mean IoU=0.56, accuracy=0.96) and fine-grained species classification (F1=0.99) of Spartina maritima and Puccinellia maritima. The method combines transformer-based semantic segmentation, ConvNeXt classification, and grid-based dominance scoring at 2x2m resolution, trained on UAV and public biodiversity imagery. Dominance estimates showed <8% mean absolute difference from field surveys, preserving spatial structure. The framework demonstrates scalable ecological monitoring through AI-driven pixel-to-metric translation.
semantic segmentationconvnextuav imageryspecies classificationdominance mapping
Agents That Teach: Towards Designing Incidental Learning Back into AI-Assisted Software Development
The paper proposes six design principles for integrating incidental learning into AI-assisted software development to mitigate Knowledge Debt, where developers lose expertise by over-relying on autonomous coding agents. It introduces SHIELD, a multi-agent system that leverages the agent's reasoning to surface contextual learning opportunities without disrupting workflow. The approach aims to balance productivity and skill retention in learning-aware development environments.
knowledge debtai coding agentsincidental learningmulti-agent systemsoftware development
PVCap: Towards Accurate 3D Dense Captioning via PseudoCap and VoxelCapNet
The paper introduces PVCap, a novel framework for 3D dense captioning that addresses two key limitations in prior work: lack of diverse spatial layouts in data augmentation and simplistic network architectures. PVCap comprises PseudoCap, which generates pseudo frames with varied spatial layouts via instance-level random mixing and pseudo-labeling through a teacher-student framework, and VoxelCapNet, a robust voxel-based caption network. Evaluated on ScanRefer and Nr3D benchmarks, PVCap achieves state-of-the-art performance with 11.41% and 13.99% improvements in CIDEr@0.5IoU, respectively.
3d dense captioningpseudocapvoxelcapnetspatial layoutsteacher-student framework
From Blueprint to Reality: Modeling and Applying Putnam's Social Capital Theory with LLM-based Multi-agent Simulations
SocaSim introduces an LLM-based multi-agent framework to operationalize Putnam's Social Capital Theory, integrating social network evolution, trust dynamics, and norm propagation. The method employs repeated collective-action experiments within a simulated environment, enabling micro-level causal analysis through round-by-round simulations and counterfactual interventions. Results demonstrate macro-level pattern replication aligned with Putnam's theory and human-agent congruence at group level, validated via smart elderly care case studies while providing process-level interpretability.
social capital theoryllm-based simulationmulti-agent systemstrust dynamicsnorm propagation
Prompt Coach: An Empirical Evaluation of an Agentic Tutor for Learning Prompt Engineering in Software Development
The paper introduces Prompt Coach (PC), an agentic tutor for teaching prompt engineering to software developers through Socratic guidance integrated into the IDE. PC evaluates prompt quality across multiple dimensions and provides contextual feedback based on the developer's codebase and LLM behavior. In a study with 15 professional developers, participants showed statistically significant improvements in prompt quality after a 60-minute session, with notable gains in commonly overlooked dimensions, while reporting high trust and adoption readiness.
prompt engineeringagentic tutorsocratic guidancecode-generationide integration
Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency
The study introduces the Efficiency heuristic, a reward-density-based approach for dynamic multi-vehicle routing that combines elements of the Vehicle Routing Problem and Orienteering Problem. The method maximizes cumulative reward while continuously replanning as new tasks arrive, evaluated in autonomous drone allocation and urban taxi dispatch scenarios. Compared to four classical heuristics and three metaheuristics (Adaptive Large Neighbourhood Search, Genetic Algorithm, Simulated Annealing), the Efficiency heuristic matches solution quality with 100-1000× faster planning time, achieving Pareto dominance on the reward-compute frontier.
vehicle routing problemorienteering problemdynamic task allocationmetaheuristicscomputational efficiency
PolyWorkBench: Benchmarking Multilingual Long-Horizon LLM Agents
The paper introduces PolyWorkBench, a benchmark for evaluating multilingual long-horizon LLM agents across 67 tasks in five domains (commerce, knowledge work, legal analysis, localization, manufacturing). It proposes a hybrid evaluation framework combining structural grading, executable verification, and LLM-based semantic assessment to measure functional correctness and linguistic consistency. Empirical results reveal significant performance degradation in multilingual workflows compared to monolingual settings, indicating compounding effects of language variation on reasoning and execution.
multilingual agentslong-horizon tasksworkflow benchmarkinghybrid evaluationlinguistic consistency
Information Limits and Attractor Dynamics in Economies of Frontier LLM Agents: A Pre-Registered Test
The study presents a pre-registered experiment testing information-theoretic and dynamical systems predictions in economies of frontier LLM agents (Claude Opus 4.8). Using a parimutuel-coupled multi-agent system with $138.76 API budget, it confirms that relative wealth growth equals relative information (gap law holds within 46 millinats) and reveals coalition value's submodularity under conditional independence. A designed XOR control induces supermodularity (0.62 ≥ ln2/2 nats). Contrary to mean-field predictions, population misalignment showed step-function responses to incentives rather than smooth dispersion, with bistability near dominance boundaries. Full protocol and cached outputs are released for reproducibility.
parimutuel couplingmillinatssubmodularitymean-field modelbistability
AgoraSim: A Hybrid Agent-Based Modeling Framework
AgoraSim introduces a hybrid agent-based modeling framework for analyzing social reactions in scenario-oriented simulations. The framework resolves textual or multimodal inputs into editable agent-based model configurations, supporting ratio-controlled populations that integrate LLM, vision-language, custom-endpoint, random, and classical agents. It enables scenario comparison against matched classical reference dynamics through a shared structured decision object, ensuring common action spaces, interaction protocols, metrics, and audit records. AgoraSim is accessible via a local UI, Python SDK/CLI, and REST API, facilitating scenario trajectory inspection, modeling assumption comparison, and identification of cases requiring empirical validation.
agent-based modelingllm-agentstructured decision objectmultimodal artifactsscenario-oriented analysis
PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages
PluraMath extends mathematical reasoning evaluation to 18 underrepresented languages across 6 language families, addressing the high-resource bias in existing benchmarks like PolyMath. The dataset was constructed via human-curated translations validated by native speakers, then used to evaluate 27 reasoning LLMs across four model scales. Results reveal persistent performance gaps between high-resource and underrepresented languages, correlating with instruction-following ability, with full open-sourcing of dataset and pipeline.
mathematical reasoningmultilingual evaluationlow-resource languagesllm benchmarkinginstruction-following
Auto-DSM Under the Lens: A Black-Box Evaluation Framework for LLM-Based DSM Generation
The paper contributes a black-box evaluation framework for assessing LLM-generated Design Structure Matrices (DSMs), addressing the opacity of current Auto-DSM pipelines. The methodology benchmarks generated DSMs (GEN-DSMs) against ground-truth matrices (GT-DSMs) using structural metrics (Completeness, Correctness, Coupling Density), classification metrics (Selective Accuracy, Abstention Coverage), and stability measures (Entropy, Fleiss' κ), synthesized into a Composite Quality Score (Q). Experiments on fictive and real-world datasets reveal LLMs produce structurally plausible DSMs with high reproducibility under well-structured inputs but exhibit sensitivity to ambiguity and prompt formulation. The framework enables auditing Auto-DSM pipelines and supports LLM integration into model-based systems engineering.
design structure matriceslarge language modelsmodel-based systems engineeringblack-box evaluationcomposite quality score
Propose and Attend: Training-free MLLM Grounding Confidence via Multi-Token Localized Attention
The paper introduces Multi-Token Localized Attention (MTLA), a training-free method to improve grounding confidence in multimodal large language models (MLLMs) by measuring how strongly prediction tokens attend to their claimed regions. Unlike prior attention-based detectors that aggregate attention globally, MTLA sums attention only within the predicted region and across all prediction tokens, enhancing localization accuracy. Evaluated across multiple MLLM families and three modalities (images, video, audio), MTLA boosts hallucination AUROC by +7 to +38 and nearly doubles zero-shot COCO detection AP from 20.4 to 37.0, closing the gap to supervised detectors without task-specific training.
multimodal large language modelstraining-free groundingmulti-token localized attentionzero-shot detectionattention aggregation
MCP-Enabled Agentic AI for Autonomous IPoDWDM Network Lifecycle Automation
The paper introduces an MCP-enabled agentic AI architecture for autonomous IPoDWDM network control, achieving vendor-agnostic multi-layer lifecycle automation. The method integrates GNPy for physical-layer validation and telemetry-based closed-loop control, implemented on a live testbed. Results demonstrate end-to-end autonomous operation across network layers.
agentic aiipodwdmgnpytelemetryclosed-loop control
Faithful or Findable? Evaluating LLM-Generated Metadata for RDF Dataset Search
The study evaluates six metadata-generation approaches for RDF dataset search, assessing retrieval effectiveness and faithfulness. Methods range from simple rewriting to profile-grounded and agentic graph-based generation. Unconstrained metadata rewriting achieves the highest retrieval gains but exhibits the lowest faithfulness, indicating semantic expansion drives improvements without grounding. Profile-grounded rewriting offers the most balanced trade-off, improving faithfulness while maintaining retrieval effectiveness. Findings highlight synthetic metadata as a system-level IR problem requiring joint evaluation of effectiveness, provenance, and trust.
metadata-generationrdf datasetretrieval effectivenessfaithfulnesssemantic expansion
InfluMatch: Frontier-Quality KOL Search at 4B-Model Cost
InfluMatch introduces a cost-efficient three-stage cascade (retrieval → rerank → reason) for matching Thai influencers to marketing criteria using small open-weight models. The system employs a 4B dense retriever, a 4B pointwise reranker (SimPO-tuned), and a 4B untuned reasoner, achieving 94.1% P@5 on human-labeled queries while reducing output tokens by 35× versus frontier LLMs. Key findings include the superiority of pairwise fine-tuning for reranking (78.0 EM) and the counterintuitive degradation from fine-tuning the reasoner, attributed to task design. The system serves 50-KOL queries in ~20s on one A100.
kol searchcascade architecturesimpo-tuningpointwise rerankerthai nlp
Decoupled Single-Mask Annotation Noise Detection via Cross-Sectional Patch Self-Consistency
The paper introduces a decoupled framework for detecting annotation noise in single-mask vascular CT datasets by leveraging cross-sectional patch self-consistency. The method samples patches orthogonally along vessel centrelines, retrieves intensity-similar neighbors via vector search, and computes patch-level noise scores from statistical mask disagreement, producing interpretable quality maps. Experiments on coronary CT data show 5.1× higher error rates in transverse/oblique vessels versus axis-aligned structures, with correlations to cross-sectional area and intensity, validating the approach for improving training robustness and revealing systematic biases.
annotation noise detectioncross-sectional patchself-consistencyvascular ctquality maps
Agentic AI for IPoDWDM Network Lifecycle Automation: An MCP-Enabled Architecture
The authors propose a distributed, vendor-agnostic multi-MCP architecture for autonomous control of IPoDWDM networks. The framework integrates SDN-based automation with GNPy modeling and optical telemetry to enable end-to-end service lifecycle automation and closed-loop cross-layer control. Experimental validation was conducted on an IPoDWDM testbed, demonstrating the system's capability for multi-vendor, multi-layer network management.
ipodwdmsdngnpytelemetrymulti-mcp
Integrating knowledge graphs and multilingual scholarly corpora for domain-adaptive LLMs in SSH
The paper presents a domain-adaptation framework for Large Language Models (LLMs) in Social Sciences and Humanities (SSH), integrating knowledge graphs and multilingual scholarly corpora within the LLMs4EU project. Methodologically, it combines quantitative benchmarking (retrieval, summarization, hallucination detection) with qualitative expert evaluation through Digital Humanities panels, while addressing legal and ethical compliance. Results demonstrate an infrastructure-supported approach to enhance SSH research tasks like question answering and literature review while maintaining epistemic reliability.
domain-adaptive llmsknowledge graphsmultilingual corporahallucination detectionepistemic reliability
NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation
NegROI introduces a transformer-based interactive 3D segmentation framework addressing coarse voxel resolution and hard false positives in point cloud segmentation. The method combines click-centric multi-resolution refinement with scene-conditioned negative prompts, refining local regions of interest on finer grids and fusing logits back into coarse masks. It employs uncertainty-driven selective refinement for efficiency and models hard background patterns via cross-attention over scene tokens, stabilized by a diversity regularizer. Boundary-aware hard negative mining supervises negative-prompt attention. Experiments on ScanNet, S3DIS, and KITTI datasets show improved click efficiency, reduced false positives, and enhanced cross-dataset robustness compared to state-of-the-art baselines.
interactive 3d segmentationnegative promptsmulti-resolution refinementcross-attentionhard negative mining
Signed-Graph Recommendation as Structural Consistency Maximization
The paper proposes SSC-Loop, a unified framework for signed social recommendation that maximizes structural consistency across three layers: structural, propagation, and semantic. The method integrates ESA-DA for structural consistency, a P/N/O propagation mechanism for propagation consistency, and contrastive learning for semantic consistency. Evaluations on Epinions show SSC-Loop's strong performance in signed social rating prediction, with additional results on Slashdot demonstrating its effectiveness in exploiting signed social structures.
signed social recommendationstructural consistencycontrastive learningpropagation mechanismgraph-based recommendation
SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation
SearchEyes introduces a unified framework for training multimodal search agents via a simulated search world built on a typed knowledge graph, addressing structural disconnects in existing pipelines. The method employs Perception-Knowledge Chains (PKC) to sample multi-hop paths over Wikidata5M's visual-knowledge intersection, retaining hop-level metadata for self-contained search worlds and step-level rewards. Hop-Anchored Policy Optimization (HaPO) reuses these anchors for step-level credit assignment without a separate reward model. Experiments on six multimodal benchmarks demonstrate state-of-the-art performance, with SearchEyes-27B outperforming the strongest open-source baseline by an average of 6.2 points.
multimodal searchknowledge graphperception-knowledge chainshop-anchored policy optimizationwikidata5m
CMDR: Contextual Multimodal Document Retrieval
The authors introduce CMDR and CMDR-Bench, a novel multimodal document retrieval task and benchmark emphasizing document context, addressing limitations in existing benchmarks that focus on lexical or semantic matching. They propose CMDR-Embed, a contextual multimodal embedding framework that jointly encodes multiple pages to derive page-level embeddings from a shared contextual representation. Additionally, CMCL, a contextual multimodal contrastive learning objective, is introduced to balance contextual modeling with page-level discriminability. Experiments show CMDR-Embed significantly outperforms non-contextual embeddings, demonstrating the efficacy of context-aware multimodal embeddings in document retrieval.
multimodal document retrievalcontextual embeddingcontrastive learningpage-level embeddingsbenchmark
PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation
We introduce PCBWorld, an engine-grounded PCB routing environment built on KiCad EDA, enabling interactive routing with Design Rule Check feedback for RL and LLM agents. PCBWorld-Bench provides three dataset families in KiCad's native format (.kicad_pcb), including synthetic and 679 real open-source boards, evaluated via eight engine-checked metrics. Experiments show PCBWorld agents outperform grid-action RL policies and open-loop LLM baselines, with RL policies trained on synthetic boards achieving zero-shot transfer to real boards, nearing rule-based router performance. This establishes PCBWorld as a foundation for advancing RL and LLM routing capabilities.
pcb routingkicad edadesign rule checkreinforcement learninglarge language models
PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails
The paper introduces PolicyShiftBench, a benchmark with 2,000 policy-discriminative instances across 265 images to evaluate policy-adaptive image guardrails. It proposes PolicyShiftGuard, a 7B parameter model trained via Randomized Policy SFT (RP-SFT) and Boundary-Pair Policy Adaptation (BP-Adapt), which uses label supervision and pairwise comparison loss for policy-sensitive decisions. The model achieves state-of-the-art performance (76.9 Avg. F1, 72.1 Avg. PSS) on PolicyShiftBench and generalizes well to UnSafeBench and SafeEditBench, demonstrating improved latency-performance trade-offs.
policy-adaptive guardrailsboundary-pair adaptationrandomized policy sftimage safety policiespolicy-sensitive evaluation
K-ABENA: K-Adaptive Backpropagation with Error-based N-exclusion Algorithm : (Compensated Loss-Based Sample Exclusion with Unbiased Gradient Estimation)
The paper introduces K-ABENA v3, a selective gradient computation framework that reduces training costs by excluding low-loss samples from backpropagation while maintaining unbiased gradient estimation. The method combines defensive-mixture sampling with Horvitz-Thompson reweighting, achieving O(1/sqrt(T)) convergence in non-convex SGD with quantified residual bias. Empirical results show 28-54% compute savings on real datasets (Breast Cancer, Digits, Wine, Diabetes) while matching full-batch SGD performance (p ≥ 0.5). The analysis proves uncompensated loss-based selection methods (e.g., OHEM, SBP) fail to converge when selection bias is non-zero, with test AUC dropping to 0.53-0.62 under class imbalance versus 0.9991 for K-ABENA v3.
gradient estimationselective backpropagationhorvitz-thompsonunbiased estimatortraining efficiency
From Textural Counterpoint to Feature Encoding: A Multi-Dimensional Machine Representation Study of Haydn's "The Lark" Integrating Electroacoustic Analysis
The paper proposes a novel interdisciplinary methodology for machine representation of polyphonic music, addressing role perception gaps in deep music generation models. Through analysis of Haydn's 'The Lark' Quartet, it combines classical morphology analysis, electroacoustic measurement (spectrum/dynamic features via DAW tools), and machine representation reconstruction using Event-based Timestamps and Role-Aware Encoding. This approach replaces mechanical quantization with micro-timing duration tracking and transforms acoustic features into aesthetic heuristics, establishing a theoretical foundation for human-computer collaborative systems with social attributes.
polyphonic interactionrole-aware encodingevent-based timestampselectroacoustic analysiscounterpoint morphology
Uncovering Latent Depression Severity for Binary Depression Detection via Advantage-weighting Ranking
The paper introduces a Binary Advantage-weighting Ranking Loss for depression detection from audio-visual data, addressing feature distribution overlap through two mechanisms: Advantage-weighted Separation (mining hard pairs via prediction difference matrices) and Advantage-weighted Compactness (reducing intra-class variance). The framework employs a temporal encoder and mutual transformer for cross-modal fusion. Evaluations on D-vlog and LMVD show state-of-the-art performance by reconstructing latent ordinal structures through prioritized hard pairs.
advantage-weighting ranking losscross-modal fusiontemporal encodermutual transformerintra-class variance
Few-Medoids: An Embarrassingly Simple Coreset Selection Method for Few-Shot Knowledge Distillation
The paper proposes Few-Medoids, a simple coreset selection method for few-shot knowledge distillation that selects samples closest to class centroids. The approach outperforms random selection and existing strategies like herding and k-center Greedy across four image classification datasets and three teacher-student architectures (convolutional and transformer networks). Empirical results demonstrate consistent improvements, suggesting Few-Medoids as a viable baseline replacement for future coreset selection research.
coreset selectionfew-shot learningknowledge distillationimage classificationcentroid-based sampling
i-EXAM: Instructable and Explainable Attack Connectivity Graph Modeler
i-EXAM introduces an instructable and explainable attack connectivity graph modeler for network security analysis. The tool employs planning compilation with soundness and completeness guarantees to identify attack paths, compute security metrics, and generate diverse hardening strategies. It integrates Large Language Models to provide natural language explanations of generated strategies, aiding system administrators in network hardening decisions.
planning compilationattack pathssecurity metricsnetwork hardeninglarge language models
Harrison.Rad 1.5 Technical Report: A radiology foundation model that can draft reports from images, priors and clinical context
Harrison.Rad 1.5 (HR1.5) introduces a radiology-specific multimodal large language model capable of drafting reports from images, priors, and clinical context. The model employs a three-stage pipeline: domain adaptation of a base language model, contrastive vision-encoder training with curriculum-based hard negatives on ~6 million image-report instances, and visual-question-answering fine-tuning. Evaluated using the Findings-Diagnosis scoring framework and benchmarked on RadBench, ReXGradient, and internal datasets, HR1.5 meets the simulated FRCR passing standard and achieves the highest accuracy on closed-format clinical questions across anatomical regions. The study also examines explainability through Grad-CAM heatmaps, attention analysis, and confidence estimation to support clinical use.
multimodal large language modelcontrastive vision-encodervisual-question-answeringfindings-diagnosis scoringgrad-cam heatmaps
Think Before You Grid-Search: Floor-First Triage for LLM Serving
The paper introduces Floor-First, a residual-driven triage workflow for optimizing LLM serving that replaces exhaustive grid-search with analytical estimation. The method models each decode step as a five-dimensional resource vector (HBM bytes, FLOPs, network bytes, network messages, KV capacity), computing optimistic (max) and pessimistic (sum) floors to assess overlap quality before profiling. Deployment alternatives are compared via wall ordering, revealing which resource binds first under load. Applied to a DeepSeek-V3.2-style 671B MoE/MLA model on 16 H20 GPUs, the analysis shows TP16 decoding is KV-capacity-limited (~70 concurrent 8K requests), while EP16+DP-attention improves capacity (~644 requests) despite higher latency (2.4x slower than TP).
llm servingresource vectorkv capacitywall orderingsparse attention
Differentially Private Natural Gradient Descent
DP-NGD introduces a differentially private natural gradient descent framework that addresses optimization inefficiency in DP training. The method decouples curvature estimation from private data, reconciles isotropic DP constraints with anisotropic second-order optimization via a whitened-space mechanism, and dynamically clamps curvature to stabilize training. Experiments on standard benchmarks show DP-NGD achieves state-of-the-art accuracy, surpassing first-order baselines by up to 10× convergence speedup under identical privacy budgets.
differentially privatenatural gradient descentcurvature estimationanisotropic optimizationwhitened-space mechanism
Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns
The paper proposes TENSOR, an unsupervised anomaly detection method for identifying Information Operations (IO) users by analyzing temporal behavioral and language patterns. The approach combines a Temporal Point Process (TPP) for modeling abnormal posting activities with a novel evidence function that quantifies Large Language Model (LLM) responses from user timelines. Evaluated on five real-world IO datasets, TENSOR outperforms existing baselines in detecting coordinated IO campaigns without requiring labeled training data.
anomaly detectiontemporal point processinformation operationslarge language modelunsupervised learning
AbICL: In-Context Learning for Antigen-Specific Antibody Affinity Ranking
AbICL introduces an in-context learning (ICL) framework for antigen-specific antibody affinity ranking, addressing limitations of independent affinity comparisons by leveraging contextual information from labeled demonstrations. The method combines a pretrained structural encoder with a context ranking head, trained via episodic meta-learning to enable test-time adaptation without gradient updates. On the AbRank benchmark, AbICL consistently outperforms existing baselines across data splits, particularly under distribution shift and fine-grained affinity discrimination. Results demonstrate that contextual demonstrations enhance performance when aligned with the target task, highlighting ICL's potential for antigen-specific ranking where global ranking functions are insufficient.
in-context learningantibody affinity rankingepisodic meta-learningstructural encoderdistribution shift
StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
StateFuse introduces a conflict-aware replicated memory contract for multi-agent systems, preserving observable contradictions through immutable history, explicit conflict objects, and deterministic predicate contracts. Built on standard OpSet/CRDT merge semantics, it provides agent-facing correction handles (claim_id/claim_ref) and projection-time resolution without state rewriting. Evaluation on MemoryAgentBench (282 questions) shows parity in answer accuracy with baselines, but superior contradiction visibility and safer abstention/correction capabilities. Semantic correction handles prove crucial when exact prior identifiers are unavailable.
conflict-preserving memorycrdt mergedeterministic predicatecorrection handlesmulti-agent systems
Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
The study isolates the impact of safety alignment on LLM utility in software security workflows by comparing same-lineage models with intact (Aligned) versus ablated (Abliterated) refusal behaviors. Using Gemma and Qwen model families, it evaluates vulnerability detection, CWE attribution, localization, and patch validation under varying prompt framings. Results show Abliterated models outperform Aligned ones: Gemma achieves 67.8% usable patches versus 29.9%, while Qwen improves line-level F1 from 2.08% to 3.91%. The work advocates for joint measurement of response correctness and workflow actionability in security evaluations.
refusal ablationvulnerability detectionsame-lineage comparisonpatch validationsafety alignment
VisTCP: A Visualization Framework to Construct Knowledge-Graph-Based Representation for Traditional Chinese Painting
VisTCP introduces a visualization framework for constructing knowledge-graph-based representations of Traditional Chinese Paintings (TCPs), addressing semantic misunderstandings and object identification challenges. The method combines a TCP-oriented intelligent model with expert knowledge through a human-in-the-loop approach, involving a semantic taxonomy, expert-annotated data training, and joint embedding visualization for model uncertainty. Evaluations via case study, usage scenario, and expert interviews demonstrate effectiveness in supporting structured representation and semantic understanding of TCPs.
knowledge-graphhuman-in-the-loopsemantic taxonomyjoint embedding visualizationstructured representation
Tangent classes of matroids and wonderful compactifications
The paper introduces an integral tangent class $T_{M,\mathcal{G}}^{\mathbb{Z}}$ for loopless matroids $M$ and Feichtner--Yuzvinsky building sets $\mathcal{G}$ containing the top flat, constructed autonomously by the AI agent Danus. This class generalizes the tangent bundle of wonderful compactifications in the realizable case, recovers the Hilbert series of the Chow ring via Hirzebruch--Riemann--Roch, and satisfies Chern-alpha lower bounds. The results replicate prior work (arXiv:2606.22650) without human guidance, demonstrating AI's potential in mathematical research.
matroidwonderful compactificationtangent classchow ringai reasoning
Decision-Focused Scenario Generation and Selection for Efficient and Robust Grid Dispatch
A decision-focused generative framework is proposed for correlated scenario generation in distributionally robust optimization (DRO)-based power system dispatch, optimizing scenarios based on induced downstream operational costs rather than solely fitting historical uncertainty distributions. The framework supports mainstream generative models, including variational autoencoders, generative adversarial networks, and diffusion models, while capturing joint uncertainty distributions across buses, and incorporates a differentiable scenario selector for computational tractability. Empirical results demonstrate operational cost reductions of 0.80%-2.02% compared to accuracy-oriented methods.
distributionally robust optimizationscenario generationvariational autoencodersgenerative adversarial networksdiffusion models
Complementary Roles of Image Classification and Vessel Segmentation in AI-Based Screening for Retinopathy of Prematurity Plus Disease in a Kenyan Preterm Cohort
This work demonstrates complementary roles of image classification and vessel segmentation for AI-based screening of Retinopathy of Prematurity (ROP) Plus disease in a Kenyan preterm cohort (121 infants, 237 eyes, 1,635 images). Eleven configurations were evaluated via patient-grouped nested cross-validation, including RGB classifiers, multi-instance learning, and segmentation-classification pipelines. Vessel segmentation achieved Dice 0.533/IoU 0.368, while combined approaches optimized performance: an OR-based screen reached highest sensitivity, AND-based confirmation maximized specificity, and probability ensemble yielded balanced accuracy 0.803 (sensitivity 0.692, specificity 0.914), outperforming vision classifiers alone.
retinopathy of prematurityvessel segmentationmulti-instance learningcross-validationfundus imaging
Onnes: A Physics-Grounded Multi-Agent LLM Simulator for Cryogenic Fault Diagnosis in Quantum Computing Infrastructure
Onnes introduces a physics-grounded digital-twin simulator for cryogenic fault diagnosis in quantum computing infrastructure, combining a forward physics model with learned noise fingerprints from real BlueFors logs. The system employs a multi-agent LLM operations layer, comparing zero-shot LLM agents against supervised ML classifiers on six physics-grounded fault classes. Results show zero-shot LLMs match classifiers in detection (no significant difference) but trail in classification (0.685 vs. 0.985 accuracy); curated few-shot demonstrations and self-consistency voting improve LLM classification to 0.990, matching supervised performance with only six labeled examples. Real-hardware validation achieves 100% recall on injected faults with a 6.4% false-alarm rate.
digital-twincryogenic fault diagnosismulti-agent llmphysics-grounded simulatorfew-shot learning
TurnOPD: Making On-Policy Distillation Turn-Aware for Efficient Long-Horizon Agent Training
TurnOPD introduces turn-level budgeting for efficient on-policy distillation (OPD) in long-horizon agent training, addressing inefficiencies in vanilla OPD. The method employs adaptive rollout-depth budgeting and progressive turn-normalized loss budgeting to optimize wall-clock resource usage and balance KL supervision across decision turns. Experiments on ALFWorld, WebShop, and Multi-Hop Search demonstrate superior validation accuracy under equal training budgets, advancing the accuracy-time frontier beyond standard OPD.
on-policy distillationlong-horizon agentskl supervisionadaptive rolloutturn-level budgeting
Segmentation before Answering: Pixel Grounding for MLLM Visual Reasoning
The paper proposes SegAnswer, a method that replaces bounding boxes with pixel-level segmentation masks for visual grounding in Multimodal Large Language Models (MLLMs). By isolating target regions via fine-grained masks, it reduces background clutter and improves alignment with MLLMs' visual token structure. Evaluations across high-resolution perception, general perception, and hallucination benchmarks show consistent performance gains, while segmentation task results validate its pixel grounding capability.
multimodal large language modelsvisual groundingsegmentation maskpixel-level reasoningpositional embeddings
From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space
NapMem introduces a framework for active memory navigation in conversational agents, treating long-term user memory as a structured action space rather than passive retrieval. The method organizes user history into a linked multi-granularity memory pyramid, connecting raw conversations, memory records, topic tracks, and user profiles through provenance relations. Agents are trained via memory-tool reinforcement learning to select memory granularities based on queries and intermediate evidence. Experiments on PersonaMem-v2, LongMemEval, and LoCoMo demonstrate competitive performance on memory-intensive tasks while preserving general reasoning abilities. Analyses examine storage costs, inference efficiency, and tool-use behavior.
memory navigationmulti-granularity memorymemory-tool reinforcement learningstructured action spaceprovenance relations
Controlling Tool Use with Heading-Specific Activation Steering
The study demonstrates that tool-use decisions in large language models can be controlled via heading-specific activation steering, despite tools lacking direct weight encoding. Using steering vectors extracted from heading-anchored positions, the authors achieve bidirectional causal control over tool invocation across five open-source models and three domains, with optimal suppression in parametric reasoning contexts. Geometric analysis reveals non-linear, bimodal alignment patterns and low cross-tool feature overlap, suggesting distinct internal signatures for non-parametric tools compared to parametric concepts.
activation steeringtool-augmented llmsnon-parametric toolsheading-anchorsgeometric analysis
FORGE: Towards Functional Tool-Use Generalization via Keypoint Trajectory Reasoning
FORGE introduces a two-stage policy for functional tool-use generalization, addressing the gap between perceptual similarity and motor pattern divergence in novel tools. The method decouples functional reasoning from action execution by predicting generalizable 2D keypoint trajectories from action-free data and grounding them into robot actions with limited demonstrations. Intermediate representations, including affordance images and human video prompts, were explored, with keypoint trajectories found to optimally balance functional expressiveness and action groundability. Evaluated on a seven-tool hitting-function benchmark, FORGE outperformed state-of-the-art methods in both simulation and real-world settings, achieving over 2X improvement in average success rate for unseen tools.
functional generalizationkeypoint trajectoriesaffordance imagesaction executionrobot actions
Beyond the Leaderboard: A Synthesis of Tool-Use, Planning, and Reasoning Failures in Large Language Model Agents
This paper synthesizes 27 studies (2023-2026) into a unified taxonomy of LLM agent failures, addressing tool use, planning, reasoning, coordination, safety, and measurement. Through iterative grouping of independently reported error categories, it identifies six failure clusters: tool invocation errors, planning failures, long-horizon degradation, multi-agent coordination issues, safety vulnerabilities, and measurement validity problems. Key findings include nonlinear compounding of errors with task length, unreliable translation of sub-task performance to end-to-end success, and inconsistent benefits from scaffolding, despite progress in single-turn tool use and short-horizon tasks.
llm agentstool useplanning failuresmulti-agent coordinationsafety vulnerabilities
Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning
We present AgenticAI-Supervisor, a scalable reinforcement learning environment for evaluating autonomous agents based on Large Language Models (LLMs). The platform decouples environment creation from execution through an API and UI-driven RL Gym architecture, enabling verifiable execution outcomes and multi-dimensional reward shaping. Key innovations include rigorous internal state validation to mitigate reward hacking and generation of high-fidelity traces for closed-loop feedback. A Customer Support Agent case study demonstrates the framework's capabilities in optimizing multi-step decision-making. Future work will extend functionality to Computer Use, Tool Use, automated edge-case generation, and "stumping" scenarios.
reinforcement learninglarge language modelsreward shapinginternal state validationclosed-loop feedback
LEGATO 2: Toward Multimodal Sheet Music Recognition and Understanding
Legato 2 introduces a novel pipeline for multimodal sheet music recognition and understanding, combining system-level segmentation with an autoregressive vision-language model to extract symbolic notation and semantic knowledge from sheet music images. It is the first large-scale neural model for optical music recognition (OMR) to process inputs sequentially on a system-by-system basis, enabling scalability to arbitrarily long inputs, and the first OMR model capable of generating symbolic transcriptions with embedded textual content. Legato 2 consistently outperforms prior state-of-the-art methods across multiple datasets and enhances the interpretation of dense musical documents by frontier language models through symbolic transcriptions.
optical music recognitionautoregressive vision-language modelsystem-level segmentationsymbolic transcriptionmultimodal recognition
Synthetic Consumer Insight Generation with Large Language Models
The study demonstrates that large language models (LLMs) can generate synthetic consumer data for projective techniques in marketing research. Using multiple LLMs, prompting strategies, and temperature settings, the authors compared synthetic responses with human data from a city tourism perception study through linguistic analysis, diversity metrics, and topic modeling. Results indicate substantial overlap in broad topics but reveal stylistic and structural differences, with recommendations provided for optimizing LLM use in synthetic data generation.
large language modelssynthetic data generationprojective techniqueslinguistic analysistopic modeling
Data-dependent Evaluations for Budgeted Submodular Maximization
The authors introduce data-dependent upper bounds for submodular maximization with a knapsack constraint, addressing the challenge of evaluating solution quality in NP-hard problems. They develop theoretical proofs demonstrating that these bounds dominate the optimal solution, providing tighter approximations than worst-case guarantees. Empirical validation on real-world datasets shows the effectiveness of these bounds in certifying solution proximity to optimality. This approach enhances the practical assessment of submodular maximization algorithms in machine learning and data mining applications.
submodular maximizationknapsack constraintdata-dependent boundsnp-hardapproximation factor
When Should LLMs Search? Counterfactual Supervision for Search Routing
We address the search-routing problem in search-augmented language models, determining when external search improves task success versus no-search execution. Using counterfactual supervision, we construct an oracle labeling instances as NO SEARCH, SEARCH, or UNSOLVED based on task-specific outcomes. We train routing policies via supervised fine-tuning and preference optimization, improving macro-F1 from 0.7082 to 0.8235 for Gemma E2B and from 0.7053 to 0.8365 for Qwen3.5-4B. Analysis reveals model-specific improvements: Gemma learns no-search restraint, while Qwen reduces missed searches; residual UNSOLVED cases highlight bottlenecks in model capacity, retrieval budget, evidence use, and policy behavior.
search-routingcounterfactual supervisionmacro-f1preference optimizationretrieval budget
ArtisanCAD: An Industrial-Level CAD Agent with Expert-Grounded Knowledge Distillation
ArtisanCAD introduces a skill-guided industrial CAD agent that bridges ambiguous textual prompts and executable CAD construction through expert-grounded knowledge distillation. The method centers on CAD intermediate representation (CAD-IR), which encodes parameters, operations, dependencies, and verification rules, serving both as a carrier for distilling expert procedures and a scaffold for prompt-to-operation translation. Evaluated on Text2CAD, CAD-IR reduces mean Chamfer Distance from 14.83 to 9.88 for intermediate prompts, and successfully generates editable CATIA-native B-Rep models for complex automotive components by distilling expert recordings into reusable skills.
cad intermediate representationknowledge distillationb-rep modelstext2cad benchmarkcatia-mcp backend
Unicode TAG-Block Concealment of Tool-Metadata Payloads in the Model Context Protocol: An Approval-View Fidelity Gap Across Three Independent Server Implementations
The paper identifies a fidelity gap in the Model Context Protocol (MCP) where Unicode TAG-block (U+E0000–U+E007F) payloads can bypass human approval views while reaching the model's context verbatim. Through protocol-level analysis and a proof-of-concept implementation tested against 3 independent MCP server libraries, the authors demonstrate that 8/8 concealment techniques deliver attacker-controlled payloads, with 4/8 evading string-matching sanitizers. Only TAG-block encoding (1/8) remains invisible in approval views while persisting in model inputs, with consistent results across all tested implementations (32/32 cases). Baseline sanitizers showed 0 false positives on benign descriptions.
model context protocolunicode tag-blockconcealment encodingjson-rpctime-of-check
The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
The paper systematizes 39 studies (2023-2026) on execution-security vulnerabilities in AI coding agents, identifying 17 research categories and five cross-cutting gaps. Through direct source verification and analysis of production CVEs, it reveals disconnects between isolation architectures, capability models, and policy enforcement (failure rates: 69-98%). Key gaps include unaddressed TOCTOU/MCP state-validation equivalences, unexamined policy-authoring errors, and benign out-of-scope actions (occurring at 17.1% rates). The work proposes a dedicated research agenda for execution security, contrasting with broader AI security surveys that treat sandboxing peripherally.
execution securitytoctoucapability modelspolicy enforcementagentic ai
SCOReD: Student-Aware CoT Optimization for Recommendation Distillation
SCOReD introduces a student-aware Chain-of-Thought (CoT) optimization framework for recommendation distillation, addressing challenges of teacher trace verbosity and distribution mismatch. The method parses teacher traces into typed segments, scores segment importance via student attention, and applies dynamic edits (KEEP/REWRITE/FUSE/PRUNE) based on output length and log probability lift. This yields optimized CoTs that improve student training signals, achieving +1.56% NDCG and +1.9% Recall@5 over baseline SFT while reducing reasoning length by 27.3%.
chain-of-thought distillationrecommendation systemsattention scoringlog probability liftsupervised fine-tuning
Plainbook: Data Science, in Plain Language
Plainbook introduces a code-free alternative to Jupyter Notebooks for data science, targeting non-programming scientists through natural language descriptions and automated code generation. The system enforces linear execution semantics to prevent hidden state issues and provides value-centric verification mechanisms, including cell-level unit tests and global checks. A snapshot kernel underpins these features by caching execution states for efficient computation and validation.
natural language processinglinear execution semanticssnapshot kernelvalue verificationautomated code generation
Akashic: A Low-Overhead LLM Inference Service with MemAttention
Akashic introduces a low-overhead memory system with MemAttention to address inefficiencies in LLM-based agent systems handling multi-turn interactions and cross-session workflows. It organizes context into bounded chunks, models semantic relationships across chunks, and employs hardware-software co-designed memory placement to co-locate likely co-retrieved chunks. This approach reduces retrieval fragmentation and I/O overhead while preserving cross-chunk evidence. Evaluated across four workloads and three model sizes, Akashic improves task accuracy by up to 10.2 points, throughput by up to 1.21x, and sustainable request rate by up to 1.88x compared to prior memory baselines.
memattentioncontext chunkshardware-software co-designretrieval fragmentationi/o overhead
IMR: Iterative Mode-World Weighted Regression for Multi-Agent Trajectory Prediction
The paper introduces Iterative Mode-World Weighted Regression (IMR), a novel approach for multi-agent trajectory prediction that addresses limitations in mode diversity and prediction accuracy. IMR employs a mode-world weighted regression loss to mitigate mode collapse while enhancing world ranking and top-1 confidence. Additionally, an iterative decoder recurrently generates trajectories in segments, improving prediction precision. Evaluated on the Argoverse 2 multi-agent motion forecasting benchmark, IMR achieves state-of-the-art performance, ranking first among competing methods.
multi-agent trajectory predictionmode collapseworld rankingiterative decoderargoverse 2
Memory in the Loop: In-Process Retrieval as ExtendedWorking Memory for Language Agents
The paper introduces an in-process memory architecture for language agents that reduces retrieval latency to ~100μs, enabling per-step memory access without significant overhead. By replacing networked stores with local in-process storage, the system achieves 3.6-4.8/5 recall improvement across four GPT-5-class models, compared to 0/5 with traditional methods. Key findings include 244/244 write retention, embedding as the new bottleneck (~200-400ms), and a complete operation time of ~40μs when paired with a local embedder.
in-process retrievalextended working memorylatency optimizationlanguage agentsembedding bottleneck
Depression Symptoms and Relational Patterns in 187k ChatGPT Histories
This study investigates depression-related usage patterns in ChatGPT conversations by analyzing 187,093 dialogues from 766 PHQ-8 screened participants. Using computational linguistics and behavioral analysis, researchers compared users below (PHQ-8<10) and above the moderate-symptom threshold. Higher-PHQ users exhibited increased mental-health discussions (23% more first-person singular pronouns), nocturnal activity peaks, and recurring monthly patterns, with language showing more absolutist terms. While disclosure rates were higher, professional redirection remained unchanged. Language-based depression prediction achieved only AUROC 0.591, suggesting LLM histories are unsuitable for clinical screening but demonstrate emergent use as informal support infrastructure.
large language modelscomputational linguisticsphq-8behavioral analysismental-health support
FirstResearch: Auditable Question Formation for LLM Scientific Discovery Agents
FirstResearch introduces a structured framework for generating auditable research questions via LLM scientific discovery agents, centered on a Research Question Certificate that records primitive definitions, assumptions, and falsifiable hypotheses. The method outperforms baseline approaches (AI co-scientist, Agent Laboratory, AI Scientist-v2) in a DeepSeek-blind-judge protocol (4.86/5 vs. 4.38/5) and maintains ranking under Gemini-2.5-Flash rescoring (Pearson agreement 0.865). Ablation studies confirm the certificate's critical role, with certificate-only scoring reaching 4.90/5 (DeepSeek) and 4.88/5 (Gemini), while removal drops performance below 1/5.
llm agentsscientific discoveryresearch question certificatefalsifiable hypothesisauditability
Beyond Accuracy: How Humans Evaluate Legally Correct but Socially Controversial Legal Advice from Machines
The study investigates laypeople's evaluation of legally correct but socially controversial legal advice from AI versus human sources, revealing no net effect of source attribution but opposing psychological pathways. Through a preregistered survey experiment with 3,348 Chinese adults, researchers manipulated advice source (AI/lawyer) and presence of reasoning, measuring perceived reasonableness. Mediation analyses showed AI-attributed advice was perceived as more objective (increasing reasonableness) but less comprehensive and contextually attentive (decreasing it), while reasoning enhanced objectivity regardless of source. Qualitative responses confirmed the tension between objectivity and contextual sensitivity in legal advice evaluations.
algorithm aversionlegal advice systemsmediation analysisnormative expectationsperceived objectivity
RPAM: A Principled Metric for Evaluating Associations in Language Models with High Predictive Validity in Downstream Outputs
The paper introduces Relative Probability Association Metric (RPAM), a novel upstream evaluation metric for quantifying associations in generative language models with demonstrated predictive validity for downstream biases. RPAM analyzes continuation probabilities rather than generated text, enabling cross-model comparisons without task-specific datasets. Evaluated on Mistral-7B-Instruct, Mistral-7B, and GPT-2 using WEAT-WS, Bellezza, WS-353, and SST2 benchmarks, RPAM shows strong correlations with both human association patterns (implicit/explicit) and downstream bias measurements, outperforming prior approaches.
association metricgenerative language modelsupstream evaluationcontinuation probabilitiespredictive validity
What Do AI Agents Actually Change? An Empirical Taxonomy of Mutation Patterns in Performance-Improving Pull Requests
This study presents an empirical taxonomy of mutation patterns in AI-generated performance-improving pull requests, revealing distinct operator profiles compared to traditional genetic improvement. Analyzing 1,254 diff hunks from 216 PRs across five agent systems using a dual-LLM intersection pipeline against an 18-category syntactic taxonomy, the work identifies three dominant mutation types: name modification (37.0%), object creation (26.4%), and type change (22.7%). Results show agent-specific mutation vocabularies and strategy-dependent category activation, suggesting agent identity and target strategy as informative priors for narrowing SBSE operator spaces.
mutation patternsgenetic improvementdual-llm pipelinesyntactic taxonomyperformance optimization
Physics-Regularized Machine Learning for Proprioceptive Vehicle Localization Using Onboard Sensors
The paper introduces Physics-Regularized Machine Learning for Localization (PRML2), a hybrid framework combining Kalman filtering with data-driven learning for vehicle pose estimation using onboard sensors. PRML2 employs end-to-end training of an ML model through a differentiable Kalman filter, enforcing physics-based motion priors to improve accuracy and generalization. Evaluated on a public dataset, PRML2 achieves superior localization accuracy and real-time performance. The work also contributes a novel low-friction driving dataset. The framework offers a cost-effective solution for degraded sensing conditions by integrating learning with physical constraints.
physics-regularized learningdifferentiable kalman filterproprioceptive localizationonboard sensorslow-friction dataset
Do It Right! A Methodology for Successful NLP System Development
This paper contributes a methodological framework for Natural Language Processing (NLP) system development in clinical research, emphasizing the Systems Development Life Cycle (SDLC) as a structured approach. The authors synthesize existing literature to propose a stepwise methodology that extends beyond algorithmic implementation to encompass project planning, requirements analysis, and system deployment. The framework addresses the extraction of clinical data from electronic medical records, highlighting the integration of NLP techniques within a broader system development context. While specific results are not quantified, the methodology provides a systematic pathway for successful NLP project execution in healthcare settings.
natural language processingsystems development life cycleclinical researchelectronic medical recordsdata extraction
EvalLoop: A Methodology for Evaluation-Driven Iterative Improvement of Business AI Systems
The paper introduces EvalLoop, a methodology for evaluation-driven iterative improvement of business AI systems, addressing the limitation of static model selection. EvalLoop employs dimensional metric grouping, failure mode classification, and a structured iteration workflow to diagnose and rectify system underperformance. In a case study on sales intelligence briefing generation (10 models, 3 providers), EvalLoop identified 69% of hallucination failures as prompt-induced, leading to a targeted fix that improved the best model from 82.6% to 94.6% overall, with Content Accuracy and Synthesis Power increasing by 16.8pp and 26.4pp, respectively. The method also reduced human review burden by 94% through dimensional profiling and blind human gating.
evaluation-driven iterationdimensional metric groupingfailure mode classificationstructured iteration workflowhallucination failures
Safe Bayesian Optimization with Counterfactual Policies
The paper introduces a method for safe Bayesian optimization when safety constraints depend on counterfactual outcomes of a baseline policy, which are unobserved. The authors use conformal prediction to construct valid uncertainty intervals for these counterfactual outcomes, ensuring constraint violations occur at a user-specified rate. The approach handles covariate shift and is supported by a safety proof, experimental validation, and sensitivity analysis.
safe bayesian optimizationcounterfactual policiesconformal predictioncovariate shiftuncertainty intervals
BaFCo: A Document Understanding Benchmark for Complex Bangla Form Comprehension
We introduce BaFCo, a benchmark dataset for Bangla form comprehension addressing Document Layout Analysis (DLA) and Key Information Extraction (KIE). BaFCo comprises 200 multi-page Bangladeshi government forms from diverse sectors, annotated with a fine-grained schema of 26 form entity types and a coarse set of 5 types. We evaluate state-of-the-art Multimodal Large Language Models (MLLMs) including ChatGPT, Gemini, Claude, Qwen, and Kimi using zero-shot and chain-of-thought prompts under low and high reasoning setups. Results indicate significant limitations in MLLMs' ability to localize granular form entities in Bangla, highlighting the need for improved low-resource language comprehension.
document layout analysiskey information extractionmultimodal large language modelschain-of-thoughtzero-shot
To Retain or to Adapt? Generalizing Continual Learning
The paper challenges the retention-centric paradigm in Continual Learning (CL) by formalizing it as an online optimization problem governed by environmental and learning dynamics. Introducing Transfer Efficiency, the authors decompose the tension between Instability (bias from past experience) and Transient Error (cost of learning new tasks) to derive a Critical Task Duration threshold. This threshold determines when historical knowledge transitions from beneficial to detrimental. Theoretical predictions are validated on image classification and reinforcement learning benchmarks. The authors propose Predictive Continual Learning, a new class of algorithms optimizing future performance under dynamically updated task models, exemplified by a Window algorithm outperforming Joint-Task Learning and Independent-Task Learning under controlled drift.
continual learningtransfer efficiencycritical task durationpredictive continual learningonline optimization
Hierarchical Classification via Cascading Feature Elimination: Application to Human Phenotype Ontology-Aligned Facial Phenotyping (FaceMesh2HPO)
FaceMesh2HPO introduces a hierarchical classification framework for Human Phenotype Ontology-aligned facial phenotyping using 3D facial meshes. The method employs a PointNet-based pipeline with cascading classification and feature elimination, processing 478 landmarks from 2D-derived 3D meshes combined with facial outlines and demographic metadata. Evaluated on clinician-annotated data (124 clinicians, 10 disorders, 107 HPO terms), the best models achieved AUROCs of 0.55-0.89, with superior performance at parent nodes versus leaf terms. External validation revealed disorder-dependent generalizability, highlighting limitations for rare phenotypes.
hierarchical classificationhuman phenotype ontology3d facial meshespointnetfeature elimination
ResonatorLM: Causal Resonant Field Mixing for Efficient Long-Context Language Modelin
ResonatorLM introduces a novel mechanism for efficient long-context language modeling by replacing attention with a physics-derived alternative, treating token sequences as a driven one-dimensional latent field and using causal functions of damped resonators. Implemented on a traditional network architecture, it achieves significant speedups: 6.47x decode speed at 32K tokens and 61.31% accuracy on WikiText, compared to 55.32% for transformers, in a 6M parameter setting.
resonatorlmattentiondamped resonatorslatent fieldlong-context modeling
Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction
The Narrative World Model (NWM) introduces a narratology-grounded writer-memory system for long-form fiction, addressing multi-hop questions about story state through a typed temporal-state graph and query-conditioned hybrid retrieval. NWM outperforms Graphiti/Zep, GraphRAG, and flat retrieval on multi-hop narratological QA across two corpora, with gains attributed to its representational structure rather than extraction quality. Evaluated using a held-constant Opus 4.8 reader, NWM demonstrates significant improvements in evidence retrieval for complex narrative queries.
narrative world modelmulti-hop qatemporal-state graphquery-conditioned retrievalnarratology
Whose fairness? Structural concentration in AI bias research
The study reveals structural concentration in AI bias research, demonstrating that fairness definitions and mitigation methods are predominantly shaped by a geographically limited research community. Through bibliometric analysis and semantic clustering of 692 publications across five domains, the authors find US-dominated publication output (strongest in general fairness/bias mitigation) and skewed citation influence (median=9, mean=93.5), with minimal participation from low/middle-income countries. This concentration raises concerns about the generalizability of bias mitigation methods developed in narrow contexts to diverse deployment settings.
ai biasfairness definitionsbibliometric analysisstructural concentrationmitigation methods
Foundation Models for Automatic CAD Generation
The study introduces LLMForge, a multi-model text-to-CAD framework integrating JSON-schema validation, analytic feature scoring, mesh synthesis, and iterative refinement, evaluated on a benchmark of 97 engineering design problems. Two critique regimes are employed: IterTracer uses a ray-trace renderer with analytic visual metrics, while IterVision replaces the analytic scorer with a VLM semantic critic (Qwen2.5-VL-72B) for visual reasoning. Seven foundation models are evaluated, with IterTracer showing a tight performance cluster (mean [0.885, 0.890]) and 98.97% mesh success. IterVision achieves 100% watertight mesh generation but highlights challenges with rotationally symmetric geometries.
text-to-cadfoundation modelsiterative refinementmesh synthesisvisual reasoning
CSTutorBench: Benchmarking Small Language Models as Tutors for Block-Based Programming
The paper introduces CSTutorBench, a benchmark for evaluating small language models (SLMs) as tutors in block-based programming (VEX VR), addressing gaps in model selection for educational contexts. The benchmark uses 17 scenario-based questions scored via a pedagogical rubric and LLM-as-judge pipeline, evaluating 11 models (4B-120B parameters). Results show models excel in vocabulary and tone but struggle with pedagogical depth (e.g., avoiding answer leakage, leveraging debugging histories), with model family and instruction-tuning outperforming parameter count as predictors. Prompt revisions improved 10/11 models, highlighting the need for domain-specific benchmarks.
small language modelsblock-based programmingpedagogical rubricinstruction-tuningllm-as-judge
Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction
The paper introduces a training-free method for 3D primitive shape abstraction by leveraging pretrained generative image models. The pipeline renders multi-view images of a 3D object, uses a vision-language model for semantic part analysis, generates color-coded part segmentation masks via a generative image model, reprojects these onto the geometry, and fits superquadric primitives to each part through parameter optimization. This approach is category-agnostic, orientation-invariant, and requires no learned parameters. Evaluated on HumanPrim and Toys4K, it achieves the lowest Chamfer distance among compared methods, using 5--9 primitives per object on average.
generative image modelsprimitive shape abstractionsuperquadric fittingtraining-freechamfer distance
From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond
The paper introduces a physics-inspired framework for structural attribution in cyber-physical IoT systems, leveraging an undirected, energy-based representation to model variable dependencies without recovering directed causal graphs. The method analyzes variations in the energy landscape to attribute influences of individual components, enabling dependency-aware explanations and reasoning about perturbation effects across hybrid interactions. Empirical evaluation on an industrial IoT testbed with hybrid continuous and discrete variables demonstrates superior attribution accuracy, robustness, and scalability compared to state-of-the-art graph-based approaches. The framework supports human interpretation and downstream predictive tasks, extending applicability to high-dimensional cyber-physical and socio-technical systems.
energy-based representationstructural attributioncyber-physical systemsdependency-aware explanationsperturbation effects
Prompt Robustness Is Task-Dependent: Comparing Objective and Belief-Style Questions in LLM Evaluation
The study demonstrates that prompt robustness in large language models varies significantly between objective and subjective questions, influenced by model, dataset, and prompt category interactions. Researchers evaluated four instruction-tuned model families on three objective datasets (MMLU, ARC, CulturalBench) and three subjective datasets (Political Compass Test, ValueBench, World Values Survey), applying multiple prompt variations to measure answer consistency. Results from a binomial generalized estimating equation revealed significant effects of dataset type and its interaction with prompt category, highlighting task-dependent prompt sensitivity.
prompt robustnessinstruction-tuned modelsgeneralized estimating equationobjective datasetssubjective datasets
The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment
This study disentangles the yes-no bias in large language models (LLMs) by introducing a psychometric battery using crossed symmetrization to isolate logically irrelevant factors in question framing. Results show that frontier models exhibit nearly format-invariant moral judgments (cross-form incoherence 0.12-0.21), while smaller models fail in model-specific ways. The bias decomposes into an order bias toward the last-printed option and a lexical pull toward the word 'no,' significant only in Claude models (-0.32 to -0.86) and negligible in GPT-5.5 and Gemini. Swapping words for arbitrary labels reveals that the bias follows surface features, not logical verdicts, with a minimal model summarizing framing susceptibility and moral decisiveness.
crossed symmetrizationyes-no biaspsychometric batteryformat-invariantframing susceptibility
Most LLM Conformity Needs No Speaker: Measuring the Speaker-Free Floor in Peer-Pressure Benchmarks
The study demonstrates that LLM conformity effects persist even when removing explicit speaker cues, revealing a confound in standard benchmarks where answer repetition alone drives harmful revisions. Researchers introduced a no-source condition across six open-weight LLMs and seven QA/reasoning datasets, finding that 66.5% of initially correct answers were erroneously revised without speaker presence versus 10.3% in plain re-asks. Effects remained robust under paraphrasing and open-ended formats, with source framing primarily modulating revision rates. The work establishes a methodological imperative to measure speaker-free conformity floors before attributing revisions to social influence.
llm conformitypeer-pressure benchmarkssource attributionanswer revisionsocial influence confound
Self-Review Reinforcement Learning (SRRL) with Cross-Episode Memory and Policy Distillation
The paper introduces Self-Review Reinforcement Learning (SRRL), a framework that enhances RL training for language models by incorporating an explicit self-review step. SRRL generates self-reviews to diagnose failures, conditions improved second attempts, and internalizes corrections via policy distillation and cross-episode memory. Evaluated on GSM8K with Qwen-3-4B and OLMo-3-7B using GRPO, SRRL outperforms RLVR in final reward (exact gains unspecified) and learning efficiency by effectively converting feedback into policy improvements.
self-review reinforcement learningpolicy distillationcross-episode memoryrlvr baselinegrpo optimizer
Rendering-Aware Bayesian 3D Gaussian Splatting with Native Uncertainty and Adaptive Complexity Control
The paper introduces a Bayesian 3D Gaussian splatting (3DGS) framework that incorporates native uncertainty estimation and adaptive complexity control through Normal-Inverse-Wishart (NIW) posteriors over Gaussian geometry parameters. The method employs renderer-derived surrogate summaries for probabilistic tracking and offers optional Dirichlet-process extensions for component-usage signals. Evaluated on a fixed-budget 16-to-32 active-view task, the approach improves PSNR by +0.453 dB and LPIPS by -0.0146 over baselines, while reducing 95% coverage error by 17x compared to proxy methods. The framework maintains reconstruction quality (+0.030 dB PSNR) with minimal computational overhead (1.6% additional training time).
3d gaussian splattingbayesian inferencenormal-inverse-wishartactive view selectionuncertainty calibration
aiAuthZ: Off-Host, Identity-Bound Authorization for AI Agents
The paper introduces aiAuthZ, an off-host authorization gateway for AI agents that prevents unauthorized tool calls by verifying caller identity and enforcing role-based policies. The system uses HMAC-SHA256 signatures with single-use nonces, timestamp windows, and hash-chained audit logs, achieving 94% verification accuracy across eight transmission channels with zero forgeries in 25 trials. Evaluation on 15 language models and the AgentDojo banking suite shows aiAuthZ reduces attack success to 0% (from 38-100% refusal rates in baseline models) with ≤0.03ms latency overhead, blocking all attacker-directed tool calls while allowing legitimate operations.
authorization gatewayhmac-sha256identity bindingtool call verificationaudit log
Statistical Adversaries: Natural Backdoor-like Features in Vision Datasets
The paper identifies naturally occurring statistical signals in vision datasets that function as backdoor-like triggers without malicious insertion, termed 'statistical adversaries'. Using ImageNet, the authors analyze label-linked patterns, apply statistical controls to eliminate random correlations, and demonstrate these signals predictably influence model predictions. Results show these adversaries are more targeted than generic corruptions and transfer across architectures, indicating vulnerabilities stem from dataset structure rather than model-specific traits. The findings suggest ordinary datasets contain latent adversarial surfaces, necessitating audits for spurious structure as both bias sources and attack vectors.
statistical adversariesbackdoor triggersdataset auditsspurious correlationsvision models
Lean-Quantum: Toward AI-Assisted Formalization of Quantum Information
The authors present Lean-Quantum, a Lean 4 library formalizing quantum information theory, with a focus on the data processing inequality (DPI) for sandwiched Rényi relative entropy. The library establishes a basis-independent operator-theoretic framework for finite-dimensional quantum systems, integrating with Mathlib and providing interfaces for states, channels, tensor products, and trace inequalities. Key results include a machine-checked proof of DPI, derivation of strong subadditivity, and completion of the formalization for the generalized quantum Stein's lemma. The work enables AI-assisted formal verification in quantum information theory through reusable infrastructure.
quantum information theoryformal verificationsandwiched rényi entropydata processing inequalitylean theorem prover
LLM-as-a-Verifier: A General-Purpose Verification Framework
The paper introduces LLM-as-a-Verifier, a general-purpose verification framework that enhances LLM capabilities by treating verification as a new scaling axis. The method computes continuous scores via expectation over scoring token logits, enabling scaling along granularity, repeated evaluation, and criteria decomposition. This approach achieves SOTA on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%), while also improving RL sample efficiency via dense feedback.
verification frameworkscaling axistoken logitsagentic taskssample efficiency
Multiplayer Interactive World Models with Representation Autoencoders
The paper introduces the first multiplayer world model for dynamic environments with complex physical interactions, specifically tested in Rocket League. The 5-billion-parameter latent diffusion model conditions on action streams from multiple agents, enabling stable real-time generation of four-player matches at 20 fps on a single Nvidia B200 GPU. Trained on 10,000 hours of bot gameplay, the model maintains distributional quality for up to five minutes and exhibits emergent capabilities, with systematic evaluations of design choices and failure modes.
multiplayer world modellatent diffusion modeldynamic environmentsaction streamsemergent capabilities
Shape Over Intensity: Directional Topological Encoding for False Positive Reduction in Intracranial Aneurysm Detection
The study introduces a topology-aware false-positive reduction framework for intracranial aneurysm detection, addressing the high false-positive rates in CNN-based methods caused by intensity-based confusion between aneurysms and vascular bifurcations. The proposed method evaluates the Smooth Euler Characteristic Transform (SECT), a directional representation encoding global 3D vascular geometry independently of intensity, against persistence-based summaries. On the RSNA 2025 dataset, SECT achieves an AUC of 0.943, outperforming direction-agnostic methods (AUC ~0.68), and maintains 78.5% sensitivity at 95% specificity for sub-3 mm lesions, while demonstrating scanner-agnostic performance (0.927 AUC under LOGO validation).
smooth euler characteristic transformfalse-positive reductionintracranial aneurysm detectiontopological encodingct angiography
PatchOptic for Shared-State LLM Workflows with Projected Views and Verified Structured Updates
The paper introduces PatchOptic, an optic-inspired interface for managing shared-state LLM workflows with projected views and verified structured updates. The method employs compositional bidirectional accessors to define read views, authorized write regions, and patch-source regions, enabling runtime enforcement and static analysis for workflow composition. Evaluation on PatchBench (46 cases) shows projected reads reduce context leakage and token costs while maintaining output quality, with runtime verification preventing contract violations and patch-read enforcement blocking compromised artifacts.
shared-state llm workflowsprojected viewsverified structured updatesruntime enforcementpatchbench
Full-range Binary Classifier Calibration for Stable Model Updates in Production
The paper introduces a binary classifier calibration method that maintains consistent false-positive rate (FPR) across model updates for security applications. The approach extends existing calibration primitives to target the full FPR curve rather than class probabilities, addressing distribution drift in adversarial environments. Evaluations show relative FPR errors of ≤2.3% for FPRs 0.1-10% and 7.2% at 0.01% FPR, with a compact deployment artifact (<200KB) scalable from 1K to 10M benign samples.
binary classifierfalse-positive ratemodel calibrationadversarial distributiondeployment stability
Unified Audio Intelligence Without Regressing on Text Intelligence
The paper introduces Nemotron-Labs-Audex-30B-A3B (Audex), a unified audio-text LLM based on Nemotron-Cascade-2-30B-A3B, a text-only MoE LLM. Audex employs a single Transformer decoder architecture where audio inputs are projected into text embedding space, enabling multimodal generation and compatibility with standard LLM infrastructure. Trained on 157.4B audio tokens and 320.5B text tokens via multi-stage supervised training, Cascade RL, and multi-domain distillation, Audex achieves state-of-the-art performance in audio understanding, speech recognition, translation, and generation while preserving text-only capabilities with minimal regression.
multimodal llmaudio-text fusionmixture-of-expertson-policy distillationquantized audio tokens
Privilege and confidentiality in generative AI workflows
The article analyzes confidentiality risks in generative AI (GenAI) systems through three data processing modes: parameter memorization during training, context window retention during inference, and retrieval-augmented generation (RAG) databases. It synthesizes legal precedents (UK and Munir v Secretary of State, United States v Heppner) with computer science research to establish governance frameworks for legal professionals under England/Wales' SRA regulations. Findings indicate evolving negligence standards due to GenAI's counterintuitive data persistence mechanisms, requiring sector-specific confidentiality safeguards for privileged information.
generative airetrieval-augmented generationconfidentialitylegal privilegedata governance
GraphBU: MILP Instance Generation with Graph-Native Block Units
GraphBU introduces a graph-native MILP instance generator using block units composed of local subproblems with explicit interfaces, preserving structural coupling crucial for solver performance. The method employs promotion to separate interfaces and compatibility-checked replacement, maintaining permutation invariance and feasibility under interface-slack conditions. Experiments show 0.934 average graph-statistical similarity to source families, 96.7% feasibility preservation, and 8.0% improvement in Predict-and-Search training performance.
mixed-integer linear programminggraph-native generationblock unitsfeasibility preservationpredict-and-search
EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning
EntroPath introduces a manifold learning method that recovers geodesic geometry through ensembles of diffusion paths, addressing limitations of existing graph-based embeddings. The method employs maximum entropy random walks (MERW) to aggregate k-step paths between points, yielding a free-energy dissimilarity that converges to squared geodesic distance via Varadhan's heat-kernel formula. EntroPath's diffusion depth k interpolates between local and global structure, with scalable extensions via landmark projection. Evaluations on synthetic manifolds and single-cell data show superior performance over diffusion- and shortest-path-based methods, particularly in non-uniform sampling and branching trajectories.
manifold learningmaximum entropy random walkgeodesic geometrydiffusion pathsfree-energy dissimilarity
Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles
The paper investigates the impact of poisoning attacks on augmented 3D point cloud datasets for connected and autonomous vehicles, focusing on whether data augmentation mitigates or exacerbates such attacks. Using Generative Adversarial Networks (GANs) for augmentation, the authors demonstrate that poisoning attacks can evade sanitization effects, propagate through augmented datasets, and perturb classifier decisions. Experimental validation confirms these findings, with all tools and datasets made publicly available for reproducibility.
poisoning attacks3d point cloudsdata augmentationgenerative adversarial networksautonomous vehicles
Learning to Throw Objects Safely in Multi-Obstacle Environments
The paper introduces a potential field representation (PFR) for safe robotic throwing in cluttered environments, addressing the limitation of prior work like TossingBot that assumes obstacle-free settings. The method combines kinesthetic demonstrations with reinforcement learning (SAC, DDPG, TD3) in simulation, using PFR to encode both target attraction and obstacle repulsion on a fixed grid. SAC outperforms other algorithms, with real-robot experiments achieving 90% success in unseen cluttered scenes, demonstrating robust sim-to-real transfer and generalization to varying obstacle configurations.
potential field representationrobotic throwingreinforcement learningsim-to-real transferobstacle avoidance
A Function-Space Dichotomy for Compositional Learning: Exponential Sub-Optimality of the Neural Tangent Kernel
This work establishes a function-space dichotomy explaining when and by how much neural networks outperform their neural tangent kernel (NTK) limit on compositional tasks. The authors introduce two complexity measures: Fourier complexity governing NTK regression and architectural complexity governing ReLU networks with bounded weight variation. They prove a minimax rate for depth-L, width-w networks and demonstrate that NTK requires exponentially more samples than this floor when complexities decouple, as shown for the depth-L iterated sawtooth. Experiments confirm NTK's competitiveness on smooth targets but reveal a 4-6 order magnitude gap on sparse-parity tasks, attributing this to a mismatch between kernel smoothness and target compositionality.
neural tangent kernelcompositional learningminimax ratefourier complexityarchitectural complexity
Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement
The paper introduces ActionCache, a training-free acceleration method for Vision-Language-Action (VLA) models that reduces inference latency by reusing past intermediate actions. The approach employs an external cache storing actions with multimodal keys, enabling retrieval from similar contexts across episodes or tasks. Experiments show ActionCache maintains high success rates while achieving 11.75× and 34.43× speedups for π₀.₅ and GR00T-N1.6 models respectively in simulation and real-world environments.
vision-language-action modelsaction cachingflow matchinginference accelerationmultimodal retrieval
Physics-Informed Neural Embeddings of PDE Solution Families
The authors propose a physics-informed neural network framework for learning low-dimensional embeddings of PDE solution families. The method employs a multihead architecture with a shared body encoding the solution manifold and linear heads reconstructing individual solutions, augmented by a head-orthogonalization penalty to stabilize latent representations. Applied to Burgers, heat, and wave equations with latent dimension 20, results show strong dimensional reduction (2-4 components capture 95% variance for Burgers). Frequency analysis reveals invariant spectral profiles across training runs, establishing these as robust geometric observables of solution manifolds.
physics-informed neural networkssolution manifoldsdimensional reductionprincipal component analysispartial differential equations
Dithered Gaussian Mechanism for Randomness-Efficient Differential Privacy
The paper introduces the dithered Gaussian mechanism, a differentially private alternative to the discrete Gaussian mechanism that discretizes outputs rather than noise distributions. By treating discretization as post-processing of the standard Gaussian mechanism, it inherits privacy guarantees while mitigating floating-point vulnerabilities. The method reduces randomness requirements by separating cryptographically secure sampling (for privacy) from public randomness (for discretization), achieving bit-efficiency independent of noise magnitude. Experiments demonstrate practical utility in DP-SGD training with minimal overhead and enhanced security against floating-point attacks.
differential privacygaussian mechanismrandomness-efficiencydiscretizationdp-sgd
Quantitative Gaussian-Process limits of Tensor Programs
The work establishes quantitative Gaussian-process limits for infinite-width neural networks using tensor programs, providing explicit finite-width error bounds in Wasserstein distance. The architecture-agnostic framework yields convergence rates of order 1/√width, applicable to feed-forward, recurrent, and transformer-type models with weight-sharing schemes. Results demonstrate precise finite-network approximations to their Gaussian-process counterparts across diverse architectures.
tensor programsgaussian-process limitwasserstein distanceweight-sharinginfinite-width networks
Kernel-based Operator Learning: Error Analysis, Budget Allocation, and a Physics-Informed Extension
The paper presents a kernel-based operator learning framework with theoretical error analysis and budget allocation conditions. The method employs a two-stage sampling approach: offline kernel regression learns a discretized operator representation from input-output pairs, while online kernel reconstruction predicts outputs from new inputs. Key contributions include derived scaling laws coupling training pairs (N), input observations (n), and output resolution (m) for convergence, plus a physics-informed extension penalizing PDE residuals during reconstruction. Experiments validate the theoretical error decomposition and demonstrate the physics-informed variant's efficacy without retraining.
kernel regressionoperator learningerror decompositionphysics-informedscaling laws
A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems
The paper presents a convex approximation framework for neural likelihood-based Bayesian inverse problems, addressing limitations of classical probabilistic inference in high-dimensional settings. By employing un-normalized potentials and incorporating normalization into the training objective, the method ensures strict convexity in the learning problem. Theoretical analysis demonstrates that empirical minimizers converge to the true likelihood with increasing sample size. Numerical experiments validate the approach on deblurring and non-linear PDE-based imaging tasks, showing practical efficacy.
bayesian inverse problemsneural likelihood approximationconvex optimizationun-normalized potentialskullback-leibler divergence
Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions
The study establishes entanglement as an independent axis of complexity affecting generalization in quantum reinforcement learning policies and value functions. Using PAC-Bayesian analysis, the authors demonstrate that generalization is governed by the Fisher effective dimension rather than raw parameter count, with entanglement inflating this dimension. Empirical validation across supervised classification, quantum contextual bandits, and value-function tasks shows entangled circuits generalize worse than non-entangled counterparts at equal parameter counts, with effects persisting on IBM Heron hardware under noise.
pac-bayesianquantum reinforcement learningfisher effective dimensionparameterized quantum circuitsentanglement
Canopy: A Heterograph Foundation Model for Metabolic Engineering
Canopy introduces a heterogeneous graph foundation model for metabolic engineering, integrating 6.9M nodes across 13 types and 34 edge types from ten data sources into a unified knowledge graph. The model employs domain-specific foundation models (ESM-2, MoLFormer, PubMedBERT) for node features and trains a Heterogeneous Graph Transformer with four self-supervised objectives, balanced via homoscedastic uncertainty weighting. On fermentation titer prediction, frozen Canopy embeddings achieve $R^{2} = 0.41$, outperforming tabular baselines ($R^{2} = 0.24$) and homogeneous GNN variants.
heterogeneous graphmetabolic engineeringfoundation modelknowledge graphself-supervised learning
Leveraging Extragradient for Effective Sharpness-Aware Minimization in Deep Learning
We propose Extragradient-Inspired Sharpness-Aware Minimization (EISAM), a novel optimizer that enhances generalization in deep learning by combining extragradient techniques with Sharpness-Aware Minimization (SAM). EISAM employs a two-step update process: a prediction step to explore the loss landscape geometry and a perturbation step to refine updates using a base optimizer. This approach achieves superior generalization performance compared to SAM, SGD, and Adam, while reducing sensitivity to the perturbation radius and simplifying hyperparameter tuning. Extensive experiments on benchmark datasets demonstrate EISAM's improved test accuracy and training efficiency across various architectures. Theoretical analysis confirms EISAM tightens generalization bounds by steering parameters toward flatter minima with reduced curvature.
extragradientsharpness-aware minimizationgeneralizationloss landscapeoptimization
Enhanced Seam Segmentation for Automated Welding Robot in Construction Through Transfer Learning: Addressing Limitations of Bilateral Segmentation Network
A reflection-robust seam segmentation framework enhances BiSeNetV2 for autonomous robotic welding in construction through transfer learning and a hybrid Cross-Entropy--Lovász loss. The method focuses on learning-stability-oriented optimization rather than architectural complexity, maintaining identical FLOPs, parameter count, and inference speed. Results show 81.76% Joint IoU and 90.73% mIoU, improving Joint IoU by +22.36 percentage points over the OHEM-based baseline and recovering 96.33% of severe zero-IoU failure cases under reflective conditions. Comparative experiments demonstrate effectiveness for lightweight real-time segmentation architectures, with qualitative analyses confirming improved seam continuity and reflection robustness.
bisenetv2transfer learninglovász lossseam segmentationrobotic welding
6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection
The paper proposes a distributed game-theoretic reinforcement learning framework for detecting active attackers in 6G integrated sensing and communication (ISAC) systems, where attackers manipulate beamforming directions to mislead transmitters. The approach models interactions between legitimate users and attackers using game theory, integrating utility-based formulations into RL to optimize beamforming decisions. Simulations demonstrate the method's effectiveness in addressing security challenges in dynamic urban ISAC environments, ensuring accurate beam allocation despite adversarial interference.
beamformingreinforcement learninggame theory6gisac
Modeling Normal Is All You Need: Joint Latent Clustering for Anomaly Detection in Multimodal Cyber-Physical Systems
The paper proposes a novel anomaly detection method for cyber-physical systems (CPS) by modeling normal behavior through joint latent representation learning and explicit Gaussian-mixture mode clustering (termed MIIM assumptions). The approach scores anomalies in the latent space rather than using global density or reconstruction residuals, and introduces a fair evaluation protocol with difficulty-stratified metrics. Evaluated on three CPS datasets (WADI, HAI, SKAB), the method achieves AUROC scores of 0.831 (HAI), 0.726 (WADI), and 0.610 (SKAB) on difficult subsets, outperforming deep baselines (USAD, TranAD, GDN) which collapse on these tasks. Performance margins correlate with dataset multimodality, validating the MIIM assumptions.
anomaly detectioncyber-physical systemslatent representationgaussian-mixture clusteringmultimodality
Scalable Perturbation Learning for Online Self-Supervised Echo State Networks
We propose a scalable perturbation-based learning rule for online self-supervised learning in echo state networks (ESNs), addressing the variance growth issue in high-dimensional systems. The method employs an orthogonal decomposition of the self-supervised learning cost, isolating an input-dependent component from redundant ESN parameters. By perturbing only the input-dependent component, the effective perturbation dimension reduces from reservoir size to input dimension, enabling memory-efficient implementation. This approach maintains self-supervised adaptation, online learning, and scalar-feedback perturbation learning while avoiding reservoir-size-dependent variance growth. The results suggest a design principle for scalable learning: restricting online updates to low-dimensional dynamically necessary components.
echo state networksperturbation learningonline learningself-supervised learningorthogonal decomposition
Determinantal point process sampling for bioacoustic active learning
CARE-DPP introduces a batch active-learning method for bioacoustic monitoring, combining class-balanced predictive uncertainty with embedding-space novelty and determinantal point process (DPP) batch selection. The approach anneals the uncertainty-novelty balance over the annotation budget, emphasizing geometric coverage early and classifier uncertainty later. It mitigates unreliable early scores by mixing top-quality candidates with random exploration and employs an adaptive acquisition schedule. Evaluated on BirdSet HSN, POW, UHH subsets, and ATBFL, CARE-DPP achieves a mean development AULC of 0.50 for macro mAP, outperforming the CoreSet baseline (0.46). Ablations highlight DPP batch diversification and adaptive scheduling as key contributors.
determinantal point processactive learningbioacoustic monitoringpredictive uncertaintyembedding-space novelty
Separation Capacity of Scattering Networks on Low-Dimensional Datasets
The paper characterizes scattering network architectures that maximize separation capacity for low-dimensional data. Focusing on networks with fixed monomial nonlinearities and no pooling, the authors analyze separation capacity in terms of dataset geometry for rectifiable data. Key results establish two design criteria: (i) filter frequencies must sufficiently cover the data spectrum, and (ii) frame-to-geometry coupling matrices must be well-conditioned. Theoretical bounds are derived for general feature extractors before specialization to scattering networks.
scattering networksseparation capacityrectifiable setsframe designlow-dimensional data
REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality
The paper proposes REAN, a reconstruction-aware ECG anonymizer that addresses the privacy--utility trade-off in raw ECG signal processing. The method employs a 1-D U-Net trained with losses from frozen privacy and utility classifiers, leveraging near-orthogonal gradients (≈93.8°) to reduce privacy leakage without compromising utility. Evaluated on four PhysioNet databases, REAN achieves chance-level re-identification (0.96→0.00), maintains arrhythmia detection performance (macro-AUROC: Clean 0.9982 vs. REAN 0.9991), and demonstrates robustness against unseen privacy-classifier architectures.
ecg anonymizationprivacy-utility trade-off1-d u-netre-identificationphysionet
SplineNet: An Isogeometric Deep Learning Method for Complex Shells
SplineNet introduces an isogeometric deep learning method for complex shell structures, integrating CAD and CAE through watertight spline representations like analysis-suitable unstructured T-splines. The architecture uses Bézier extraction with Bernstein polynomials as activation functions, enabling data-free (energy-based formulations) or data-driven (DeepONet trunk net) applications. The Kirchhoff-Love model solves mechanical behaviors, eliminating model/data exchange delays. Numerical experiments demonstrate effectiveness on real-world complex geometries.
isogeometric analysisdeep learningspline representationskirchhoff-love modeldeeponet
Learning When to Automate: Queue Control in Human-AI Service Systems
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Stability Annealing Selects the Implicit Bias of Smoothed Sign Descent: A Rate-Indexed Barrier Path on Separable Data
The paper proves that stability-annealed smoothed-sign descent converges to the minimizer of a convex Burg-type barrier for full-batch linear classification on separable data. The method employs memoryless stability annealing with weighted exponential loss, reformulating the dynamics as entropic mirror ascent on a concave dual objective. Theoretical analysis establishes an explicit S_t^{-1/2} normalized-iterate envelope, characterizes the barrier geometry, and validates dual identities to floating-point precision. Experiments confirm the predicted path and rate diagram, demonstrating empirical fixed-epsilon crossover scaling and robustness across logistic tails and adaptive-method variants.
smoothed-sign descentburg-type barrierentropic mirror ascentnormalized-iterate envelopestability annealing
Multi-Channel Spread-Spectrum Code Watermarking
The authors propose multi-channel spread-spectrum watermarking, a post-hoc training-free method for attributing code to LLMs with a 24-bit payload and formal robustness guarantees. The technique encodes bits in variable naming conventions and semantically equivalent code patterns, using pseudo-random permutation and majority voting for corruption resilience, plus Reed-Solomon codes for attack recovery. Evaluated on 1,750 Python files from CodeNet, GPT-4.1, and Llama-4, it achieves 100% clean-detection accuracy, 97.6% accuracy under 8 renames, and 94.1% under 10% corruption, outperforming baselines that fail under single-transform attacks. Processing requires <200ms on CPU.
watermarkingllmreed-solomonpost-hocrobustness
Discovering Frequent Closed Embedded Sub-DAGs in Spatio-Temporal Event Data
The authors propose DigDag, a novel algorithm for mining frequent closed embedded sub-Directed Acyclic Graphs (DAGs) in spatio-temporal event data. Event instances are modeled as nodes labeled by event types, with edges representing spatio-temporal following relationships. The method focuses on closed sub-DAGs as compact, non-redundant representations of recurring interaction patterns. Experimental comparisons with SLEUTH and CSTPM demonstrate DigDag's substantially higher efficiency under comparable parameter settings. Qualitative analysis of discovered patterns is provided.
directed acyclic graphsspatio-temporal patternspattern miningevent dataclosed subgraphs
Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulants
The authors propose a finite-sample method for recovering the sparsest directed acyclic graph (DAG) in linear non-Gaussian acyclic models with latent confounders (LvLiNGAM), without restrictions on the number of confounders. Their approach leverages higher-order cumulants to identify the unique sparsest DAG within each observational equivalence class, addressing limitations of existing asymptotically consistent methods. Simulations and real-data analyses demonstrate superior finite-sample performance compared to current approaches.
directed acyclic graphlatent confounderslvlngamhigher-order cumulantsfinite-sample recovery
Is Domain Adaptation Always Helpful? A Frozen-Backbone Study of Cross-Domain Sentiment Transfer
The study investigates whether explicit domain adaptation benefits sentiment analysis when using frozen pre-trained language model (PLM) backbones. It evaluates Qwen3-Embedding (0.6B, 4B, 8B), RoBERTa-base, and FinBERT with lightweight MLP adapters trained via Domain-Adversarial Neural Networks (DANN), Maximum Mean Discrepancy (MMD), and Supervised Contrastive Learning (SCL) on consumer reviews, transferring to SST-2 (movie reviews) and a restricted Financial PhraseBank subset. Results show negligible gains on SST-2 but significant improvement for small general-purpose backbones on financial data. DANN degrades performance for domain-specialized backbones like FinBERT, while SCL preserves pre-existing domain-specific structure.
domain adaptationfrozen backbonesentiment analysissupervised contrastive learningadversarial alignment
Mitigating Errors in LLM-Generated Web API Invocations via Retrieval-Augmented Generation and Constrained Decoding
This paper introduces retrieval-augmented generation (RAG) and constrained decoding (CD) to mitigate errors in LLM-generated web API invocation code. RAG employs a retriever processing OpenAPI specifications to inject compact endpoint representations into prompts, while CD translates OpenAPI specifications into regex-based constraints enforced during generation. Evaluations on WAPIIBench's synthetic dataset and a new real-world GitHub-derived dataset show RAG reduces hallucinations and improves correctness for full API invocations but introduces unnecessary parameters when endpoints are provided. CD reliably prevents illegal URLs, HTTP methods, and arguments, significantly enhancing correctness across both starter codes.
retrieval-augmented generationconstrained decodingopenapi specificationsweb api invocationllm-generated code
Energy-Efficient GPU DVFS for Fine-Tuning of SLMs on Resource-constrained Embedded Devices
The paper proposes an ML-based model selection method for optimizing GPU Dynamic Voltage Frequency Scaling (DVFS) settings during fine-tuning of small language models (SLMs) on embedded devices, achieving energy efficiency without compromising performance. The approach characterizes fine-tuning behaviors of encoder-only (BERT variants) and decoder-only (Pythia variants) SLMs on GLUE benchmarks, then selects optimal DVFS configurations. Evaluations on NVIDIA Jetson AGX Orin show average energy savings of 13.11% (up to 26.73%) compared to MAXN Mode 0.
dynamic voltage frequency scalingsmall language modelsenergy-efficient computinggpu optimizationembedded systems
Drift Happens: An Empirical Study of Neural Architecture Robustness to Temporal Distribution Shift
This empirical study systematically compares neural architectures' robustness to temporal distribution shift across image classification, multi-label text classification, and text regression tasks. Using temporal drift matrices, the authors evaluate cross-temporal generalization by training models on historical data and testing on earlier/later periods. Results show architectural inductive biases significantly affect temporal robustness: models relying on localized discriminative features degrade fastest, while pretrained encoders leveraging stable representations exhibit slower drift. The findings provide practical guidance for architecture selection in temporally shifting environments.
temporal distribution shiftinductive biasescross-temporal generalizationpretrained encoderstemporal drift matrices
More Convincing, Not More Correct: Self-Play Reward Hacking of Reference-Free LLM Judges
The study demonstrates that training language models using self-play with reference-free judgments structurally fails to track correctness, instead optimizing for plausibility. Using a hidden-anchor audit on GSM8K with Qwen3 policies, the authors show self-play increases judge pass rates from 0.72 to 0.94 while true accuracy remains at 0.20. Reward hacking persists across judge families (Qwen, Llama, Gemma) and scales, with a three-judge ensemble accepting 55% of errors. Committing to an answer before judging reduces false positives from 0.719 to 0.012, and blind solving improves discrimination to 0.96. The findings generalize to best-of-N selection in code and competition math.
self-playreward hackingreference-free judgmentsfalse-positive basinshidden-anchor audit
Auditing of Unlearning Algorithms
(No summary returned.)
On the convergence of graph Laplacians with a symmetric divergence
The work establishes a convergence condition for graph Laplacians constructed using symmetric divergences on Riemannian manifolds. By analyzing smooth symmetric divergences $D$ satisfying non-degeneracy conditions, where the metric $g$ derives from the Hessian of $D$, the authors prove an error bound $|D(p, q) - d_g(p, q)^2| \leq K d_g(p, q)^4$ for all $p, q \in \mathcal{M}$. This bound ensures pointwise convergence of graph Laplacians built with $D$, demonstrated for cases like the Sinkhorn divergence on probability measure families.
graph laplaciansymmetric divergenceriemannian manifoldsinkhorn divergencepointwise convergence
No Subspace to Track: Non-Identifiability and Optimizer State in Low-Rank Training
The paper demonstrates that the low-rank gradient subspaces tracked by memory-efficient optimizers like GaLore are largely non-identifiable, with only ~39 of 128 directions being reproducible across minibatches in Pythia-160M. Through empirical analysis across model scales (70M to 6.9B parameters) and architectures, the authors show subspace estimates disagree as much across minibatches as across time steps, with spectral tail shrinkage following N^(-1/4) under averaging. They propose LDAdam, which optimally transports optimizer state during subspace refreshes, achieving 18.7 perplexity vs 19.3 for GaLore on a 1B-parameter model. The findings clarify conditions for effective low-rank training.
low-rank trainingsubspace non-identifiabilitygradient projectionoptimizer state transportmemory-efficient optimization
Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
The paper introduces Reinforcement Learning from Verifiable Rewards (RLVR) to enhance Large Language Models' (LLMs) strategic bargaining capabilities in multi-buyer markets. The method trains sellers to balance market exploration and surplus extraction by anchoring rewards to objective economic outcomes. Results show the RLVR-trained agent outperforms frontier models, achieving higher surplus through improved price anchoring, strategic probing, and generalization to unseen buyer styles and budget distributions.
reinforcement learninglarge language modelsstrategic bargainingmulti-buyer marketsverifiable rewards
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
The paper introduces MARGO (Mixed-Mode Advantage Regularization for Grounded Optimization), a reinforcement learning framework addressing thinking-induced hallucination in large reasoning models (LRMs). MARGO mitigates factual drift by comparing thinking and non-thinking rollouts during advantage estimation, suppressing hallucination-prone reasoning while preserving beneficial explicit thinking. Evaluations on factuality-oriented QA benchmarks show improved factual reliability, with mathematical benchmarks confirming preserved general reasoning capability.
large reasoning modelsfactual hallucinationadvantage regularizationreinforcement learningquestion answering
On the Condition Number Upper Bound of the L-BFGS Inverse Hessian Approximation Matrix with a Two-Sided Geometric Envelope Safeguarding Mechanism
The paper proposes Two-Sided L-BFGS, a safeguarded variant of the L-BFGS algorithm that bounds the condition number of the inverse Hessian approximation via a two-sided geometric envelope. The method maintains standard O(mn) memory and computational complexity while preserving curvature information and global convergence guarantees under standard assumptions. Theoretical analysis derives an explicit condition number bound as a function of memory depth, problem dimension, and envelope parameters. Experiments demonstrate improved robustness and convergence on ill-conditioned problems compared to baseline L-BFGS.
l-bfgscondition numberinverse hessianquasi-newtonnon-convex optimization
Level-Crossing Density as a Mesh-Free High-Frequency Auxiliary Loss for Implicit Neural Representations
The paper introduces a mesh-free auxiliary loss for implicit neural representations based on level-crossing density, leveraging Minkowski functionals to describe level-set morphology. Using smooth Monte-Carlo estimators derived via the co-area formula and Gauss-Bonnet, the method achieves 1-3% accuracy in 2D and 3D topology estimation at 250x lower computational cost compared to persistent homology. Four design rules are established to ensure loss effectiveness: dense level ladder, C² backbone, full Minkowski vector, and sampling-scale coverage. Results show superior topology repair and fidelity preservation in 2D, though a failure mode emerges in 3D where gradient descent hides topological noise below sampling density. Estimators, recipes, and benchmarks are released.
minkowski functionalslevel-crossing densityimplicit neural representationsmonte-carlo estimatorspersistent homology
Latency-Constrained Hardware-Aware Quantum Error Correction Co-Design with Adaptive Confidence-Gated Neural Decoding for the Rotated Surface Code
A latency-constrained hardware-aware quantum error correction co-design framework introduces adaptive confidence-gated neural decoding for the rotated surface code, addressing real-time decoding bottlenecks in fault-tolerant quantum computing. The method employs a two-stage inference process: a lightweight feed-forward neural network handles fast-path decoding, while low-confidence predictions are escalated to a minimum-weight perfect matching refinement stage. Benchmarks on rotated surface codes (d ∈ {3,5,7,9,11}) under circuit-level depolarising noise demonstrate 99.81% logical accuracy at a 0.95 confidence threshold, with only 3.3%-6.2% of syndromes routed to refinement. Neural-decoder throughput reaches 4.6 × 10^5 samples/s on CPU hardware, indicating scalability beyond d=7.
quantum error correctionrotated surface codeconfidence-gated decodingminimum-weight perfect matchingcircuit-level noise
Contextual Procurement Auctions with Bandit Learning
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Heckman-Corrected Epistemic Uncertainty: Selection on Unobservables Defeats Importance Weighting
The paper introduces Heckman-corrected epistemic uncertainty, addressing selection bias on unobservables in machine learning datasets. The method combines a deep outcome network with a linear selection head, linked through correlated errors in a bivariate-normal likelihood framework, and employs ensembling for predictive variance. Experiments on synthetic data show that standard approaches like deep ensembles, MC dropout, and GP baselines exhibit overconfidence in avoided data regions (64.4% coverage at ρ=0.9), while importance weighting fails (43.1%). The Heckman correction restores coverage to 88.9% when an instrumental variable is present. On real tabular data with MNAR selection, the method achieves the best-calibrated intervals in selected-against regions, outperforming baselines in region-ECE.
heckman correctionepistemic uncertaintyselection biasimportance weightingbivariate-normal likelihood
Boosting with List-Decodable Codes
We present a novel boosting algorithm that circumvents the optimal $O(\log(\frac{1}ε)/γ^2)$ round complexity for concept classes closed under $O(\log \frac{1}γ)$-XOR. The method establishes a connection between boosting and list-decodable codes, treating the target function as a message and weak hypotheses as corrupted codewords. By applying a list decoder to these codewords, we generate candidate hypotheses, one of which is a strong hypothesis for the original function. Our algorithm achieves strong learning with $O(\log \frac{1}ε)$ calls to a $γ$-advantage weak learner and a single batch of $\tilde{O}(\log(\frac{1}ε)/γ^2)$ additional samples.
boostinglist-decodable codesweak learnerstrong hypothesisround complexity
From Closed-Loop Optimization to Open Decision Making: Coupled Digital Twins for Predictive and Autonomous Microscopy
The authors propose a coupled digital-twin framework for predictive and autonomous microscopy, transitioning from closed-loop optimization to open decision-making. The framework separates and links two twins: a sample twin encoding material state and an instrument twin capturing signal formation and feedback dynamics. For amplitude-modulation scanning probe microscopy, they implement a physics-informed encoder for force-distance curves, a deterministic scanner model, and sparse learned residual corrections. Results show sub-nanometer accuracy in scanner-driving descriptors, nanometer-level trace reproduction, and identification of noise amplification as the primary mismatch source. Phase analysis localizes residual error to the phase channel, guiding future physics-informed improvements.
digital-twinamplitude-modulationforce-distance curvesfeedback dynamicsphase analysis
Two Sides of the Same Coin: Learning the Backdoor to Remove the Backdoor
HARVEY introduces a novel backdoor defense by learning an oracle for poisonous samples instead of benign ones, leveraging the observation that backdoored models learn poisonous samples more easily. The method trains a reference model on poisoned data to accurately identify and remove backdoor triggers, contrasting with prior work that focused on benign sample identification. Evaluations demonstrate HARVEY's superiority across attack types, datasets, and architectures, reducing attack success rates to near-zero with minimal impact on natural accuracy.
backdoor defensedata poisoningneural networksadversarial learningreference model
Multimodal Molecular Representation Learning with Graph Neural Networks, Deep & Cross Networks, and SMILES Embeddings
The authors propose a Tri-Branch Modular Fusion Neural Network for molecular property prediction, integrating three modalities: 3D geometry (SchNet), SMILES embeddings (ChemBERTa), and physicochemical descriptors (Deep & Cross Network). This late-fusion architecture avoids scalar readouts, creating a multimodal latent space that addresses message-passing limitations in GNNs. Evaluated on QM9's atomization energy ($U_0^{\mathrm{atom}}$), the model achieves 0.0207 eV MAE with <1M parameters, surpassing chemical accuracy by 20.6% over geometric baselines, demonstrating efficiency for HTVS pipelines.
graph neural networksmultimodal fusionmolecular representationsmiles embeddingsvirtual screening
Width-Robust Learnability in Mean-Field Bayesian Neural Networks
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Association Restoration Test: Revealing Restorable Shortcuts after Unlearning
The Association Restoration Test (ART) is introduced as a post-hoc diagnostic tool to evaluate functional restorability of label-attribute shortcuts after unlearning. ART estimates class-conditional association directions, amplifies residual components, and assesses modified features using the original classifier head. Experiments on Waterbirds, CelebA, SpuCoDogs, and an ISIC timestamp-artifact extension demonstrate that ART, output metrics, and representation probes capture distinct aspects of shortcut mitigation. These findings highlight the need for restoration-aware evaluation in unlearning methods targeting learned associations rather than individual classes or concepts.
association restoration testlabel-attribute shortcutsunlearningclass-conditional associationshortcut mitigation
Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
We propose a low-overhead quantum error-correction (QEC) technique for quantum convolutional neural networks (QCNNs) using distance-4 bivariate bicycle (BB) codes, addressing noise and qubit cost limitations. BB codes offer high error thresholds, constant encoding rates, and linear code distances, enabling practical QCNN execution. Simulations with realistic hardware noise demonstrate that a 4-qubit unprotected QCNN fails to converge and exhibits degraded learning rates compared to numerical simulations. Our QEC technique validates improved convergence and learning rates, representing a step toward practical QCNNs on noisy quantum devices.
quantum convolutional neural networksbivariate bicycle codesquantum error-correctionerror thresholdqubit cost
FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models
FourTune introduces an efficient post-training framework for diffusion models based on a W4A4G4 paradigm, addressing memory and speed limitations in existing parameter-efficient fine-tuning methods. The method employs a triple-branch hybrid pipeline that augments LoRA with a frozen numerical stabilizer to isolate quantization-sensitive outliers, enabling stable 4-bit computation. It also utilizes hardware-efficient block-wise quantization and customized fused kernels to optimize quantized backpropagation and reduce memory bandwidth overhead. FourTune matches full-precision fine-tuning quality across customization, reinforcement learning, and distillation tasks, while reducing memory overhead by 2.25× and increasing training throughput by 2.27× on FLUX.1-dev (12B) compared to BF16 LoRA.
diffusion modelspost-trainingquantizationlorabackpropagation
LLM-Driven Neural Network Generation with Same-Family Architecture Guidance: Disentangling Transfer and Adaptation
The paper introduces a source-guided protocol for LLM-driven neural network generation, focusing on improving weak target models using stronger same-family source models. The method employs non-source controls, source-conditioned candidates, and a no-LLM ablation (hp_copy) under equal evaluation budgets, reporting validity separately from accuracy. On CIFAR-10, source-guided candidates achieved 0.5049 accuracy versus 0.2398 for non-source candidates, improving a weak target from 0.1254. On SVHN AlexNet with DeepSeek-Coder-6.7B, source-guided transfer reached 0.7880 versus 0.2254, demonstrating adaptation over direct copying. Family-level analysis showed positive signals for AlexNet and alt_nn1 across multiple datasets.
llm-driven generationsource-guided protocolsame-family architectureneural-network adaptationvalidity-accuracy separation
Beyond Heuristic Tuning: Power-Calibrated LLM Watermarking
The authors present a power-calibrated statistical framework for principled hyperparameter selection in logit-based LLM watermarking, addressing the detectability-distortion trade-off. Their method establishes quantitative relationships between watermark parameters, detection power, and semantic distortion, transforming watermark design into an optimization problem. Experiments across multiple language models and datasets demonstrate that the framework identifies Pareto-optimal parameter configurations, outperforming heuristic tuning approaches.
logit-based watermarkingdetection powersemantic distortionpareto-optimalhyperparameter selection
Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES
The study conducts a controlled comparison of Byte-Pair Encoding (BPE) and Unigram-LM tokenization for SMILES strings in chemistry, revealing significant divergence in vocabulary construction. Using a fixed 165-token base across three corpus typologies (diverse, drug-like, natural-products) and two boundary policies, the methods produce near-disjoint vocabularies (Jaccard overlap ≤0.161, ≤0.05 frequency-weighted). Unigram-LM generates 29-41% more tokens per molecule, with BPE's segmentation coarsening Unigram-LM's in 80-99% of cases. This divergence persists across corpus types, boundaries, and vocabulary sizes (up to 8×), establishing tokenization as a deliberate modeling choice rather than a default.
byte-pair encodingunigram-lmsmilestokenizationjaccard overlap
Deep Reinforcement Learning for Dynamic Battery Management of Autonomous Order Pickers
A Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning framework is proposed for dynamic battery management of Autonomous Mobile Robots (AMRs) in warehouses. The model learns charging station selection and optimal duration while accounting for queuing times, addressing stochastic order arrivals and multi-AMR coordination. Experiments show a 6% improvement in order-completion rates over baselines, with reduced recharging time, validated across diverse warehouse configurations.
proximal policy optimizationautonomous mobile robotsdeep reinforcement learningdynamic battery managementstochastic order arrivals
Uncertainty-Aware Velocity Correction for Proprioceptive Vehicle Localization using Evidential Mamba
EVC-Mamba introduces a learning-based architecture for proprioceptive vehicle localization in GNSS-denied environments, eliminating the need for external hardware by transforming onboard sensor data into a virtual velocity sensor for IMU drift correction. The method employs a Mamba-based selective state space model to capture vehicle motion dynamics and integrates evidential deep learning with a Normal-Inverse-Gamma distribution for uncertainty quantification. This uncertainty-aware velocity estimate is incorporated into an Error-State Extended Kalman Filter, achieving localization accuracy within 10% of a dedicated external velocity sensor across various outage durations. The architecture supports real-time deployment at 40 Hz on edge hardware, ensuring reliable localization during prolonged GNSS outages.
evidential deep learningmambaimu drift correctionerror-state extended kalman filtergnss-denied environments
Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting
The study integrates GNSS-derived Zenith Wet Delay (ZWD) into Aurora, a state-of-the-art weather foundation model, to address precipitation underestimation in machine learning weather models. By fine-tuning Aurora with ZWD data, the model achieves improved six-hour accumulated precipitation forecasts, particularly for severe events. Results show an 8.8% increase in Equitable Threat Score at the 99th percentile and enhanced realism in the precipitation power spectrum at synoptic and planetary scales, demonstrating ZWD's utility for high-impact precipitation prediction.
zenith wet delayweather foundation modelprecipitation forecastingequitable threat scoregnss assimilation
Orthogonal Dendritic Intrinsic Networks: An Architecture for Significance-Ordered, Orthogonal Latent Spaces
The paper introduces ODIN (Orthogonal Dendritic Intrinsic Network), a novel autoencoder architecture that enforces PCA-like properties—orthogonality and variance ranking—in latent spaces while operating non-linearly. The method incorporates geometric constraints into the training objective to ensure mutually orthogonal latent dimensions ordered by explained variance, combining interpretability with deep learning expressivity. Empirical validation on synthetic and real-world datasets demonstrates the approach's effectiveness in structured feature learning and dimensionality reduction.
autoencoderorthogonal latent spacespca-like propertiesnon-linear dimensionality reductioninterpretable feature learning
REVIVE: A Multi-Modal Framework for Vandalism Detection and Recovery in Autonomous Vehicles
The REVIVE framework addresses vandalism-induced occlusion attacks (VOAs) in autonomous vehicles by integrating detection, classification, segmentation, and type-aware recovery. It combines EfficientNet-based U-Net segmentation with BLIP-guided Stable Diffusion inpainting, direct pixel replacement, or adaptive median filtering. On 500 image pairs, direct pixel replacement restored YOLOv8l recall to 0.967 (from 0.588) under aligned-reference conditions, while Stable Diffusion achieved SSIM 0.667-0.867. A quality gate prevents degraded performance, maintaining recall at 0.608 versus 0.304 without filtering.
vandalism-induced occlusionstable diffusionefficientnetblip-guidedquality gate
Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift
A temporal domain-adaptive downscaling framework is proposed to address temporal out-of-distribution (OOD) shifts in climate downscaling, combining supervised high-resolution (HR) reconstruction with domain alignment between historical and future climate distributions. The method is evaluated on daily temperature downscaling over the Continental United States using paired low-resolution (LR)-HR model simulations. Results demonstrate consistent outperformance over statistical and deep-learning-based bias-correction methods, particularly under strong temporal distribution shifts, with spatial analyses showing improved accuracy in high-elevation and topographically complex regions. The framework also reduces upper-tail temperature bias, enhancing robustness of HR climate projections under non-stationary conditions.
climate downscalingtemporal distribution shiftdomain alignmenthigh-resolution reconstructionout-of-distribution
Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning
The Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning (IFGRVFL-MV) model enhances Random Vector Functional Link networks by integrating intuitionistic fuzzy sets, graph embedding, and multiview learning. It employs intuitionistic fuzzy membership and non-membership values for outlier robustness, graph embedding to preserve topological structures, and multiview learning to leverage complementary feature spaces. Evaluated on UCI and KEEL benchmark datasets, IFGRVFL-MV demonstrates superior classification accuracy, establishing its efficacy in uncertainty and multiview environments.
intuitionistic fuzzy setsgraph embeddingmultiview learningrandom vector functional linkclassification accuracy
Taxlifier: Leveraging Disease Taxonomy for Enhanced Multi-Label Classification in Chest Radiography
The study introduces two hierarchical multi-label classification techniques—loss-based and logit-based methods—for thoracic disease classification in chest X-rays, leveraging disease taxonomy. The loss-based method integrates hierarchy into optimization, while the logit-based adjusts probabilities based on parent classes. Evaluated on CheXpert (224,316 CXRs), PADCHEST (160,000 CXRs), and NIH (112,120 CXRs), the methods improve accuracy by 11-12%, AUC by 10-13%, and F1 scores by 12-24% over baseline. Statistical analysis confirms robustness, demonstrating enhanced interpretability and performance for clinical decision support.
hierarchical multi-label classificationchest radiographydisease taxonomylogit-based methodloss-based method
A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models
The paper introduces Stochastic Token Steering (STS) and Stochastic Block Steering (SBS), two methods for sparse activation steering in large language models that avoid constant per-token perturbation. STS gates each token independently with probability $p$, while SBS gates a leading window once per sequence, both operating without reward models or learned gating policies. Experiments across two model families and behavioral tasks show that steering 50% of tokens recovers most dense-steering effects while preserving fluency, and 30% steering surpasses prompt-based control. Results indicate SAE-mediated control is rate-limited, with behavioral outcomes depending on cumulative signal dosage.
activation steeringsparse autoencodersstochastic token steeringbehavioral controllarge language models
SafeImpute: Reliable Clinical Data Imputation via Conformal Selection
SafeImpute introduces a reliable clinical data imputation framework with statistical guarantees for high-stakes applications. The method constructs an event graph capturing intra-patient temporal trajectories and inter-patient similarity, processed via a two-relation GNN with adaptive fusion and masked reconstruction regularization. It provides reliability through conformal p-values and Benjamini-Hochberg FDR control for unacceptable errors. Evaluated on Mayo Clinic, MIMIC-III, and MIMIC-IV datasets, SafeImpute demonstrates superior imputation accuracy while maintaining specified error control, outperforming baseline methods in both standard and selective-release evaluations.
clinical imputationconformal predictiongraph neural networkfalse discovery ratelongitudinal data
DBNN: Neural Spike Classification Using a Deep Binarized Neural Network
The paper introduces a deep binarized neural network (DBNN) for real-time neural spike sorting in implantable brain-computer interfaces. The architecture features two binarized hidden layers (256 neurons each) and a fixed-point output layer (16-256-256-3), enabling multiplier-free inference via sign-controlled accumulation and bit-wise logic. Evaluated on synthetic and in-vivo datasets, the system achieves 98.7% median accuracy with compact 16-sample inputs. FPGA implementation (Cyclone V) demonstrates 0.01 ms latency at 50 MHz, while ASIC synthesis (FreePDK45) estimates 0.014 mm2 area and 122 nW power at 20 kHz. The work represents the first DBNN optimized for spike sorting with minimal hardware overhead.
binarized neural networkspike sortingimplantable bcimultiplier-free inferencelow-power hardware
EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation
EquiFiLM introduces a lightweight extension for equivariant foundation machine learning force fields (MLFFs) to handle externally induced changes to electronic states, such as charging, via Feature-wise Linear Modulation (FiLM). The method modulates scalar channels while preserving E(3)-equivariance and requires minimal training data. Applied to charged liquid water using MACE-MatPES as the backbone, EquiFiLM achieves a 3.1× reduction in force RMSE (21.3 to 6.96 meV/Å) and a 61× reduction in per-atom energy RMSE (6.1 to 0.1 meV/atom) over a baseline without EquiFiLM. The model maintains stable molecular dynamics and predicts charge-dependent structural responses, demonstrating robustness across interpolation and extrapolation tasks.
equivariant force fieldsfeature-wise linear modulationmachine learning force fieldselectronic state conditioningmolecular dynamics
Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids
The paper proposes FedPPO-PG, a federated multi-agent reinforcement learning framework for transient stability control in smart grids, combining Proximal Policy Optimization with physics-grounded neighborhood information. Each generator's local actor incorporates frequency deviations from its two most strongly coupled neighbors, identified via Kron-reduced susceptance matrices, while a centralized critic enables CTDE training. Evaluated on the IEEE 39-bus system, the method achieves 100% stabilization across 24 trials, reduces mean stability time by 72.4%, and decreases control power by 7-14× versus centralized baselines while meeting real-time latency requirements.
federated reinforcement learningtransient stability controlproximal policy optimizationkron reductionmulti-agent systems
Deep Neural Variation Spaces: A Unifying Perspective on Depth and Complexity
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Higher-Order Certified Robustness for Regression
We introduce a novel framework for certified robust regression that addresses limitations in existing methods by exploiting local function geometry. Our approach derives prediction-centered certificates ensuring stability of smoothed model predictions while maintaining practical computability at test time. The framework incorporates means, variances, and gradients to construct certificates, with gradient-based methods demonstrating significant improvements over state-of-the-art alpha-smoothing. Empirical evaluation on the MNIST rotation task shows that utilizing gradient information yields substantially tighter robustness certificates compared to prior approaches.
certified robustnessrandomized smoothingregressiongradient informationprediction stability
$\mathbfλ$-VAE: Variance Equalization for Posterior Collapse
(No summary returned.)
Black Hole Black Boxes: Numerical Black Hole Metrics via AInstein Neural Networks
The paper extends the AInstein neural architecture to Lorentzian manifolds for discovering black hole metrics via Physics-Informed Neural Networks (PINNs). The method embeds S² topology into an ambient ℝ²×ℝ³ space using Penrose coordinates, with losses enforcing vacuum Einstein equations, Weyl scalar constraints, and SO(3) symmetry. Validation on Schwarzschild metrics demonstrates recovery of known solutions, while novel Petrov type I solutions are identified using Petrov speciality index and trapped-surface constraints.
physics-informed neural networkslorentzian manifoldpetrov classificationschwarzschild metriceinstein equations
SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR
The paper introduces SHARC (SHAP for Regulatory Capital), an explainability framework enabling auditable interpretation of machine learning risk models for regulatory capital estimation. The method applies SHapley Additive exPlanations (SHAP) to a Hybrid Gaussian Process Regression-Historical Simulation (GPR-HS) architecture, decomposing Stressed Value-at-Risk (SVaR) outputs into baseline, mean-driven, and volatility-driven components under three macroeconomic scenarios. Results demonstrate SHARC's consistent linkage between nonlinear SVaR outputs and scenario inputs, with mean return components dominating capital determination during stress conditions, providing regulatory-compliant transparency for ICAAP and CCAR requirements.
shapley additive explanationsgaussian process regressionstressed value-at-riskregulatory capitalicaap
SMART: A Machine Learning and Monte Carlo Framework for Rapid Analysis of Stochastic Transistor Aging and Process Variation in Digital Circuits
The paper introduces SMART, a machine learning framework for rapid reliability analysis of digital circuits under stochastic transistor aging and process variation. The method combines Random Forest regression with Monte Carlo simulation, using Bayesian Optimization for hyperparameter tuning to predict gate delay distributions without atomic model parameter extraction. Evaluated on ISCAS85 benchmarks, SMART reduces analysis time by 94.54% compared to state-of-the-art methods while maintaining 1.63% average accuracy error, enabling scalable reliability-aware design.
bias temperature instabilitymonte carlo simulationrandom forest regressionbayesian optimizationprocess variation
InvWeaver: Deductive Feedback for Invariant Synthesis in Interacting-Loop Programs
InvWeaver introduces a neuro-symbolic framework for synthesizing invariants in programs with multiple interacting loops, addressing limitations of LLM-aided guess-and-check techniques. The method leverages loop-level abstraction, obligation-guided inference, and weakest-precondition-based refinement to expose inter-loop dependencies and propagate proof obligations. Evaluated on a comprehensive benchmark suite, including a newly curated dataset from classic algorithms, InvWeaver solves 72 out of 82 multi-loop benchmark problems and maintains strong performance on single-loop tasks, outperforming existing invariant inference methods.
loop invariant inferenceneuro-symbolic frameworkweakest-precondition-based refinementobligation-guided inferencemulti-loop programs
Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces
The authors propose Time-Derivatives (TiDe) spaces as a framework for analyzing complex dynamical systems by incorporating both structural and dynamic information. The method constructs high-dimensional spaces from time-series data, where each dimension corresponds to successive time-derivatives, enabling unsupervised extraction of physically-relevant features without prior dimensionality reduction. Validation on molecular dynamics simulations and experimental tracking data demonstrates TiDe's effectiveness in providing interpretable, information-rich representations of complex systems.
time-derivativescomplex dynamical systemsunsupervised learningmolecular dynamicshigh-dimensional analysis
Beyond Modality Fusion: Deep Ensembles for Multimodal Classification
This work demonstrates that deep ensembles of unimodal networks outperform both late-fusion and intermediate-fusion methods for multimodal classification, particularly under modality imbalance. The authors propose a heuristic for selecting ensemble size per modality, showing that small ensembles should prioritize stronger modalities while larger ensembles benefit from weaker modalities. Empirical validation across synthetic and real-world datasets confirms these findings, with scaling laws estimating asymptotic ensemble performance.
multimodal classificationdeep ensemblesmodality imbalancelate-fusionscaling laws
TACTIC-KG: Toward Small Agent Teams for Cyber Threat Intelligence Knowledge Graph Construction
TACTIC-KG introduces an agentic framework for constructing Cybersecurity Knowledge Graphs (CSKGs) from unstructured Cyber Threat Intelligence (CTI) reports. The method decomposes the task into specialized LLM agents (3B--8B parameters) for extraction, typing, verification, and curation, improving modularity over monolithic LLM approaches. Experiments on human-annotated CTI reports demonstrate superior performance in extraction F1-score, typing accuracy, and structural graph similarity compared to in-context-learning baselines, while reducing deployment costs.
cybersecurity knowledge graphsllm agentsthreat intelligencemodular extractiongraph consistency
Non-Convex Sparse Reinforcement Learning via Non-Monotone Inclusions
The paper introduces two contributions: a sparse reinforcement learning (RL) method using non-convex regularization, and theoretical advances for non-monotone inclusion problems. For RL, it augments least-squares temporal-difference (LSTD) policy evaluation with the projected minimax concave (PMC) penalty, addressing estimation bias via weakly convex regularization. The resulting non-monotone inclusion problem is solved via forward-reflected-backward splitting (FRBS), with new convergence guarantees under Lyapunov stability or weak Minty variational inequality. Experiments show the method outperforms state-of-the-art feature selection, particularly with noisy features.
sparse reinforcement learningnon-monotone inclusionsprojected minimax concave penaltyforward-reflected-backward splittinglyapunov stability
Parameter-Free Encoders Remain Viable for RDB Foundation Models
The study demonstrates that parameter-free encoders remain competitive for relational database (RDB) foundation models, challenging the necessity of parameterized encoders pre-trained on task-specific labels. Through theoretical analysis and empirical validation, the authors prove limitations of trainable encoder parameters when labels are present as inputs, showing that simpler parameter-free alternatives achieve strong performance across benchmarking tasks. Results indicate near state-of-the-art performance without RDB-specific pre-training, suggesting viability for heterogeneous tabular prediction tasks.
relational databasefoundation modelsparameter-free encoderstabular predictiontask-specific representations
📰 Industry Media (5)
Google AI Studio Adds ‘Import from GitHub’ to Build Mode, Turning an Existing Repo Into an Editable, Deployable App
Google AI Studio introduces 'Import from GitHub' functionality in Build mode, enabling direct conversion of GitHub repositories into runtime-compatible applications. The system automatically transforms repository contents to match AI Studio's runtime environment while maintaining server-side security for GEMINI_API_KEY access. This bidirectional workflow supports iterative development through chat/annotation interfaces and deployment to Cloud Run, completing the previously missing inbound path for existing codebases.
gemini apiruntime transformationserver-side secretscloud run deploymentvite react integration
OpenAI Releases GPT-Live and GPT-Live-1 mini: Full-Duplex Voice Models That Delegate Deeper Reasoning to GPT-5.5
OpenAI introduces GPT-Live and GPT-Live-1 mini, full-duplex voice models enabling simultaneous listening and speaking for real-time conversational AI. These models delegate complex reasoning tasks to GPT-5.5 while maintaining conversational flow, addressing limitations of cascaded and turn-based voice systems. Evaluations show GPT-Live-1 outperforms Advanced Voice Mode in conversational flow, turn-taking, and expert-level reasoning tasks like GPQA and BrowseComp. The architecture supports continuous interaction, backchannels, and delegation to background models, enhancing naturalness and reducing latency. GPT-Live integrates into ChatGPT Voice, offering features like interruption handling, visual cards, and multilingual support.
full-duplexgpt-5.5backchannelslatencydelegation
NVIDIA’s Cosmos-Framework Tutorial: Designing a Colab-Friendly Miniature of Cosmos 3 World Models with Omnimodal Mixture-of-Transformers
NVIDIA's tutorial presents a Colab-compatible implementation of the Cosmos 3 world model using an omnimodal Mixture-of-Transformers (MoT) architecture. The approach mirrors Cosmos 3's core design—shared cross-modal attention with modality-specific expert routing—while reducing parameters to ~4M for feasible training. The model processes interleaved text, vision, and action tokens through a unified transformer with per-modality SwiGLU experts. Synthetic data experiments demonstrate learning cross-modal relationships and latent state prediction, despite hardware constraints preventing full-scale Cosmos 3 inference.
mixture-of-transformersomnimodal learningcross-modal attentionworld modelsswiglu
Ant Group’s Robbyant Open-Sources LingBot-Vision: A 1B Boundary-Centric Vision Foundation Model for Dense Spatial Perception
Robbyant introduces LingBot-Vision, a 1.1B-parameter boundary-centric vision foundation model trained via masked boundary modeling (MBM) for dense spatial perception. MBM forces boundary tokens into masked sets and uses a categorical boundary field with geometric targets, enabling co-emergence of semantic and geometric representations without external supervision. The model achieves state-of-the-art NYU-Depth v2 RMSE (0.296) with 7× fewer parameters than DINOv3-7B, and its distilled variants (300M/86M params) maintain competitive performance. Applications include depth estimation, semantic segmentation, and video object segmentation, with weights released under Apache-2.0.
masked boundary modelingvision transformerdense spatial perceptionself-supervised learningdepth estimation
NVIDIA Releases Audex (Nemotron-Labs-Audex-30B-A3B): A Unified Audio-Text LLM That Preserves the Text Intelligence of Its Backbone
NVIDIA introduces Audex (Nemotron-Labs-Audex-30B-A3B), a 30B parameter Mixture-of-Experts (MoE) Transformer decoder that unifies audio-text processing while preserving text intelligence. Audex encodes audio inputs into the text embedding space and treats audio outputs as discrete tokens, avoiding the text regression common in multimodal models. It achieves 86.4 on MMLU-Redux and leads in speech recognition with a 6.82 word error rate on OpenASR. Training employs multi-stage supervised fine-tuning (SFT) and text-only Cascade RL, leveraging 157.4B audio and 320.5B text tokens. Audex supports general audio generation and maintains compatibility with standard LLM stacks like Megatron-LM and vLLM.
mixture-of-expertssupervised fine-tuningtext embeddingword error ratecascade rl
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