AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchAutoHGNN: Robust and Efficient Neural Architecture Search for Hypergraph Neural Networks
arXiv:2609.33392v1 Announce Type: cross Abstract: Hypergraph neural networks have achieved significant success in recent years. However, manual architecture crafting is labor-intensive and often fails to capture complex,…
- arXiv cs.AIagentsEvaluating System One Models for Agent Security Decisions: Reliability, Calibration, and Selective Automation
arXiv:2609.33401v3 Announce Type: replace-cross Abstract: Agentic software connects language models to tools that modify files and interact with external services. Developers use model-based security checks to screen ext…
- arXiv cs.AIagentsDataMagic: Authoring Data Videos through Declarative Multi-Agent Orchestration
arXiv:2609.33403v1 Announce Type: cross Abstract: Data videos communicate data insights through dynamic charts, voice narration, and synchronized animations, and have become a widely adopted form of data storytelling. Ho…
- arXiv cs.AIresearchDecoupling Token Roles in Autoregressive Pretraining
arXiv:2609.33405v1 Announce Type: cross Abstract: Autoregressive pretraining increasingly draws on heterogeneous data, making it important to understand how a model learns from an individual token. The next-token predict…
- arXiv cs.AIresearchResolving State-Representation Mismatch: State-Space Visual Reasoning for Open-Loop VLA Planning
arXiv:2609.33412v1 Announce Type: cross Abstract: Despite rapid progress in vision-language-action (VLA) models, existing reasoning paradigms still face a fundamental \emph{state-representation mismatch} in open-loop pla…
- arXiv cs.AIresearchTTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models
arXiv:2609.33414v1 Announce Type: cross Abstract: Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual conditions can reinf…
- arXiv cs.AIresearchInvestigating the Effect of k-NN Preprocessing on Developing Graph Neural Networks: A Fairness-Based Perspective
arXiv:2609.33416v1 Announce Type: cross Abstract: In this paper, a methodology to design fair graph convolutional neural networks (GCNs) is developed and tested over several application data sets. The graphs that are use…
- arXiv cs.AIresearchExplainable Deep Learning of Resting-State Functional Connectomes Reveals Network Biomarkers of Adolescent Intelligence
arXiv:2609.33422v1 Announce Type: cross Abstract: Mapping resting-state brain organization to individual differences in cognitive ability remains a major challenge in population neuroinformatics. Although deep learning e…
- arXiv cs.AIresearchA Light Bilevel Refinement Aligns Self-Supervised Representations for Stronger Task-Specific Learning
arXiv:2609.33424v1 Announce Type: cross Abstract: Self-supervised pretraining learns representations that are broadly transferable across downstream tasks, yet direct fine-tuning can be suboptimal due to misalignment bet…
- arXiv cs.AIagentsGraph-Guided Repository Environment Construction
arXiv:2609.33429v1 Announce Type: cross Abstract: Coding agents now increasingly rely on execution to validate their solutions, making the construction of reliable execution environments a critical enabling capability. H…
- arXiv cs.AIagentsHESP: Separating What to Probe from When to Stop in Local LLM Alert-Triage Agents
arXiv:2609.33446v1 Announce Type: cross Abstract: Security operations centers receive far more alerts than analysts can investigate, and organizations that cannot send their telemetry to hosted models must automate triag…
- arXiv cs.AIresearchProtected Cores Are Not Enough: Certifying AI-Proposed Revisions of Temporal Specifications
arXiv:2609.33461v1 Announce Type: cross Abstract: Runtime monitoring traditionally evaluates a specification that is fixed before execution or externally modified when requirements change. In learning-enabled and data-in…
- arXiv cs.AIresearchA Cheap Verifier is Good Enough: LLM Post-training is Robust to Erroneous Rewards
arXiv:2609.33467v1 Announce Type: cross Abstract: When post-training large language models on tasks with semi-verifiable rewards, there are many factors (training steps, base model size, training order, data quality, ver…
- arXiv cs.AIresearchChameleon: Dynamic Format Adapter for Efficient Diffusion
arXiv:2609.33496v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is the standard way to run modern diffusion models on memory-constrained accelerators, yet every existing diffusion PTQ scheme fixes the…
- arXiv cs.AIresearchSource Anchoring for Physical Consistency in Flow Matching Models
arXiv:2609.33510v1 Announce Type: cross Abstract: Deep generative models are used to solve partial differential equations and model distributions of physical system states, but ensuring that the generated samples satisfy…
- arXiv cs.AIresearchE-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding
arXiv:2609.33530v1 Announce Type: cross Abstract: Natural Language Understanding (NLU) plays a crucial role in various applications, yet its performance suffers from weaknesses in handling the complexities of human langu…
- arXiv cs.AIresearchTerMeZO: Ternary Sparse Zeroth-Order Optimization for Fine-tuning BitNet Models at the Edge
arXiv:2609.33548v1 Announce Type: cross Abstract: Fine-tuning anguage models (LLMs) with first-order optimizers requires a memory several times larger than that required for inference. Memory-efficient zeroth-order optim…
- arXiv cs.AIresearchOOD Generalization as a Bifurcation Problem
arXiv:2609.33562v1 Announce Type: cross Abstract: Systematic out-of-distribution (OOD) generation remains a critical bottleneck for continuous-time generative models. While standard joint classifier-free guidance (CFG) r…
- arXiv cs.AIagentsMA-JEPA: Joint-Embedding World Models for Multi-Agent Reinforcement Learning
arXiv:2609.33563v2 Announce Type: replace-cross Abstract: World models improve sample efficiency by training policies on imagined trajectories, but their usefulness depends on learning representations that capture the in…
- arXiv cs.AIresearchCompressing Value Predictions for Learning-Augmented Metrical Task Systems
arXiv:2609.33580v1 Announce Type: cross Abstract: Learning-augmented algorithms for metrical task systems (MTS) can exploit predictions of canonical dual values, but existing formulations typically require a prediction f…
- arXiv cs.AIresearchViCoR: Reliable Molecular Structure Extraction via Spatially Aligned Verification and Executable Revision
arXiv:2609.33603v1 Announce Type: cross Abstract: Reliable optical chemical structure recognition (OCSR) is essential for building high-quality chemical data from scientific literature, yet even small recognition errors…
- arXiv cs.AIresearchLearning Transferable Reaction Mechanisms from Visual Chemical Knowledge
arXiv:2609.33608v1 Announce Type: cross Abstract: Reaction mechanisms describe the step-by-step transformations underlying chemical reactions and are central to reaction analysis and synthesis. Learning-based models have…
- arXiv cs.AIresearchQuizzing the Translation: A Prover-Grounded Evaluation Metric for NL$\rightarrow$FOL
arXiv:2609.33612v1 Announce Type: cross Abstract: A standard pipeline for symbolic reasoning over natural-language problems translates them into first-order logic and invokes a theorem prover. The translation step is the…
- arXiv cs.AIresearchSpatialSpeak: QA-Native Reconstruction with Local and Global Context for Spatial Chain-of-Thought Reasoning
arXiv:2609.33616v1 Announce Type: cross Abstract: Vision-language models (VLMs) can benefit from geometric priors for multi-view spatial reasoning, yet answer-only training does not directly supervise the intermediate ge…
- arXiv cs.AIresearchLearning Dynamics of Continual Learning: A Unified View of Data Attribution, Forgetting, and Plasticity Loss
arXiv:2609.33620v1 Announce Type: cross Abstract: Modern language models are likely to be updated throughout their lifetime rather than trained once and frozen. Each update therefore participates in a recurring cycle: de…
- arXiv cs.AIagentsStoryEngine: A State-Grounded Agentic Framework for Video Storytelling
arXiv:2609.33627v1 Announce Type: cross Abstract: Despite recent progress in agentic multi-shot video generation, producing coherent and consistent long-form stories remains challenging. Existing agentic pipelines typica…
- arXiv cs.AIresearchSafety Reconstructed: Generative Modeling via Masked Diffusion Builds Strong Safety Guardrails
arXiv:2609.33634v1 Announce Type: cross Abstract: Guard models are the last line of defense between a language model and a harmful output, yet their training objective is surprisingly narrow. Existing guards learn to pre…
- arXiv cs.AIresearchLearning to Learn from Context: Synthetic Training from Perturbed Public Documents
arXiv:2609.33642v1 Announce Type: cross Abstract: Real-world tasks often require large language models (LLMs) to learn from complex task-specific context rather than pretrained parametric knowledge. This capability remai…
- arXiv cs.AIresearchFrom Distributions to Stochastic Processes: Neural Approximation of Measure-Valued Maps
arXiv:2609.33649v1 Announce Type: cross Abstract: Learning mappings between probability distributions arises naturally when inputs and outputs are represented by populations of samples rather than individual observations…
- arXiv cs.AIresearchTsubame: Tree Replay for Diffusion-Based Speculative Decoding
arXiv:2609.33652v1 Announce Type: cross Abstract: Context-aware dynamic trees allocate the speculative decoding budget according to draft path probabilities, adapting their depth and branching to the current context. Und…
- arXiv cs.AIresearchDemonstration-Free Success-Probability Reward Learning for Generalist Robot Policies
arXiv:2609.33653v1 Announce Type: cross Abstract: Reinforcement learning (RL) enables generalist robot policies to improve through trial-and-error interaction, yet its effectiveness is fundamentally constrained by sparse…
- arXiv cs.AIresearchWhen Noise Meets Long-Tail: Feature-Threshold Dual Calibration for Robust Pseudo-Labeling
arXiv:2609.33668v1 Announce Type: cross Abstract: Pseudo-labeling has become a cornerstone of learning from unlabeled data in semantic segmentation. Yet its effectiveness drops sharply in real-world scenarios where stron…
- arXiv cs.AIresearchDynamic Kuramoto-Hodge Operators for PDEs on Complex Geometries and Topologies
arXiv:2609.33693v1 Announce Type: cross Abstract: Learning PDE operators on complex domains requires capturing interactions among fields on vertices, edges, and faces, alongside global responses shaped by topology. Exist…
- arXiv cs.AIresearchUnderstanding Confabulation and Rethinking Reconstruction in Activation Explanations
arXiv:2609.33702v1 Announce Type: cross Abstract: Natural Language Autoencoders (NLAs) produce unsupervised text explanations of a model's activations: a verbalizer describes an activation and a reconstructor learns to r…
- arXiv cs.AIresearchFrom Granular Revision Operations to Meaningful Revision Units: Evaluating LLMs for Revision Boundary Detection
arXiv:2609.33720v1 Announce Type: cross Abstract: Revision traces provide valuable evidence about students' writing processes, but their usefulness for learning analytics depends on how individual revisions are represent…
- arXiv cs.AIresearchBOReFT: Manifold Steering of Language Models for Black-box Optimization
arXiv:2609.33722v1 Announce Type: cross Abstract: Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typically improve propos…
- arXiv cs.AIresearchCollaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos
arXiv:2609.33754v2 Announce Type: replace-cross Abstract: Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by privacy. This is acute in financial fr…
- arXiv cs.AIresearchTransformer-based Neural Beamforming for Real-Time Speech Enhancement on Smart Low-Power Hearable Devices
arXiv:2609.33755v1 Announce Type: cross Abstract: Accurate, efficient, and low-latency spatial beamforming is a key component in emerging smart hearable devices, enhancing speech while suppressing noise and interference.…
- arXiv cs.AIagentsSecProbe: Adaptive Evaluation of Coding Agents on Cybersecurity Vulnerabilities
arXiv:2609.33763v1 Announce Type: cross Abstract: Assessing cybersecurity vulnerability awareness in coding agents requires evaluations that reveal capability gaps and remain informative as models evolve. Static benchmar…
- arXiv cs.AIresearchBeyond Fixed Features: Architecture-Dependent Sensitivity to Node Representations under Heterophily
arXiv:2609.33764v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) perform well on homophilic graphs but struggle in heterophilic settings, where connected nodes often carry dissimilar labels. Existing evalua…
- arXiv cs.AIagentsDEALS: Decentralized Expertise-Aware Load Serving for Multi-Agent LLM Systems
arXiv:2609.33768v1 Announce Type: cross Abstract: Multi-agent systems (MAS) have recently emerged as an effective approach for coordinating large language model (LLM)-based agents to solve complex tasks through structure…
- arXiv cs.AIresearchSelecting Diverse SFT Traces Improves Post-RL Generalization
arXiv:2609.33780v1 Announce Type: cross Abstract: Verified solutions are not equally useful for preparing reasoning models for reinforcement learning (RL). We present a comprehensive study of route diversity, the variati…
- arXiv cs.AIresearchSurprising Success, Repeated Failure: Entropy-Guided Credit Assignment for Exploration in LLM Reasoning
arXiv:2609.33781v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) enhances reasoning in large language models (LLMs) through outcome-level feedback, yet recent approaches to finer-gr…
- arXiv cs.AIresearchDo We Really Need KL Divergence for On-Policy Distillation of Large Language Models?
arXiv:2609.33791v1 Announce Type: cross Abstract: Since the advent of knowledge distillation, KL divergence has been the standard loss in distillation. Recently, on-policy distillation (OPD) has emerged as an efficient p…
- arXiv cs.AIresearchDiffusion Reward Models
arXiv:2609.33803v2 Announce Type: replace-cross Abstract: Reward models underpin the alignment of large language models, yet the dominant designs reduce each prompt--response pair to a point estimate or to a distribution…
- arXiv cs.AIresearchMinkowskiPE: Minkowski Positional Encoding for Spatiotemporal Perception
arXiv:2609.33804v1 Announce Type: cross Abstract: Modeling spatiotemporal coupling is a key challenge in building physical intelligence across scales, from microscopic to macroscopic. Existing models capture such structu…
- arXiv cs.AIagentsCodeActionBench: Evaluating Agentic Code-as-Policy for Embodied Manipulation
arXiv:2609.33807v1 Announce Type: cross Abstract: How well can general-purpose multimodal models turn visual understanding and reasoning into embodied manipulation via executable code? We introduce CodeActionBench, a ben…
- arXiv cs.AIresearchAugmenting Visual Anomaly Detection with Automated Interpretability
arXiv:2609.33818v1 Announce Type: cross Abstract: Visual anomaly detectors identify deviations from known-normal data, but their anomaly signals may mix evidence of actual anomalies with benign visual variation. We inves…
- arXiv cs.AIresearchAchieve What You Imagined: Learning to Align Actions with Visual Plans
arXiv:2609.33832v1 Announce Type: cross Abstract: World-action models can jointly predict future visual observations and robot actions. However, discrepancies may exist between their visual predictions and the consequenc…
- arXiv cs.AIresearchAn Active-Bottleneck Mechanism for Weak-to-Strong Generalization
arXiv:2609.33835v1 Announce Type: cross Abstract: Weak-to-strong generalization (W2SG) occurs when a student trained on a teacher's predictions outperforms that teacher. We study when this happens under fully converged,…