AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchProgram-Verified Self-Evolution for Vision-Language Models
arXiv:2609.33855v1 Announce Type: cross Abstract: Self-evolving vision-language models train on questions they generate from unlabeled images. Since these questions have no gold answers, prior methods label them by major…
- arXiv cs.AIresearchPI-NOMT: Physics-Informed Neural Optimal Mass Transport for Brain Fluid Dynamics
arXiv:2609.33857v1 Announce Type: cross Abstract: Recovering hidden transport mechanisms from sparse spatiotemporal observations is a fundamental inverse problem in scientific machine learning. In brain tracer imaging, d…
- arXiv cs.AIresearchRobot-GST: geometry-aware spatial-temporal robot policy representation and evaluation
arXiv:2609.33872v1 Announce Type: cross Abstract: Robotic manipulation policies are advancing rapidly with increasing reliance on vision-language models for end-to-end decision making. However, reliable deployment remain…
- arXiv cs.AIagentsCounterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents
arXiv:2609.33875v3 Announce Type: replace-cross Abstract: Outcome-only reinforcement learning gives software engineering (SWE) agents a terminal success signal but little direct guidance about intermediate decisions. We…
- arXiv cs.AIresearchFinite Probes Suffice: Identifiability and Universality for Weight-Space Learning
arXiv:2609.33901v2 Announce Type: replace-cross Abstract: Learning properties of neural networks has recently attracted growing interest, with existing approaches operating either directly on network parameters or throug…
- arXiv cs.AIresearchJIVE: Jacobian-Informed Volume Expansion for Diverse Generative Sampling
arXiv:2609.33906v1 Announce Type: cross Abstract: Generative models often suffer from mode collapse and limited sample diversity. While prior works attempt to mitigate this by jointly generating a batch of samples and re…
- arXiv cs.AIresearchValidating Memory-Optimal Transformer Kernels on Real Hardware: From Formal Derivation to Measured Performance Across Two HPC Clusters
arXiv:2609.33916v1 Announce Type: cross Abstract: We validate memory-optimal cost functions for transformer kernels derived via the Mathematics of Arrays (MoA). Companion Papers I-IV formally derive kernels for attention…
- arXiv cs.AIresearchQuantization Error Is Spectrally Flat: A Single Random Probe Is a Calibrated, Data-Free Sensitivity Estimator, with Application to Budget-Targeted Mixed-Precision Quantization
arXiv:2609.33923v2 Announce Type: replace-cross Abstract: A single random Gaussian probe gives an unbiased estimate of the squared Frobenius norm of a layer's quantization error. The estimator is well-behaved because rou…
- arXiv cs.AIresearchDiffusion-Based Rollouts as a Stabilization Mechanism for Long-Horizon Environmental Forecasting
arXiv:2609.33930v1 Announce Type: cross Abstract: Extending forecast lead times while maintaining predictive skill remains a major challenge in environmental forecasting. We investigate diffusion-based rollouts as a stab…
- arXiv cs.AIresearchUnifying Video Tasks via Spatiotemporal Analogy
arXiv:2609.33935v2 Announce Type: replace-cross Abstract: Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free alternative by specif…
- arXiv cs.AIresearchEEG-Fusion: Failure-Informed Source-Free Expert Routing for Robust Motor Imagery EEG Decoding
arXiv:2609.33962v1 Announce Type: cross Abstract: Subject-independent motor-imagery (MI) EEG decoding can exhibit subject-level failures even when average performance appears acceptable: under subject shift, a decoder ca…
- arXiv cs.AIresearchThinkNet: Evolutionary Deep Ensembles for Efficient Motor-Imagery EEG Classification
arXiv:2609.33967v2 Announce Type: replace-cross Abstract: Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users und…
- arXiv cs.AIresearchOn the Token Value Inequality in Efficient Reasoning
arXiv:2609.33970v1 Announce Type: cross Abstract: Chain-of-Thought reasoning has enabled large language models to achieve substantial performance gains on complex tasks. However, these gains come at the cost of dramatica…
- arXiv cs.AIresearchHARMONIA: Interpretable Graph Learning through Mixtures of Neural Bases
arXiv:2609.33972v1 Announce Type: cross Abstract: Existing interpretable graph additive models still face limitations in either computational scalability or modeling flexibility. In terms of structural modeling, previous…
- arXiv cs.AIresearchGroupMask: Layer-Adaptive Group-wise Sparsity for Semi-Structured LLM Pruning
arXiv:2609.33977v1 Announce Type: cross Abstract: Semi-structured pruning compresses large language models (LLMs) while keeping a regular sparse structure, but the prevailing N:M pattern fixes the same local sparsity rat…
- arXiv cs.AIresearchA packet-level digital hardware twin for commissioning megahertz diagnostic edge AI and plasma control system integration in tokamaks
arXiv:2609.33994v1 Announce Type: cross Abstract: High-bandwidth plasma diagnostics increasingly provide inputs to machine learning and signal-processing algorithms intended for real-time tokamak control, but the complet…
- arXiv cs.AIresearchWhen Known Physics Helps Neural PDE Models: Residual Constraints Out-Regularize Generic Priors for Nonlinear Dynamics
arXiv:2609.34012v1 Announce Type: cross Abstract: Neural PDE surrogates increasingly incorporate structural priors, yet it is often unclear whether their gains arise from physics-specific information or simply from regul…
- arXiv cs.AIagentsMaat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflows
arXiv:2609.34017v1 Announce Type: cross Abstract: Large-language-model multi-agent systems (LLM-MAS) introduce a characteristic reliability problem: an error produced by one agent can be accepted as context by downstream…
- arXiv cs.AIresearchSR4-Fit: A Unified Interpretable Rule-Based Machine Learning Framework for Informative and Trustworthy Decision-Making
arXiv:2609.34019v1 Announce Type: cross Abstract: In many high-stakes applications, machine learning is dominated by black-box models that require post hoc explanations to justify their predictions. These explanations ar…
- arXiv cs.AIresearchUncovering shortcut learning in audio classifiers by discovering recurring concepts in temporal explanations
arXiv:2609.34030v1 Announce Type: cross Abstract: Correlations between events in machine learning datasets may result in shortcut learning, where models learn to predict the target event based on the presence of a correl…
- arXiv cs.AIresearchADPTNet: Adaptive with Prescriptive Timescales Non-Linear SSM for Sequence Modelling
arXiv:2609.34034v1 Announce Type: cross Abstract: A central aim of neuromorphic computing is to provide a viable alternative to highly energy-intensive Transformer-based AI. However, efficient alternatives struggle to ca…
- arXiv cs.AIagentsUOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents
arXiv:2609.34036v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a critical decision ste…
- arXiv cs.AIresearchARCH-B: Architectural Representation, Comprehension and Hierarchy Benchmark
arXiv:2609.34047v1 Announce Type: cross Abstract: Multimodal models increasingly interpret visual environments, but their ability to recognize the same building across photographs, floor plans, elevations, sections, and…
- arXiv cs.AIresearchDo World Models Learn Global Understanding?
arXiv:2609.34058v1 Announce Type: cross Abstract: AI systems often feel brittle and fragmented. A large language model (LLM) may correctly explain a concept but fail to apply it, or follow safety instructions in one cont…
- arXiv cs.AIagentsLearning Perturbation Robust Policies for LLM Agents with Stable Optimization
arXiv:2609.34064v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become an effective post-training paradigm for long-horizon large language model (LLM) agents. However, we find that the resulting policie…
- arXiv cs.AIresearchWho Gets a Token, and What Does It Carry? Unequal Name Support and Concept Access in Large Language Models
arXiv:2609.34065v1 Announce Type: cross Abstract: Names are personal identifiers, but they also carry social meaning and are widely used to evaluate how language models treat different people. Such evaluations typically…
- arXiv cs.AIresearchFLARE: Flow Matching with Local Axis-Angle Representations for Stochastic Micromagnetic Evolution
arXiv:2609.34070v1 Announce Type: cross Abstract: Long-horizon micromagnetic simulation remains expensive because conventional and learned solvers typically propagate Landau--Lifshitz--Gilbert (LLG) dynamics step by step…
- arXiv cs.AIresearchMaskCoFT: Masked Co-Adaptive Fine-Tuning for Memory-Efficient MoE Inference
arXiv:2609.34077v2 Announce Type: replace-cross Abstract: Mixture-of-experts (MoE) language models often exceed the memory of a single GPU. Expert offloading keeps most experts in host memory and loads them on demand, so…
- arXiv cs.AIresearchWhiteCon: Semi-Supervised Domain Adaptation Regression Through Whitening Transform and Dual Consistency
arXiv:2609.34078v1 Announce Type: cross Abstract: Domain adaptation is crucial for addressing distributional shifts that degrade model performance across domains. While most existing research has centered on classificati…
- arXiv cs.AIagentsAD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving
arXiv:2609.34085v1 Announce Type: cross Abstract: Autonomous driving requires \textit{world models} that can understand the physical world, reason and plan, and operate safely. In this paper, we first systematically eval…
- arXiv cs.AIresearchTRACE: Expert-Aligned ECG Representation Learning with Rigorous Benchmarking and Real-World Validation in Acute Cardiac Care
arXiv:2609.34088v1 Announce Type: cross Abstract: TRACE (Text-Reinforced Analysis of Cardio ECGs) is a multimodal electrocardiogram (ECG) representation model that learns clinically grounded signal embeddings for downstr…
- arXiv cs.AIresearchThe Devil is in the Spectrum Bias: Spectrum-Balanced Feature Matching for Robust Representation Distillation
arXiv:2609.34106v1 Announce Type: cross Abstract: Large visual foundation models have demonstrated remarkable transferability across a wide range of downstream tasks. To deploy such models efficiently, feature matching h…
- arXiv cs.AIresearchUnknown is not normal: separating language-model extraction from rule-based decision logic for clinical risk scores
arXiv:2609.34112v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to compute clinical risk scores from free-text notes. Notes are often incomplete, and treating undocumented findings as…
- arXiv cs.AIresearchSlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving
arXiv:2609.34117v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models activate few experts per token, yet batched decoding can access nearly the entire expert pool, making expert-weight traffic a major bottle…
- arXiv cs.AIresearchProbabilistic electrical power demand forecasting with uncertainty quantification
arXiv:2609.34120v1 Announce Type: cross Abstract: The majority of research on electricity consumption forecasting has focused on deterministic approaches, which generate a single point estimate for each time step in the…
- arXiv cs.AIagentsJET: Judge-Guided Evolution at Test Time for Agent Programs
arXiv:2609.34126v1 Announce Type: cross Abstract: An agent's executable program governs how it uses tools, processes observations, and responds to failures. Evolving this program at test time can help adaptation, but dec…
- arXiv cs.AIresearchSTITCH-RAG: Spatio-Temporal Influence Tracing over Topic Hypergraphs for Multi-Hop Retrieval-Augmented Generation
arXiv:2609.34127v1 Announce Type: cross Abstract: Multi-hop retrieval-augmented generation requires a retriever to connect evidence distributed across documents while preserving a concise, faithful generation context. Ex…
- arXiv cs.AIresearchPrefLUT: Reusable and Refinable Personalized Color Editing from Pairwise Preferences
arXiv:2609.34133v1 Announce Type: cross Abstract: Photographic color editing is inherently personal: the same image can appear too warm, too muted, or already satisfactory to different users. Most lookup table (LUT) and…
- arXiv cs.AIresearchToward a Graded Measure of Belief Stability in Large Language Models
arXiv:2609.34158v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly mediate how people access and reason with information, yet factual reliability is usually evaluated one judgment at a time. We i…
- arXiv cs.AIresearchWorldGraph: Graph-Native World Modeling
arXiv:2609.34159v1 Announce Type: cross Abstract: World models infer latent states of an environment to capture its underlying dynamics and predict future evolution. Many real-world environments, however, are inherently…
- arXiv cs.AIresearchGradLev: Token-Parallel Test-Time Training Via Costate Prediction
arXiv:2609.34174v2 Announce Type: replace-cross Abstract: Test-time training (TTT) allows a model to improve its predictions at inference time by updating weights after every observed token. However, sequential gra- dien…
- arXiv cs.AIresearchUnified Visual-Tactile-Action Modeling from Human Demonstrations for Dexterous Manipulation
arXiv:2609.34182v2 Announce Type: replace-cross Abstract: Dexterous manipulation requires tactile feedback. However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleoperation provides limit…
- arXiv cs.AIresearchEntroPack: Fast and Accurate Entropy-Coded Weight Compression at Arbitrary Bitrates
arXiv:2609.34185v1 Announce Type: cross Abstract: Weight compression helps large neural networks fit deployment memory budgets, but common fixed-width formats offer only coarse storage choices. Entropy coding supports fi…
- arXiv cs.AIresearchLLMs are not stochastic parrots: Evidence for meaning-mediated abstraction from conlang-like tasks
arXiv:2609.34187v4 Announce Type: replace-cross Abstract: The strong version of the stochastic parrot argument claims that, although large language models (LLMs) may exceed rote regurgitation, they cannot move beyond sta…
- arXiv cs.AIresearchAlphaPareto: Formulaic Alpha Discovery with LLM-Guided Multi-Objective Reinforcement Learning
arXiv:2609.34188v1 Announce Type: cross Abstract: Formulaic alpha discovery is a core challenge in quantitative trading, as identifying alphas that work well together remains difficult. Recent reinforcement learning (RL)…
- arXiv cs.AIresearchReGDiff: Guided Diffusion in Regulated Latent Space for Exploring Metamaterial Voxel Geometry
arXiv:2609.34231v1 Announce Type: cross Abstract: Metamaterials are artificially engineered structures whose mechanical and physical behaviors are strongly shaped by geometry rather than composition. Voxel representation…
- arXiv cs.AIresearchCoherence-Aware Distributional Evaluation of Open-Ended Text Generation
arXiv:2609.34240v2 Announce Type: replace-cross Abstract: Existing open-ended generation metrics measure likelihood, lexical diversity, or distributional similarity in generic representation space, yet can miss fundament…
- arXiv cs.AIagentsCertified Multi-Source Integrity for Structured Agent Actions
arXiv:2609.34245v1 Announce Type: cross Abstract: LLM agents increasingly take privileged, often irreversible structured actions, such as paying an invoice. They assemble each action from action-critical fields in docume…
- arXiv cs.AIresearchWAM-OPD: Sharpening World Action Models via On-Policy Distillation
arXiv:2609.34250v1 Announce Type: cross Abstract: Pretrained world action models (WAMs) provide generalist capabilities across diverse robotic manipulation tasks, yet improving target-task performance to an expert level…
- arXiv cs.AIresearchInvestigating Human--AI Discrepancies via Multiple-Solution Problems
arXiv:2609.34258v1 Announce Type: cross Abstract: Frontier artificial intelligence (AI) models are benchmarked on whether they reach a correct answer. Yet many problems admit several correct answers and repeated attempts…