AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchReLaG: A Scalable Framework Generalizing Random Splits to Data with Latent Relations
arXiv:2609.38538v1 Announce Type: cross Abstract: Random splitting can yield non-independent train--test subsets when a dataset contains related samples, as is common in certain applications such as biochemical studies.…
- arXiv cs.AIresearchTowards Universal Wasserstein Barycenters through Flow Matching
arXiv:2609.38547v1 Announce Type: cross Abstract: Defining a weighted mean over probability measures under probability metrics is a central tool in probabilistic machine learning. Under the Wasserstein metric, these are…
- arXiv cs.AIresearchRetargeting Motions to Diverse Skeletons via Learnable Flattening
arXiv:2609.38578v3 Announce Type: replace-cross Abstract: Cross-structural motion retargeting aims to transfer motion between different skeletal topologies. Despite recent progress, existing state-of-the-art models strug…
- arXiv cs.AIresearchFlexRouter: Learning Complementary Model Sets for Flexible LLM Routing
arXiv:2609.38585v1 Announce Type: cross Abstract: Existing Large Language Model (LLM) routing methods score LLMs independently to select top-$k$ models. However, this ignores model correlations and enforces a rigid compu…
- arXiv cs.AIresearchPrompt2Skill: Unsupervised Skill Optimization From Natural Language Instructions
arXiv:2609.38593v1 Announce Type: cross Abstract: Skills are external artifacts that Large Language Models (LLMs) consume at inference time to improve their performance on specialized domains by incorporating relevant pr…
- arXiv cs.AIagentsBeyond Oracle Communication: Benchmarking Interactive Intent Alignment Under Miscommunication and Evolving User Intent
arXiv:2609.38604v1 Announce Type: cross Abstract: Modern LLM agents increasingly tackle complex tasks through interactive, long-horizon exchanges with users, while existing benchmarks generally assume that users always a…
- arXiv cs.AIagentsAfter a Decade: Bringing Shadow Removal into the Real World with Agentic Training Data
arXiv:2609.38607v1 Announce Type: cross Abstract: Shadow removal looks nearly solved on established benchmarks, yet remains brittle in the real world. Models have advanced; the paired training data they rely on have bare…
- arXiv cs.AIresearchExo2EgoHOI: Hand-Object-Interaction Aware Exocentric-to-Egocentric Video Generation
arXiv:2609.38615v1 Announce Type: cross Abstract: Egocentric videos of human manipulation provide valuable visual experience for embodied intelligence, yet collecting such data at scale is costly. Exocentric-to-egocentri…
- arXiv cs.AIresearchDifferentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders
arXiv:2609.38618v1 Announce Type: cross Abstract: We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unknown number of exogeno…
- arXiv cs.AIresearchInterpretable but Fragile? Robustness of Concept Bottlenecks under Geometric-Semantic Perturbations
arXiv:2609.38625v3 Announce Type: replace-cross Abstract: Concept Bottleneck Models (CBMs) are designed to provide interpretable intermediate representations, yet how such bottlenecks affect robustness remains unclear, w…
- arXiv cs.AIresearchTemplate-Search Domain Adaptation via Multi-Stage Feature Alignment for Cross-Modal Object Tracking
arXiv:2609.38637v1 Announce Type: cross Abstract: Visual object tracking typically assumes that the initial template and subsequent search frames share the same sensing modality. In practice, sensor availability or opera…
- arXiv cs.AIresearchAlignment via Training Against Probes Without Losing Monitorability
arXiv:2609.38645v3 Announce Type: replace-cross Abstract: Models are usually aligned based on their observed outputs, using demonstrations, preference data, or reward signals. These objectives reward responses that look…
- arXiv cs.AIagentsTERRA: Terrain-Aware Reconstruction, Retargeting and Control for Musculoskeletal Locomotion
arXiv:2609.38653v1 Announce Type: cross Abstract: Recent advances in musculoskeletal modeling and reinforcement learning have enabled muscle-actuated agents to reproduce increasingly complex human motions. Yet these capa…
- arXiv cs.AIresearchBandits with Multiple Optimal Arms: Minimax Regret and Non-Adaptivity
arXiv:2609.38659v2 Announce Type: replace-cross Abstract: We study multi-armed bandits (MAB) with multiple optimal arms, motivated by the fact that many practical decision making problems admit multiple correct answers.…
- arXiv cs.AIagentsBreaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation
arXiv:2609.38660v2 Announce Type: replace-cross Abstract: Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consistent terminology…
- arXiv cs.AIagentsCollabFlow: Recursive Self-Improvement of Agent Collaboration
arXiv:2609.38662v3 Announce Type: replace-cross Abstract: Recursive self-improvement (RSI) lets a system improve from its own outcomes; in LLM-based multi-agent systems, Agents refine one another within a task, and outco…
- arXiv cs.AIresearchUnderstanding Off- vs On-Policy Distillation: A Tale of Distinct Training Objectives
arXiv:2609.38666v1 Announce Type: cross Abstract: On-policy distillation (OPD) learns from teacher feedback on student-generated responses and has shown promise in reducing forgetting relative to supervised fine-tuning (…
- arXiv cs.AIresearchProvable Test-Time Scaling for Beam Search in LLM Reasoning
arXiv:2609.38672v1 Announce Type: cross Abstract: Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning invalid reasoning pat…
- arXiv cs.AIresearchReGain: Restoring Subject Fidelity in Personalization on Synthetic Images
arXiv:2609.38680v1 Announce Type: cross Abstract: Text-to-image diffusion models are personalized to a subject by DreamBooth fine-tuning on a handful of its images. Increasingly, these images come from a diffusion model…
- arXiv cs.AIresearchUnveiling the Value of Motion for Cinematic Camera Trajectories
arXiv:2609.38683v1 Announce Type: cross Abstract: Cinematic camera motion is a fundamental storytelling tool, defined not only by where the camera is positioned in the scene, but also by how it moves in terms of directio…
- arXiv cs.AIresearchAlways-On Experimentation
arXiv:2609.38695v1 Announce Type: cross Abstract: Generative AI has dramatically accelerated the rate at which new treatments---from novel pharmaceuticals to online marketing campaigns---can be conceived and deployed. As…
- arXiv cs.AIresearchCascadia: A Control-Plane-Free Alternative to Hyperconverged AI Infrastructure
arXiv:2609.38697v1 Announce Type: cross Abstract: We present Cascadia, a system for serving large language models on fleets of commodity Intel AIPCs using their CPU, integrated-GPU, and NPU resources. Every node embeds i…
- arXiv cs.AIresearchSpatialCORE: Confidence-Aware Grounded Spatial Reasoning in Large Vision--Language Models
arXiv:2609.38716v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have made remarkable progress across visual perception tasks, yet spatial reasoning remains a persistent weakness, especially for que…
- arXiv cs.AIresearchSoft Spatial Reasoning
arXiv:2609.38717v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) commonly perform spatial reasoning through chain-of-thought (CoT), encoding intermediate reasoning as autoregressive sequences of dis…
- arXiv cs.AIagentsWhere the Evidence Lives: Auditing AI Companions' Self-Descriptions
arXiv:2609.38753v1 Announce Type: cross Abstract: Companion agents describe themselves: they remember, they understand their users, the relationship has changed them. We argue that such accounts, and the experience ratin…
- arXiv cs.AIresearchAdaptive-GEPA: Make Your Harness Fit Heterogeneous Requests
arXiv:2609.38762v1 Announce Type: cross Abstract: Reflective optimizers such as GEPA improve language model prompts from execution traces and evaluator feedback; full-program extensions can also rewrite tools and control…
- arXiv cs.AIresearchdattri-LLM: A Unified and Efficient Library for Training Data Attribution at LLM Scale
arXiv:2609.38767v2 Announce Type: replace-cross Abstract: Training data attribution (TDA) estimates the contribution of individual training examples to model outputs. Most scalable TDA methods rely on per-example gradien…
- arXiv cs.AIresearchLearning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics
arXiv:2609.38768v1 Announce Type: cross Abstract: Prior work has shown that neural networks exhibit implicit biases toward low-complexity structure (e.g., spectral bias), memorization dynamics, and compression-like effec…
- arXiv cs.AIresearchDistilling Diffusion Score Discrepancy for Efficient Training Data Attribution
arXiv:2609.38776v2 Announce Type: replace-cross Abstract: Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either require cost…
- arXiv cs.AIresearchRAST: Resolution-Aware Privileged Structure Transfer for Low-Resolution Audio Activity Recognition
arXiv:2609.38780v1 Announce Type: cross Abstract: Audio is increasingly used for human activity recognition (HAR) because it captures object interactions, environmental events, and contextual cues in everyday environment…
- arXiv cs.AIresearchChartDensity-Bench: Benchmarking MLLMs for Numerical Data Reconstruction under Visual Density
arXiv:2609.38781v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) offer a promising approach for recovering numerical data from scientific charts, but their ability to reconstruct chart data from…
- arXiv cs.AIagentsEvaluating Persistent Calibration under Evolving Model Knowledge
arXiv:2609.38797v1 Announce Type: cross Abstract: As AI systems move from static repositories to agents that are capable of continual adaptation and learning, maintaining their trustworthiness means equipping the models…
- arXiv cs.AIresearchUncovering Uncontrolled Repetition through Residual Stream Dynamics
arXiv:2609.38802v1 Announce Type: cross Abstract: Uncontrolled repetition can prolong autoregressive generation in large language models (LLMs) and enable resource consumption attacks. Prior analyses of repetitive genera…
- arXiv cs.AIagentsExplicit Trajectory Diversity for RL-Based Post-Training of LLM Agents
arXiv:2609.38805v1 Announce Type: cross Abstract: LLM agents often admit multiple high-quality solutions to the same task, differing in reasoning structure, tool-use pattern, or interaction trajectory. Yet existing notio…
- arXiv cs.AIagentsStateTree: Enhancing Long-Term Dialogue Reasoning via Reinforcement Learning
arXiv:2609.38809v1 Announce Type: cross Abstract: Large language models deployed as personalized assistants must reason over long, evolving interaction histories. However, in long-term dialogue reasoning, relevant eviden…
- arXiv cs.AIresearchCRAFT: Causal Responsibility and Failure Tracing in Medical Vision Language Models
arXiv:2609.38810v3 Announce Type: replace-cross Abstract: As vision language models are increasingly deployed in clinical diagnosis, understanding how they internally resolve competing visual and textual signals becomes…
- arXiv cs.AIresearchFuture Video Generation Better Aligns with the Human Visual Cortex than Observed Video
arXiv:2609.38819v1 Announce Type: cross Abstract: Studying the alignment between the internal representations of vision models and the responses of the visual cortex to the same observed visual stimuli has enabled us to…
- arXiv cs.AIresearchBARRAC: Adaptation of an English Aspect-based Sentiment Analysis Approach for Classification Tasks in Arabic Dialects
arXiv:2609.38820v1 Announce Type: cross Abstract: With the rapid growth of Arabic NLP, several models, datasets and benchmarks have been reported. This paper asks whether approaches developed for majority languages like…
- arXiv cs.AIagentsSkillSeek: Revisiting Agent Skill Retrieval at Marketplace Scale
arXiv:2609.38822v1 Announce Type: cross Abstract: Anthropic's Agent Skills package reusable procedural know-how for an LLM agent into SKILL.md directories, and open-source aggregations have grown past 230,000 skills, mak…
- arXiv cs.AIresearchscTrilemma: Balancing Identity, Invariance, and Fidelity in Single-Cell Representation Learning
arXiv:2609.38840v2 Announce Type: replace-cross Abstract: Single-cell RNA-seq representation learning is fundamentally label-free: cell identities, states, and contexts are not fixed training targets, so what constitutes…
- arXiv cs.AIresearchScoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics
arXiv:2609.38847v1 Announce Type: cross Abstract: Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists. A judge checks each criterion of a rubric, and the verdicts are aggregate…
- arXiv cs.AIresearchWhere MLLMs Fail and Why: Causal Task Decomposition for Capability Failure Diagnosis
arXiv:2609.38851v1 Announce Type: cross Abstract: End-to-end accuracy on compositional tasks records how often MLLMs fail, but cannot distinguish whether a failure reflects an intrinsic deficit in the targeted capability…
- arXiv cs.AIresearchMitigating the Length-Scaling Tax with Online Distillation
arXiv:2609.38854v1 Announce Type: cross Abstract: Length scaling during reinforcement-learning (RL) post-training is often viewed as a sign of improved reasoning ability, especially on difficult problems, but may also ma…
- arXiv cs.AIresearchOnline Evolution Strategy for Flow-Matching VLA Policies via Self-Supervised Trajectory Distribution Optimization
arXiv:2609.38855v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models based on generative frameworks, such as Flow Matching, have recently achieved impressive performance in robotic manipulation. Unlike d…
- arXiv cs.AIresearchOptimal Design for Active Preference Learning with Biased LLM Judges
arXiv:2609.38860v1 Announce Type: cross Abstract: Learning from human preferences is central to large language model (LLM) alignment, but human preference annotation is costly. Active preference learning reduces this cos…
- arXiv cs.AIagentsEfficient Multi-Modal Planning with Reward-Guided Preference Optimization for Autonomous Driving
arXiv:2609.38862v1 Announce Type: cross Abstract: Safe and efficient trajectory planning is essential in autonomous driving. However, existing end-to-end approaches often fall short in both computational efficiency and s…
- arXiv cs.AIresearchGeoNest: Learning to Select Failure-Aware Neighborhoods for the Irregular Knapsack Problem in a Circular Container
arXiv:2609.38863v1 Announce Type: cross Abstract: The two-dimensional irregular knapsack problem in a fixed circular container is an important combinatorial optimization problem for maximizing material utilization in man…
- arXiv cs.AIresearchAudio Token Attention Is Predictable Before the Language Model Runs
arXiv:2609.38878v1 Announce Type: cross Abstract: A large audio language model (LALM) turns a minute of speech into 750-1,500 tokens and prefills every one. Image-token pruning often cuts after the language model's first…
- arXiv cs.AIresearchDoes Learning Protein Folding Generalize to Broader Reasoning?
arXiv:2609.38879v1 Announce Type: cross Abstract: Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natur…
- arXiv cs.AIresearchRight Answers, Costly Models: The Efficiency Gap in LLM-based Optimization Modeling
arXiv:2609.38884v1 Announce Type: cross Abstract: Optimization modeling formulates real-world decision problems as mathematical programs that solvers can use to find optimal decisions. Large language models (LLMs) can au…