AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchHybridStitch: Token-Adaptive Model Stitching for Efficient Diffusion Generation
arXiv:2603.07815v3 Announce Type: replace-cross Abstract: Iterative diffusion generation requires different amounts of model capacity at different token positions. Prior model combinations allocate capacity by denoising…
- arXiv cs.AIresearchAnolisVLA: Lightweight Vision-Language-Action Model with Deep State Space Models for Mobile Manipulation
arXiv:2603.15046v2 Announce Type: replace-cross Abstract: In this study, we address the problem of language-guided robotic manipulation, where a robot is required to manipulate a wide range of objects based on visual obs…
- arXiv cs.AIresearchDyJR: Preserving Diversity in Reinforcement Learning with Verifiable Rewards via Dynamic Jensen-Shannon Replay
arXiv:2603.16157v2 Announce Type: replace-cross Abstract: While Reinforcement Learning (RL) enhances Large Language Model reasoning, on-policy algorithms like GRPO are sample-inefficient as they discard past rollouts. Ex…
- arXiv cs.AIresearchContrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors
arXiv:2603.23356v2 Announce Type: replace-cross Abstract: We propose a novel clustering approach for point-cloud segmentation based on supervised contrastive metric learning (CML). Rather than predicting cluster assignme…
- arXiv cs.AIresearchSpec-Harness: Assessing and Guiding LLM-Synthesized Formal Specifications Beyond Verifiability
arXiv:2604.00280v3 Announce Type: replace-cross Abstract: Formal specifications play a central role in ensuring software reliability, yet automatically synthesizing high-quality specifications remains difficult and often…
- arXiv cs.AIresearchProcess Rewards for Outcome-Guided Reasoning Steps in LLM Reinforcement Learning
arXiv:2604.02341v2 Announce Type: replace-cross Abstract: Mathematical reasoning in large language models has improved substantially with reinforcement learning using verifiable rewards, where final answers can be checke…
- arXiv cs.AIresearchPickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking
arXiv:2604.04690v3 Announce Type: replace-cross Abstract: Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setups. We present…
- arXiv cs.AIresearchGenerating Synthetic Doctor-Patient Conversations for Long-form Audio Summarization
arXiv:2604.06138v2 Announce Type: replace-cross Abstract: Long-context audio reasoning is underserved in both training data and evaluation. Existing benchmarks target short-context tasks, and the open-ended generation ta…
- arXiv cs.AIresearchCMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection
arXiv:2604.07457v2 Announce Type: replace-cross Abstract: While decoupled control schemes for legged mobile manipulators have shown robustness, learning holistic whole-body control policies for tracking global end-effect…
- arXiv cs.AIresearchPR-MaGIC: Prompt Refinement Via Mask Decoder Gradient Flow For In-Context Segmentation
arXiv:2604.12113v2 Announce Type: replace-cross Abstract: Visual Foundation Models (VFMs) such as the Segment Anything Model (SAM) have significantly advanced broad use of image segmentation. However, SAM and its variant…
- arXiv cs.AIresearchFeed-Forward 3D Scene Modeling: A Problem-Driven Perspective
arXiv:2604.14025v2 Announce Type: replace-cross Abstract: Reconstructing 3D representations from 2D inputs is a fundamental task in computer vision and graphics, serving as a cornerstone for understanding and interacting…
- arXiv cs.AIresearchBeyond Single-Model Optimization: Preserving Plasticity in Continual Reinforcement Learning
arXiv:2604.15414v3 Announce Type: replace-cross Abstract: Continual reinforcement learning must balance retention with adaptation, yet many methods still rely on \emph{single-model preservation}, committing to one evolvi…
- arXiv cs.AIresearchTurboEvolve: Towards Fast and Robust LLM-Driven Program Evolution
arXiv:2604.18607v2 Announce Type: replace-cross Abstract: LLM-driven program evolution can discover high-quality programs, but high API cost and run-to-run variance hinder reliable progress. We propose TurboEvolve, a mul…
- arXiv cs.AIresearchUniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
arXiv:2604.19734v2 Announce Type: replace-cross Abstract: Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging th…
- arXiv cs.AIresearchMILP the Gap: An Automated Reasoning Approach to Condorcet's Paradox
arXiv:2604.19851v2 Announce Type: replace-cross Abstract: Computational social choice studies how conflicting individual preferences are aggregated into a collective decision, with applications to elections, AI evaluatio…
- arXiv cs.AIresearchCall-Chain-Aware LLM-Based Test Generation for Java Projects
arXiv:2604.22046v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have recently shown strong potential for generating project-level unit tests. However, existing state-of-the-art approaches primarily…
- arXiv cs.AIresearchPermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training
arXiv:2604.22117v3 Announce Type: replace-cross Abstract: Aligned large language models (LLMs) remain vulnerable to adversarial manipulation, and their reliance on web-scale pretraining creates a subtle but consequential…
- arXiv cs.AIresearchHiding in Plain Sight: Detectability-Aware Antidistillation of Reasoning Models
arXiv:2604.23238v3 Announce Type: replace-cross Abstract: Distillation via reasoning traces exposes frontier models to adversarial third parties, who can bypass their guardrails and misappropriate their capabilities. Exi…
- arXiv cs.AIresearchDependency Parsing Across the Resource Spectrum: Evaluating Architectures on High and Low-Resource Languages
arXiv:2605.02608v3 Announce Type: replace-cross Abstract: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource s…
- arXiv cs.AIresearchRefining Compositional Diffusion for Reliable Long-Horizon Planning
arXiv:2605.03075v2 Announce Type: replace-cross Abstract: Compositional diffusion planning generates long-horizon trajectories by stitching together overlapping short-horizon segments through score composition. However,…
- arXiv cs.AIresearchHow Do Novice Describers Edit AI Drafts? The Role of Draft Quality in Audio Description Authoring
arXiv:2605.05348v2 Announce Type: replace-cross Abstract: Audio description (AD) narrates visual elements in video for blind and low-vision audiences. Recent work has shown that giving novice describers an AI-generated d…
- arXiv cs.AIresearchXL-SafetyBench: A Country-Grounded Cross-Cultural Benchmark for LLM Safety and Cultural Sensitivity
arXiv:2605.05662v2 Announce Type: replace-cross Abstract: Current LLM safety benchmarks are predominantly English-centric and often rely on translation, failing to capture country-specific harms. Moreover, they rarely ev…
- arXiv cs.AIresearchSelective Successor Eligibility Traces for RLVR
arXiv:2605.05965v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become a key approach for improving the reasoning abilities of large language models. However, widely us…
- arXiv cs.AIresearchAdaGamma: State-Dependent Discounting for Temporal Adaptation in Reinforcement Learning
arXiv:2605.06149v2 Announce Type: replace-cross Abstract: The discount factor in reinforcement learning controls both the effective planning horizon and the strength of bootstrapping, yet most deep RL methods use a singl…
- arXiv cs.AIresearchMultimodal synthesis of MRI and tabular data with diffusion in a joint latent space via cross-attention
arXiv:2605.06699v2 Announce Type: replace-cross Abstract: We propose a multimodal latent diffusion model that jointly synthesizes volumetric magnetic resonance imaging (MRI) and tabular clinical data within a shared late…
- arXiv cs.AIresearchHIMMEL: Hierarchical Interleaved Multi-stream Motion Encoding for Long Video Understanding
arXiv:2605.08158v3 Announce Type: replace-cross Abstract: Long video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB frames, token growth wi…
- arXiv cs.AIresearchDiscrete Flow Matching for Offline-to-Online Reinforcement Learning
arXiv:2605.12379v2 Announce Type: replace-cross Abstract: Many reinforcement learning (RL) tasks have discrete action spaces, but most generative policy methods based on diffusion and flow matching are designed for conti…
- arXiv cs.AIresearchBeyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training
arXiv:2605.12483v5 Announce Type: replace-cross Abstract: In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated to the model and reward density where it is…
- arXiv cs.AIresearchDiscrete MeanFlow: One-Step Generation via Conditional Transition Kernels
arXiv:2605.12805v2 Announce Type: replace-cross Abstract: MeanFlow enables one-step generation in continuous spaces by learning an average velocity over a time interval rather than the instantaneous velocity field of flo…
- arXiv cs.AIresearchRevealing the Gap in Human and VLM Scene Description through Counterfactual Semantic Saliency
arXiv:2605.13047v3 Announce Type: replace-cross Abstract: Evaluating whether large vision-language models (VLMs) align with human perception for high-level semantic scene comprehension remains a challenge. Traditional wh…
- arXiv cs.AIresearch$\alpha$-TCAV: A Unified Framework for Testing with Concept Activation Vectors
arXiv:2605.15688v2 Announce Type: replace-cross Abstract: Concept Activation Vectors (CAV) and the Testing with CAVs (TCAV) method are fundamental tools for concept-based explainability in deep learning, yet their practi…
- arXiv cs.AIresearchConditioned in Training, Dispensable at Inference: Typological Conditioning for Multilingual Speech Translation
arXiv:2605.16026v2 Announce Type: replace-cross Abstract: Multilingual speech translation frontends built on speech large language models typically condition on language identity, as a learned embedding or a prompted lan…
- arXiv cs.AIresearchLoopQ: Quantization for Looped Language Models
arXiv:2605.16343v2 Announce Type: replace-cross Abstract: Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing the parameter count. H…
- arXiv cs.AIresearchThe Impact of AI Search on the Online Content Ecosystem: Evidence from Google and Reddit
arXiv:2605.16428v4 Announce Type: replace-cross Abstract: Search engines traditionally complement online content platforms by directing users seeking information to external websites. The emergence of generative AI searc…
- arXiv cs.AIresearchGlobal Automation Atlas
arXiv:2605.17086v3 Announce Type: replace-cross Abstract: Automation can displace or complement labour, but this need not be constant across economies. Existing exposure measures typically assign fixed scores to tasks or…
- arXiv cs.AIresearchLearning Higher-Order Interactions from Incomplete Spatiotemporal Data with Mixtures of Hypergraph Laplacians
arXiv:2605.17316v2 Announce Type: replace-cross Abstract: Traffic sensor data exhibit strong temporal and cross-sensor dependencies, yet long gaps erase both a sensor's local history and the joint observations needed to…
- arXiv cs.AIresearch$\boldsymbol{f}$-OPD: Stabilizing Long-Horizon On-Policy Distillation with Freshness-Aware Control
arXiv:2605.17862v2 Announce Type: replace-cross Abstract: Scaling on-policy distillation (OPD) for large language models (LLMs) confronts a fundamental tension: asynchronous execution is necessary for system efficiency,…
- arXiv cs.AIresearchTraining data attribution in diffusion models via mirrored unlearning and noise-consistent skew
arXiv:2605.17938v2 Announce Type: replace-cross Abstract: Training data attribution (TDA) should enable generative model interpretability and foster a variety of related downstream tasks. Nonetheless, current TDA approac…
- arXiv cs.AIresearchExact Symmetry as Algebra: Group-Structured Tensors that Enforce Physical Selection Rules
arXiv:2605.20440v3 Announce Type: replace-cross Abstract: Symmetry determines which physical responses of molecules and crystals are allowed and underlies many models used throughout the physical sciences. Yet several wi…
- arXiv cs.AIresearchStdlib or Third-Party? Empirical Performance and Correctness of LLM-Assisted Zero-Dependency Python Libraries
arXiv:2605.21405v2 Announce Type: replace-cross Abstract: Third-party Python libraries introduce dependency-management overhead, supply-chain risk, and deployment friction. We study when eliminating a dependency preserve…
- arXiv cs.AIresearchCTI2TTP: Evidence-Grounded TTP Extraction from Cyber Threat Intelligence via Diverge-then-Converge Verification
arXiv:2605.25836v2 Announce Type: replace-cross Abstract: Extracting MITRE ATT&CK techniques from cyber threat intelligence (CTI) reports is an open-set, multi-label problem requiring both high recall (not missing techni…
- arXiv cs.AIresearchGAC: Noise-Aware Adaptive Mixing for Hybrid SFT-RL Post-Training
arXiv:2605.26184v2 Announce Type: replace-cross Abstract: Hybrid post-training combining supervised fine-tuning (SFT) and reinforcement learning (RL) is the standard paradigm for aligning large language models, yet fixed…
- arXiv cs.AIresearchBeyond Questions: Evaluating the Stated Knowledge of LLMs
arXiv:2605.26937v5 Announce Type: replace-cross Abstract: Parametric knowledge in large language models (LLMs) is a cornerstone of their success, yet remains poorly understood. Existing knowledge benchmarks typically rel…
- arXiv cs.AIresearchHow Do Uncertainty Estimators Align with Hallucination Occurrence in LLMs?
arXiv:2605.27016v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can hallucinate, but do model uncertainty estimators reliably reveal when they do? Although uncertainty estimators are increasingly u…
- arXiv cs.AIresearchWhen LLM Reward Design Fails: Diagnostic-Driven Refinement for Sparse Structured RL
arXiv:2605.28918v2 Announce Type: replace-cross Abstract: For sparse, structured reinforcement-learning tasks with semantic reward-function interfaces, LLM-generated reward shaping is better framed as debugging than one-…
- arXiv cs.AIresearchGEO-Bench: Benchmarking Ranking Manipulation in Generative Engine Optimization
arXiv:2605.29107v3 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly rank products, documents, and recommendations for user queries, which makes manipulating these rankings a growing concer…
- arXiv cs.AIresearchidSCD: Identifying Training Datasets through Semantic Correlation Descriptors
arXiv:2605.30462v2 Announce Type: replace-cross Abstract: Can a dataset be recognized from the spurious correlations it induces during training? We argue that datasets leave dataset-specific traces in a model's learned s…
- arXiv cs.AIresearchHoeffding Concept Bottleneck Models with Applications to Overhead Images
arXiv:2606.00082v2 Announce Type: replace-cross Abstract: Explainability of deep learning algorithms is critical for computer-vision applications with high-stakes decisions. Concept bottleneck models (CBM) have recently…
- arXiv cs.AIresearchReal2SAM2Real: Generative 3D Caches as Complementary Context for Video Diffusion
arXiv:2606.00299v2 Announce Type: replace-cross Abstract: Video Diffusion Models (VDMs) synthesize high-quality videos, but precise camera and motion control remains challenging when large viewpoint shifts, changing occl…
- arXiv cs.AIresearchShape Your Body: Value Gradients for Multi-Embodiment Robot Design
arXiv:2606.00702v2 Announce Type: replace-cross Abstract: We propose to turn generalist multi-embodiment value functions into reusable models for robot design. Instead of running a new reinforcement learning co-design lo…