AI news archive.
10,660 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchGenerative Flow on Symbolic Abstractions for Embodiment Capability Discovery
arXiv:2610.17174v1 Announce Type: cross Abstract: Achieving complex, long-horizon robot behavior remains challenging because successful execution depends on skills grounded in a robot's physical affordances. Existing tra…
- arXiv cs.AIresearchFreeSolo: Linear-time Sparse Attention with Zero-Shot Length Extrapolation
arXiv:2610.17191v1 Announce Type: cross Abstract: Linear-time sequence models typically store long-context information in a recurrent state. A fixed-size state limits how much information can be stored, while hybrid arch…
- arXiv cs.AIresearchCompute-optimal data scaling for neural surrogates via multi-fidelity training
arXiv:2610.17221v1 Announce Type: cross Abstract: Neural surrogates for Partial Differential Equations (PDEs) are usually trained on synthetic data from numerical simulators. As models grow and scaling laws demand larger…
- arXiv cs.AIresearchFrom Conceptual EneMonIE to an Executable Backend: A Modular Architecture for Energy Information Extractionand Grounded Question Answering
arXiv:2610.17243v1 Announce Type: cross Abstract: The original EneMonIE proposal describes a Web-based semantic system for continuously monitoring energy trends through targeted information extraction from heterogeneous…
- arXiv cs.AIresearchMeasuring Learned Equivariance: An Information Theoretic Approach
arXiv:2610.17269v1 Announce Type: cross Abstract: Learning symmetries through data augmentation offers an alternative to architectural equivariance, but does not guarantee that a network respects those symmetries. Measur…
- arXiv cs.AIresearchPhase-Conditioned Affordance Fields for SE(3)-Equivariant Visuomotor Policies
arXiv:2610.17281v1 Announce Type: cross Abstract: SE(3)-equivariance provides a useful geometric inductive bias for visuomotor policies, encoding that actions covary with rigid transformations of the observation. However…
- arXiv cs.AIresearchStartLux-Decision: AutoThink Decision Models with Reinforcement Learning for Calibrated Deliberative Decisions
arXiv:2610.17309v2 Announce Type: replace-cross Abstract: We introduce StartLux-Decision, decision models from 0.8B to 27B parameters that combine efficient single-pass prediction with adaptive, confidence-guided reasoni…
- arXiv cs.AIresearchJoint Embedding with Dynamic Integration network (JEDI-net)
arXiv:2610.17314v1 Announce Type: cross Abstract: Deep learning has become the dominant form of machine learning due in part, to its ability to automatically engineer complex meta features from data. However, even deep l…
- arXiv cs.AIresearchThinking Outside the Bots: Human Ideas in AI's Semantic Blind Spots Are Not Inferior
arXiv:2610.17324v1 Announce Type: cross Abstract: AI-generated outputs are frequently perceived as highly creative, but also tend to be more similar to each other (homogenized) than human ideas. Does the human heterogene…
- arXiv cs.AIresearchOn the Statistical Mechanics of Attention
arXiv:2610.17347v1 Announce Type: cross Abstract: We revisit the statistical mechanics of Transformer attention via modern Hopfield models. We present a free-energy formalism to characterize parameter regimes and critica…
- arXiv cs.AIresearchMID: Mask-Image Distributional Divergence for Evaluating Medical Image Segmentation
arXiv:2610.17354v2 Announce Type: replace-cross Abstract: Current evaluation protocols for medical image segmentation remain largely anchored in sample-wise comparisons, e.g., Dice and Hausdorff distance, which require e…
- arXiv cs.AIresearchclaimcheck: Measuring and Improving Specification Faithfulness
arXiv:2610.17358v1 Announce Type: cross Abstract: Formal verification proves that a program satisfies its specification, but says nothing about whether the specification captures what the author meant. As LLMs increasing…
- arXiv cs.AIresearchHASTE: Hierarchical and Attribute-aware Supervision for Generative Recommendation
arXiv:2610.17368v1 Announce Type: cross Abstract: Generative recommendation models have shown promising results by representing items as semantic IDs and generating these IDs autoregressively. We introduce an approach sh…
- arXiv cs.AIresearchMitigating Black-Box Score-Based Query Attacks via Gradient Confusion
arXiv:2610.17382v1 Announce Type: cross Abstract: Score-based black-box adversarial attacks pose a serious threat to deployed machine learning systems by exploiting repeated model queries to craft adversarial examples, r…
- arXiv cs.AIresearchCross-Market Meta-Learning for Electricity Price Forecasting
arXiv:2610.17387v1 Announce Type: cross Abstract: Day-ahead (DA) electricity price forecasting is usually posed per market, with a separate model fitted to each grid's own history. This caps accuracy: statistical and tre…
- arXiv cs.AIresearchLearning to Detect Shifts in Unstructured Data Streams via Monitoring-by-Betting
arXiv:2610.17399v1 Announce Type: cross Abstract: Continuous monitoring of generative AI systems requires detecting distribution shifts in the unstructured data they ingest and generate. Existing monitoring methods typic…
- arXiv cs.AIresearchEvent-Centric Earthquake Monitoring with a Multi-Station Transformer
arXiv:2610.17404v1 Announce Type: cross Abstract: Earthquake catalogs underpin studies such as fault structure, earthquake processes and seismic hazard, and their value depends on being both complete and reliable. Machin…
- arXiv cs.AIresearchCross-View Time-Frequency Diffusion for Extreme-Aware Time-Series Generation
arXiv:2610.17407v2 Announce Type: replace-cross Abstract: Generating realistic time-series requires faithfully reproducing rare but high-impact extreme events. Existing approaches often rely on specialized representation…
- arXiv cs.AIresearchBottlenecked semantic compression by representation reconstruction
arXiv:2610.17412v1 Announce Type: cross Abstract: Vision Transformers incur high computational costs when processing long visual token sequences. Existing reduction methods typically rank tokens independently and discard…
- arXiv cs.AIresearchOn Making $\mathrm{SE}(2)$-Invariant Networks Optimal
arXiv:2610.17424v1 Announce Type: cross Abstract: Respecting a-priori known symmetries of underlying data is a key principle in designing efficient neural architectures. Convolutional neural networks are a canonical exam…
- arXiv cs.AIresearchReinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning
arXiv:2610.17426v1 Announce Type: cross Abstract: Clinical decision-making is a sequential process of information collection, clinical inference, and forecasting under partial observability, often without a uniquely corr…
- arXiv cs.AIresearchWorld-T3: Test-Time Training as Persistent Memory for Real-Time Video World Models
arXiv:2610.17434v1 Announce Type: cross Abstract: Recent advances in video world models have enabled plausible interactive environments, yet long-horizon generation places a fundamental demand on visual memory: the previ…
- arXiv cs.AIresearchKnow-Thy-Not: Training-Free Targeted Negation in Dual-Encoder Vision-Language Models
arXiv:2610.17440v1 Announce Type: cross Abstract: Dual-encoder vision-language models systematically fail at negation, scoring ''a cat without a dog'' nearly identically to ''a cat with a dog.'' We show that this failure…
- arXiv cs.AIresearchCompositional RL: Evaluating and Training LLMs for Collaborative Reasoning
arXiv:2610.17456v1 Announce Type: cross Abstract: Language models are often evaluated alone but increasingly deployed together, routed between, made to debate, ensembled, or collaboratively trained. For a model inside a…
- arXiv cs.AIresearchAssessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs
arXiv:2610.17469v1 Announce Type: cross Abstract: Social media platforms generate vast volumes of user-generated content that often obscure meaningful discourse, a phenomenon conceptualized as social noise. While existin…
- arXiv cs.AIresearchVeriPIE: Checkable Query Plans and Evidence-Guided Selection for Text-to-SQL
arXiv:2610.17493v1 Announce Type: cross Abstract: Financial Artificial Intelligence (AI) governance and model risk management (MRM) emphasize independent validation, transparency, and effective challenge of AI-generated…
- arXiv cs.AIresearchYUBI-Real2Sim: 3DGS Scene Verification for Reliable Policy Evaluation
arXiv:2610.17521v1 Announce Type: cross Abstract: Evaluating VLA policies on real robots is costly, time-consuming, and difficult to reproduce, making real-to-sim simulation essential for scalable evaluation. While 3D Ga…
- arXiv cs.AIresearchEnhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning
arXiv:2610.17523v1 Announce Type: cross Abstract: With recent developments in Artificial Intelligence (AI), the adoption of distributed learning paradigms such as Federated Learning (FL) has emerged. In simple terms, FL…
- arXiv cs.AIresearchBox-WAM: Box Trajectories as Compact State Representations for World Action Models
arXiv:2610.17526v1 Announce Type: cross Abstract: World action models (WAMs) are a category of policy models that ex- plicitly predict both actions and future world states. They have been shown to outperform action-only…
- arXiv cs.AIresearchTowards Green AI: Evaluating Energy Efficiency for Quantized LLM Inference
arXiv:2610.17531v1 Announce Type: cross Abstract: While it is known that quantization of large language models (LLMs) reduces memory usage via lower-bit weights, studies quantifying the resulting impact on energy usage a…
- arXiv cs.AIresearchConvex-Analytic View of Query Selection for Safety Verification of Markov Decision Processes
arXiv:2610.17541v1 Announce Type: cross Abstract: Safety verification of a Markov decision process (MDP) asks whether the probabilities of reaching several failure states jointly lie in a convex safe region under every s…
- arXiv cs.AIresearchFlashVSR+: Efficient Video Super-Resolution via Hybrid Attention and Reward-Yield Distribution Matching
arXiv:2610.17552v1 Announce Type: cross Abstract: Recent video super-resolution (VSR) models achieve efficient one-step generation through diffusion distillation. However, high-resolution inference remains costly, and di…
- arXiv cs.AIresearchBenchmarking Self-Supervised 3D Medical Vision Foundation Models and Transferability Estimation
arXiv:2610.17573v1 Announce Type: cross Abstract: Self-supervised pretraining has expanded the pool of 3D medical foundation models, but published evaluations use different datasets, endpoints and fine-tuning protocols.…
- arXiv cs.AIresearchDissociating Answerability from Extraction in LLM Question Answering over Radiology Reports
arXiv:2610.17576v1 Announce Type: cross Abstract: Answering questions from radiology reports involves two decisions: whether the report contains an answer and which span gives it. A false answer presents an unsupported c…
- arXiv cs.AIresearchChasing Label Shifters: A Change-Aware Framework for Dynamic Graph Node Classification
arXiv:2610.17582v1 Announce Type: cross Abstract: Entities in real-world systems often evolve over time: users shift between information consumption patterns, researchers migrate between fields, and firms transition betw…
- arXiv cs.AIresearchTaming Softmax at the Limits of Muon: A Unified Approach through Hilbert Geometry
arXiv:2610.17588v1 Announce Type: cross Abstract: Muon controls weight updates through the RMS movement of a linear layer's output. This implicitly treats the linear output as a proxy for the full module, including the n…
- arXiv cs.AIresearchRevisiting the 16x Pretraining Compute Gap between Diffusion and Autoregressive Language Models
arXiv:2610.17590v1 Announce Type: cross Abstract: Diffusion language models (DLMs) have attracted wide attention for their fast, parallel decoding, but they are believed to be far less efficient to train than autoregress…
- arXiv cs.AIresearchTanawpamana: Benchmarking Vision-Language Models on Philippine Cultural Heritage Sites
arXiv:2610.17596v1 Announce Type: cross Abstract: Vision-language models are increasingly used to identify and describe places, yet their recognition and knowledge of specific cultures remain poorly understood, particula…
- arXiv cs.AIresearchMorphology-aware deep learning predicts colorectal cancer survival from whole-slide images
arXiv:2610.17603v2 Announce Type: replace-cross Abstract: Background: Five-year survival prediction from hematoxylin-and-eosin whole-slide images could support risk stratification, but the task is weakly supervised, and…
- arXiv cs.AIresearchRELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning
arXiv:2610.17623v2 Announce Type: replace-cross Abstract: Rubric-based reinforcement learning grades a response against many written criteria but collapses them into one reward, which discards the detail the rubric carri…
- arXiv cs.AIresearch4LC: 4D Latent Cache for Efficient Conditioning in Dynamic World Modeling
arXiv:2610.17625v1 Announce Type: cross Abstract: For video generation in dynamic world modeling, observed static and dynamic content should be efficiently reused to provide conditions for repeated generation at differen…
- arXiv cs.AIresearchThe Tokens Strike Back: Safety-Aware Denoising with Directional Embedding-based Revision
arXiv:2610.17636v1 Announce Type: cross Abstract: The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models. During inference, SAD steers text samples toward safe reg…
- arXiv cs.AIresearchPrT-Flow: Probability-Tractable Flow Matching for Reinforcement Learning
arXiv:2610.17667v1 Announce Type: cross Abstract: Diffusion models and Flow Matching can represent flexible distributions over robot actions, but many reinforcement learning algorithms also require the probability of eac…
- arXiv cs.AIresearchDynamic K-Head: Difficulty-Supervised Budget Selection for Continuous Latent Reasoning
arXiv:2610.17679v1 Announce Type: cross Abstract: Chain-of-thought (CoT) improves reasoning but incurs substantial autoregressive rationale cost. Continuous CoT moves intermediate computation into recurrent hidden states…
- arXiv cs.AIresearchHelpMeOut: A Neuropsychology-Grounded Benchmark for Error-Aware Monitoring of Household Task Execution
arXiv:2610.17694v1 Announce Type: cross Abstract: We introduce HelpMeOut, an embodied AI benchmark for error-aware monitoring of household task execution. Existing embodied household benchmarks typically assume that huma…
- arXiv cs.AIresearchzPocket: Better Pocket Finding with Frozen Co-Folding Features
arXiv:2610.17696v1 Announce Type: cross Abstract: Identifying protein-ligand binding sites (pockets) is important for understanding protein function and developing drugs. However, no public method has leveraged co-foldin…
- arXiv cs.AIresearchLeanPRM: Lean-Grounded Process Supervision for Natural Language Proof Verification
arXiv:2610.17697v2 Announce Type: replace-cross Abstract: AI systems now write long and difficult mathematical proofs, and checking their step-by-step validity is becoming a bottleneck. A learned verifier could check pro…
- arXiv cs.AIresearchFrom Token Correction to Claim Admission: Training-Free Hallucination Mitigation for Large Vision-Language Models
arXiv:2610.17706v1 Announce Type: cross Abstract: Large vision-language models often exhibit object hallucination, generating claims that are not supported by the input image. To mitigate this issue, we propose Evidence-…
- arXiv cs.AIresearchWhere Small Mistakes Matter: Quantifying State Criticality for Imitation Learning
arXiv:2610.17709v2 Announce Type: replace-cross Abstract: Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the dataset. Yet not…
- arXiv cs.AIresearchCoCA: Criterion-Aware Credit Assignment for Rubric-Based Reinforcement Learning
arXiv:2610.17733v1 Announce Type: cross Abstract: Rubric-based reinforcement learning (RL) evaluates responses against explicit criteria to construct rewards, offering a viable way to train large language models on open-…