AI news archive.
19,864 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchBeyond Leaderboards: A Method for Application-Specific Capability Profiling of Large Language Models
arXiv:2610.25659v1 Announce Type: cross Abstract: Public leaderboards rank large language models (LLMs) in general, but an engineer has to choose a model for a specific application, architecture and cost. This paper pres…
- arXiv cs.AIresearchCross-Pollination Retrieval: Finding Transferable Ideas Across Fields in Scientific Literature
arXiv:2610.25678v1 Announce Type: cross Abstract: Many scientific advances come from cross-pollination, carrying an idea from one field over to a problem in another. Yet retrieving papers across fields remains underexplo…
- arXiv cs.AIresearchMT-CC: Multi-Group Temperature Scaling for Asymmetric Calibration Behavior in Class-Incremental Learning
arXiv:2610.25689v1 Announce Type: cross Abstract: As deep neural networks are required to continuously adapt to evolving data, class-incremental learning (CIL) has become an active research area. However, most existing w…
- arXiv cs.AIresearchProgress-Guided Preference Generation for Aligning Generative Control Policies
arXiv:2610.25692v1 Announce Type: cross Abstract: Generative policies have achieved strong performance in imitation learning tasks, but their success is often constrained by the limited availability of expert demonstrati…
- arXiv cs.AIresearchCausal Humility: Governing the Authority of Fairness Claims Under Causal Uncertainty
arXiv:2610.25709v1 Announce Type: cross Abstract: AI systems increasingly shape consequential decisions in employment, lending, healthcare, and public services, making fairness an important problem for auditing and gover…
- arXiv cs.AIresearchCorrected Block Sparse Spectral Optimization: Exact Models, Finite Identification, and Local Dynamics
arXiv:2610.25721v1 Announce Type: cross Abstract: The block generalized-power method pairs threshold-based support selection with inexpensive Stiefel updates, but the standard block formulation conflates two nonequivalen…
- arXiv cs.AIresearchAnatomy of Off-Policy Policy Gradient: Importance Sampling, KL Regularization, and Baselines
arXiv:2610.25725v1 Announce Type: cross Abstract: Reinforcement learning has become a central tool for large language model (LLM) post-training, where policy gradient methods are routinely deployed off-policy, even thoug…
- arXiv cs.AIresearchNo Matter Who You Are: Addressivity and Sycophancy in Large Language Models
arXiv:2610.25728v1 Announce Type: cross Abstract: Sycophancy, where an LLM becomes overly agreeable, deferential, or flattering toward the user, has been identified as a major failure mode. While post-training has been i…
- arXiv cs.AIresearchA Calibrated Instrument for Detecting Disposition Drift in Language Models
arXiv:2610.25735v1 Announce Type: cross Abstract: Before language models are released, they are tested for harmful traits such as willingness to carry out dangerous requests, deception, power seeking, and sycophancy. The…
- arXiv cs.AIresearchCognitively Decoupled Meta-Induction for Out-of-Distribution Generalization
arXiv:2610.25749v1 Announce Type: cross Abstract: In-context learning (ICL) tackles downstream tasks conditioned on demonstrations. This paradigm relies heavily on neighbor-based matching between queries and demonstratio…
- arXiv cs.AIagentsRemember Less, Adapt Longer: Bounded-State In-Context Reinforcement Learning with Recurrent Summary Memory
arXiv:2610.25758v1 Announce Type: cross Abstract: In-context reinforcement learning (ICRL) enables agents to adapt to new tasks without parameter updates, relying on memory to retain what they learn from experience. For…
- arXiv cs.AIagentsComputation, Not Memory: Chain-of-Thought and State Tracking in LLM Agents
arXiv:2610.25760v1 Announce Type: cross Abstract: Language-model agents often reason before they act, and the value of monitoring that reasoning depends on how it relates to their actions. We ask how an agent's actions d…
- arXiv cs.AIresearchAirMPA: A Meteorology-to-Pollution Adapter for Global Air Quality Forecasting
arXiv:2610.25797v1 Announce Type: cross Abstract: Accurate real-time forecasting of atmospheric pollutants is essential for reducing exposure risks and improving public health. Traditional numerical forecasting systems s…
- arXiv cs.AIresearchOptimizing Marketing Budgets for Social Influence: A Generalized Fractional Incentives Framework
arXiv:2610.25823v1 Announce Type: cross Abstract: Marketing managers routinely allocate limited budgets across individuals in social networks whose responsiveness and influence vary substantially. We study the problem of…
- arXiv cs.AIresearchDiffusion Language Models Change Speed Across Tasks and Languages
arXiv:2610.25835v1 Announce Type: cross Abstract: Diffusion language models can write several tokens in one forward pass, and they decide during generation how many passes to spend. Two outputs of the same token length c…
- arXiv cs.AIresearchWhere RL Breaks: Localizing and Repairing Failures via Targeted Continued Pretraining
arXiv:2610.25836v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is widely used to improve reasoning in language models, yet growing evidence suggests that it mostly sharpens what t…
- arXiv cs.AIresearchOV-PEAR: Observation-Verified Pose-Scale Estimation and Reconstruction for Unknown Industrial Manipulation Objects
arXiv:2610.25840v1 Announce Type: cross Abstract: Fine industrial robotic manipulation requires 6D pose, scale, and an operation-ready 3D model. Existing industrial pose estimation relies on CAD models, object-specific t…
- arXiv cs.AIresearchSWIFT: Self-guided Weak-to-strong dIFfusion models via Target extrapolation
arXiv:2610.25844v2 Announce Type: replace-cross Abstract: Training high-quality diffusion models is computationally expensive, and inference-time guidance often adds further sampling cost. In this work, we introduce Swif…
- arXiv cs.AIresearchMeasurement Validity and Representation Dynamics of Confidence Signals in Large Language Models
arXiv:2610.25846v1 Announce Type: cross Abstract: Whether a language model "knows what it knows" matters for trustworthy deployment and for deciding when it should abstain. Prior work usually measures this through one ch…
- arXiv cs.AIresearchRubrics Beyond Evaluation: Exploiting Rubric Structure in Reinforcement Learning
arXiv:2610.25850v1 Announce Type: cross Abstract: Rubrics are widely used to evaluate open-ended question answering by breaking overall quality into explicit criteria. These explicit criteria make rubrics a rich source o…
- arXiv cs.AIresearchAdaptive Decoding Methods for Token-Efficient Reinforcement Learning
arXiv:2610.25860v1 Announce Type: cross Abstract: Rollout decoding affects both which trajectories are used for reinforcement learning with verifiable rewards (RLVR) and how many completion tokens are spent collecting th…
- arXiv cs.AIresearchManifold-Constrained Residual Adaptation with Frame-Adaptive Layer Aggregation for Speaker Verification
arXiv:2610.25892v1 Announce Type: cross Abstract: Large self-supervised speech models provide powerful representations for speaker verification, but full fine-tuning is expensive. Most conventional adapters insert small…
- arXiv cs.AIagentsSecure Agentic Coding through Counterexample-Grounded Feedback
arXiv:2610.25916v2 Announce Type: replace-cross Abstract: As coding models and agent harnesses become more capable, reliable security verification is becoming a central bottleneck. Improving secure code generation requir…
- arXiv cs.AIresearchUtilityBench: Measuring Language Model Utility When Errors Have Different Costs
arXiv:2610.25924v1 Announce Type: cross Abstract: As language models are increasingly deployed in real-world scenarios with important outcomes, the impact of their mistakes often extends far beyond what correctness can c…
- arXiv cs.AIresearchKeep the Lineages Alive: Diversity-Preserving SMC for Diffusion LLM Decoding
arXiv:2610.25932v1 Announce Type: cross Abstract: Inference-time sequential Monte Carlo (SMC) decoding can improve the reasoning accuracy of masked diffusion language models (diffusion LLMs) without post-training by resa…
- arXiv cs.AIresearchThe KV-Surgeon: Compensation-Aware Offline KV-Cache Compaction
arXiv:2610.25933v1 Announce Type: cross Abstract: Offline compaction of the key--value cache (KV-cache) reduces the cost of repeatedly querying a fixed context by constructing a smaller cache once and reusing it subseque…
- arXiv cs.AIagentsPolicy Search in Code Space for Multi-Agent Pathfinding
arXiv:2610.25946v2 Announce Type: replace-cross Abstract: Developing coordination policies for complex multi-agent systems relies on either labor-intensive algorithm design or opaque task-specific reinforcement learning.…
- arXiv cs.AIagentsAdaExploreBench: Benchmarking LLM Agents for Proactive Exploration and Test-time Adaptation in Evolving Environments
arXiv:2610.25950v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly deployed in unfamiliar and evolving environments, requiring them to proactively explore, understand, and adapt to envir…
- arXiv cs.AIresearchPaceVLA: Adaptive Temporal Action Quantization in Vision-Language-Action Models
arXiv:2610.25961v2 Announce Type: replace-cross Abstract: Fast and reliable manipulation is crucial for practical robot deployment. Although Vision-Language-Action (VLA) models have recently shown remarkable progress as…
- arXiv cs.AIresearchBehavioral Probes for Information Flow in LLM Swarms
arXiv:2610.25966v1 Announce Type: cross Abstract: Most existing approaches to LLM swarm optimization are utility-driven, focusing on final task utility while leaving the processing, integration, and reliance of informati…
- arXiv cs.AIresearchReconstruction and Discrete-Event Simulation Assessment of an Analytical
arXiv:2610.25967v1 Announce Type: cross Abstract: This article presents an analytical-DES benchmark for intra-aisle robotic picking in automated warehousing. The investigated cycle combines horizontal shuttle travel, tot…
- arXiv cs.AIresearchCorrectness-Dependent Asymmetric Target Distributions for Self-Distillation Policy Optimization
arXiv:2610.25973v1 Announce Type: cross Abstract: Self-distillation policy optimization provides dense token-level supervision for LLM post-training through feedback-conditioned self-teachers. However, directly matching…
- arXiv cs.AIresearchAuditor-Assisted Summary-Channel Verification for Hosted LLM Identity Substitution
arXiv:2610.25982v1 Announce Type: cross Abstract: Hosted LLM services increasingly expose model identity as a product claim, but external users and auditors often cannot inspect which model configuration served a request…
- arXiv cs.AIagentsNIOCIS: Typed-Decision Next-Hop Routing with Node-Conditioned Option Spaces for Tool-Use Agents
arXiv:2610.25985v1 Announce Type: cross Abstract: Tool-use agents pay per decision: at each hop a language model re-reads the state and thinks for seconds, though its actions are finite and decisions recur. We present NI…
- arXiv cs.AIresearchAccelerated Drift of Scientific Prose in the LLM Era: Evidence from an Interrupted Time-Series Analysis
arXiv:2610.26008v1 Announce Type: cross Abstract: Every scientific text is embedded in the writing norms of its time: it reflects earlier publications while also shaping the texts that follow. The growing use of LLM-base…
- arXiv cs.AIresearchFrameGRPO: Incentivizing Video Reasoning via Multi-Frame Rollouts and Temporal Credit
arXiv:2610.26022v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards, particularly Group Relative Policy Optimization (GRPO), has improved video reasoning in multimodal large language models.…
- arXiv cs.AIresearchMultimodal Decoupling for Diversified Recommendation through Semantic and Structural Shaping
arXiv:2610.26024v1 Announce Type: cross Abstract: Recommender systems often suffer from over-concentrated results when optimized for accuracy, limiting recommendation diversity. While diversity has become an important ob…
- arXiv cs.AIresearchTamedFlow: Trajectory-Aware Distillation for Teacher-Assisted Diffusion Sampling
arXiv:2610.26025v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) deliver high-fidelity image and video generation, but inference requires many sequential model evaluations. Two acceleration strateg…
- arXiv cs.AIresearchHoneyed Words, Hidden Daggers: Benchmarking and Training LLMs to See Through Mock Politeness
arXiv:2610.26059v1 Announce Type: cross Abstract: The Chinese idiom "kou mi fu jian" ("honeyed words, hidden daggers") names speech that is polite in form but hostile in function. We present ZhenhuanBench, a 1,592-item b…
- arXiv cs.AIresearchNot All Similarity Is Leakage: Distribution-Aware Identity Leakage Assessment in Synthetic Face Datasets
arXiv:2610.26077v1 Announce Type: cross Abstract: Synthetic data provides the means for more ethical and less biased facial recognition. Nonetheless, the limitations of generative models and the conditioning process they…
- arXiv cs.AIresearchHow Robust Are Prognostic Radiomic Features? Comparing Selection Techniques on Pooled Glioblastoma Cohorts
arXiv:2610.26095v1 Announce Type: cross Abstract: Radiomic survival studies in glioblastoma report heterogeneous prognostic features, but how much of this stems from the methodology itself is rarely examined. We analysed…
- arXiv cs.AIresearchDon't Rewrite the SID Map: Topology Preservation for Generative Retrieval in Dynamic E-Commerce Search
arXiv:2610.26105v1 Announce Type: cross Abstract: Generative retrieval assigns each item a discrete semantic identifier (SID) and retrieves items by autoregressively generating SID sequences. In dynamic environments such…
- arXiv cs.AIresearchNash-MOPD: Optimizing Task Trade-offs in Multi-Teacher On-Policy Distillation
arXiv:2610.26124v1 Announce Type: cross Abstract: Multi-teacher on-policy distillation (MOPD) aggregates the capabilities of multiple domain-expert teachers into a single student model. However, all domains in MOPD share…
- arXiv cs.AIresearchVEILED-SH: Vulnerability Expression through Indirect Language Evaluation Dataset (Self-Harm)
arXiv:2610.26190v2 Announce Type: replace-cross Abstract: AI systems may miss indirect, culturally situated distress or provide support mismatched to the application context. Existing benchmarks do not jointly address cu…
- arXiv cs.AIresearchGeoEdit: Adversarial De-Evidencing for Silent Geoprivacy Protection via Image Editing
arXiv:2610.26194v1 Announce Type: cross Abstract: Recent efforts have demonstrated the strong capabilities of Multimodal Large Reasoning Models (MLRMs) in geographic reasoning. By jointly analyzing subtle visual cues, th…
- arXiv cs.AIresearchPhysics-anchored residual quantum neural networks for cloud-cover parameterization
arXiv:2610.26279v1 Announce Type: cross Abstract: Compact quantum neural networks have been explored for cloud-cover parameterization, but their predictive value depends on how physical knowledge is incorporated and whic…
- arXiv cs.AIresearchToward Online Robust Zero-Sum Markov Games with Function Approximation
arXiv:2610.26286v1 Announce Type: cross Abstract: Online robust zero-sum Markov games pose a coupled operator-learning and game-solving problem: from nominal interaction data, the learner must recover a worst-case minima…
- arXiv cs.AIresearchRobotic Photography System based on Vision-Language-Action Model
arXiv:2610.26287v1 Announce Type: cross Abstract: Modern robotic photography requires understanding photographic intent, planning camera motions, and producing aesthetically pleasing images in complex environments. We pr…
- arXiv cs.AIresearchMCDA: Multi-View Graph Contrastive Learning with Dynamic Topology Augmentation for Functional MRI Analysis
arXiv:2610.26322v1 Announce Type: cross Abstract: Resting-state fMRI (rs-fMRI) is a vital non-invasive tool for diagnosing brain disorders and uncovering pathological mechanisms. However, existing graph contrastive learn…
- arXiv cs.AIresearchCIS-Transolver: A Cross-Layer Incremental-State Neural Operator for Fine-Scale Automotive Aerodynamic Flow Prediction
arXiv:2610.26337v1 Announce Type: cross Abstract: Neural operators offer an efficient data-driven approach to rapid prediction of complex automotive aerodynamic flows. However, spectral bias and other factors limit accur…