AI news archive.
10,661 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchBreaking the Illusion of Review Reliability under Static Evaluation: SCOPE Fuzzing for LLM-based Scientific Reviewers
arXiv:2609.37097v1 Announce Type: new Abstract: The rapid growth of submissions and reviewing workload has accelerated the use of large language models (LLMs) in peer review. Prior studies suggest that LLM-based reviewer…
- arXiv cs.AIresearchSkillCome: Group Contrast Skill Optimization with Dual Memory
arXiv:2609.37128v1 Announce Type: new Abstract: Skill evolution improves the capabilities of large language models by analyzing trajectories generated under a given skill and modifying the skill accordingly. Existing app…
- arXiv cs.AIresearchTrain Ahead, Distill Back: Bootstrapping On-Policy Self-Distillation for Large Language Models
arXiv:2609.37132v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) improves large language models by letting a self-teacher with privileged information provide dense token-level supervision on the model's…
- arXiv cs.AIresearchFrom Judgment Quality to Downstream Utility: Rethinking LLM-as-a-Judge for Open-Ended Tasks
arXiv:2609.37145v1 Announce Type: new Abstract: LLM-as-a-Judge is increasingly used to evaluate policy responses on open-ended tasks that lack ground-truth answers. Existing work often directly converts the resulting jud…
- arXiv cs.AIresearchFrom Learner Behavior to Reusable Skills for Effective and Efficient Learner Simulation
arXiv:2609.37157v2 Announce Type: replace Abstract: Learner simulation aims to reproduce how a particular learner behaves on new tasks. Although Large Language Models (LLMs) can generate increasingly fine-grained learnin…
- arXiv cs.AIresearchAccelerated surrogate dynamics for dynamical, stochastic system evolution
arXiv:2609.37184v1 Announce Type: new Abstract: Dynamic simulations are an entrenched way of gaining insight into the evolution of system dynamics. Their computational cost however is often prohibitively high, especially…
- arXiv cs.AIresearchV-Engram: Trigger-Indexed External Memory for Modular Text-to-Image Personalization
arXiv:2609.37198v1 Announce Type: new Abstract: Pretrained text-to-image models contain broad visual knowledge, yet they cannot reliably acquire or refine a specific visual identity from only a few references while prese…
- arXiv cs.AIresearchLearning to Prove, Not Just to Answer: Reinforcement Learning from Formal Verification for Natural-Language Logical Reasoning
arXiv:2609.37203v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed for natural-language logical reasoning, where the final answer is easy to check but the proof behind it is not. In na…
- arXiv cs.AIresearchInformation Bottleneck-Guided Adaptive Hypergraph Transformer for Brain Disease Diagnosis
arXiv:2609.37220v1 Announce Type: new Abstract: Exploring high-order correlations and long-range dependencies in brain networks holds significant value for both neuroscience research and clinical diagnosis. However, prev…
- arXiv cs.AIresearchOptiCom : A Unified Framework for State-Conditioned Composition in LLM-Driven Optimization
arXiv:2609.37221v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed to solve complex scientific and practical problems via iterative optimization. However, dynamically coordinating…
- arXiv cs.AIresearchTask-Relevant Null-Space Residuals for Non-Injective Neural Mappings
arXiv:2609.37272v1 Announce Type: new Abstract: Non-injective mappings in neural networks map distinct inputs to the same representation, thereby implicitly inducing equivalence relations in the input space. However, the…
- arXiv cs.AIresearchTransolver-$\sigma$: Joint Spectral-Physical Subspace Modeling for Neural PDE Solving
arXiv:2609.37279v1 Announce Type: new Abstract: Neural solvers offer efficient surrogates for numerical simulation of partial differential equations (PDEs). For time-dependent problems, strong one-step accuracy does not…
- arXiv cs.AIresearchAssayRouter: Historical Utility Priors for Frozen Molecular Predictor Routing
arXiv:2609.37285v1 Announce Type: new Abstract: Laboratories often face a new molecular assay with 16-64 labels and a bank of predictors whose training data and parameters are unavailable. The practical question is which…
- arXiv cs.AIresearchMetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller
arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not always lead to bette…
- arXiv cs.AIresearchMubric: Mutation Testing-Guided Rubric Generation for LLM Evaluation
arXiv:2609.37322v1 Announce Type: new Abstract: Rubric-based evaluation is widely used to assess LLM-based systems by decomposing response quality into task-specific scoring criteria. However, automatically generating ru…
- arXiv cs.AIresearchVISTA: Value-Informed Event Appraisal for Multimodal Emotion Conflict
arXiv:2609.37324v1 Announce Type: new Abstract: Conflicting emotional cues can be individually valid: a subdued voice may reflect a blocked goal while a smile satisfies a social obligation. Their interpretation depends o…
- arXiv cs.AIresearchSolving Without Stopping: On-Policy Distillation at Small Scale
arXiv:2609.37326v1 Announce Type: new Abstract: On-policy distillation, where a student learns from a stronger teacher's feedback on its own outputs, is a common way to pass reasoning to smaller models. We analyze what i…
- arXiv cs.AIresearchTeaching LLMs to Generate Challenging MILP Instances via Solver Feedback
arXiv:2609.37356v1 Announce Type: new Abstract: Generating optimization instances that are both feasible and computationally challenging is crucial for benchmarking solvers and training learning-based optimization algori…
- arXiv cs.AIresearchPretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing
arXiv:2609.37362v1 Announce Type: new Abstract: Large language model (LLM) routing aims to assign each query to the most suitable model from a heterogeneous candidate pool, improving the quality--efficiency trade-off of…
- arXiv cs.AIresearchBeyond Prompt Count: How Data Shapes Transfer in On-Policy Distillation
arXiv:2609.37377v1 Announce Type: new Abstract: On-policy distillation (OPD) trains students using teacher feedback on their own sampled responses, yet how prompt choice shapes transfer across teacher-student pairs remai…
- arXiv cs.AIresearchDirect Experience World-Model Optimization: Learning the World Beyond Action Imitation
arXiv:2609.37398v1 Announce Type: new Abstract: World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold. However, current post-deployment learning paradigms typically impr…
- arXiv cs.AIresearchRouting Should Pay for Itself: Sparse Supervision for Economical LLM Routing
arXiv:2609.37402v1 Announce Type: new Abstract: Large language model (LLM) routing reduces serving cost by assigning each query to an appropriate model while preserving response quality. Learning such a router, however,…
- arXiv cs.AIresearchDemistifying Data and Simulator Assumptions in Supervised Causal Discovery
arXiv:2609.37446v1 Announce Type: new Abstract: Supervised causal discovery learns to infer causal structure for a new dataset from training datasets paired with structural labels. These training pairs are typically simu…
- arXiv cs.AIresearchCRASM-Gate: Deterministic-First Constraint- and Role-Aware Semantic Mapping with Selective Model Assistance Across Heterogeneous Industrial Standards
arXiv:2609.37458v1 Announce Type: new Abstract: Industrial standards often encode the same engineering concept through incompatible hierarchies, identifiers, roles, and structural constraints, so the nearest lexical or e…
- arXiv cs.AIresearchAuthority Before Utility: Non-Compensatory Control for Persistent LLM Memory
arXiv:2609.37474v1 Announce Type: new Abstract: Persistent memory creates a control problem that retrieval relevance alone does not solve: a memory can remain highly useful after an update, deletion, or revocation makes…
- arXiv cs.AIresearchXU-RS: Explaining Credal Width in Random-Set Language Models
arXiv:2609.37594v1 Announce Type: new Abstract: Uncertainty estimates tell us how unsure a model is, but not why. Without knowing which parts of an input influences a model's uncertainty, we cannot tell whether that unce…
- arXiv cs.AIresearchFlattening the Connectome Spectrum: A Spectral Filter for FC Induces a Pretraining Target for fMRI Encoders
arXiv:2609.37642v1 Announce Type: new Abstract: Self-supervised pretraining reshaped prediction in language and vision, and brain foundation models (BFMs) inherited its promise. Representations learned from large unlabel…
- arXiv cs.AIresearchBeyond a single latent space: a dual-latent world model for long-horizon planning
arXiv:2609.37644v1 Announce Type: new Abstract: Latent world models often struggle with long-horizon planning despite accurate short-term predictions. Recursive rollouts accumulate errors, while distance concentration in…
- arXiv cs.AIresearchMeanFlowAdvantage: Stable Reward Fine-Tuning for Few-Step Average-Velocity Generators
arXiv:2609.37670v1 Announce Type: new Abstract: MeanFlow enables efficient few-step generation by predicting interval-average velocities, but this representation creates a mismatch for reward fine-tuning: existing advant…
- arXiv cs.AIresearchLearning from Shared-Control Overrides: Context-Driven Acceleration Profile Prediction for Personalized Overtaking
arXiv:2609.37684v1 Announce Type: new Abstract: Adaptive Cruise Control (ACC) systems are typically calibrated for an average driver, often resulting in a mismatch between vehicle behavior and individual expectations dur…
- arXiv cs.AIresearchWISE-ATTA: When to Ask for Labels in Budgeted Active Test-Time Adaptation
arXiv:2609.37687v2 Announce Type: replace Abstract: Active test-time adaptation (ATTA) improves robustness under distribution shift by updating a deployed model during inference while selectively querying supervision. Ho…
- arXiv cs.AIresearchLocating Answer-Correctness Signals in Frozen Large Language Models
arXiv:2609.37700v1 Announce Type: new Abstract: Language models expose internal signals that predict whether an answer is correct, readable from a single forward pass of a frozen model without additional generations. Yet…
- arXiv cs.AIresearchGenerative Interactions: Weaving Multiparty Human Motion with Bilevel Latent Dynamics
arXiv:2609.37708v2 Announce Type: replace Abstract: Human social behaviour is not a collection of independent motions, but a jointly organised process in which group dynamics and individual variation continuously shape o…
- arXiv cs.AIresearchContext Language Models
arXiv:2609.37725v1 Announce Type: new Abstract: We introduce Context Language Models (CLMs), language models that natively manage their own context. We implement this by treating the context as a file and allowing the mo…
- arXiv cs.AIresearchSpatiotemporal Hyperedges for EEG Seizure Detection and Prediction
arXiv:2609.37730v1 Announce Type: new Abstract: Seizure detection and prediction from EEG are clinically important but challenging because seizures are rare, temporally localized, and propagate as coordinated events acro…
- arXiv cs.AIresearchCross-Entropy Guided Routing in Mixture-of-Experts Large Language Models
arXiv:2609.37751v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) large language models scale model capacity by routing each token to a small subset of experts. Their routers are regularized with load balan…
- arXiv cs.AIresearchAdam under Generalized Smoothness with Second-Moment-Type Stochastic Gradients
arXiv:2609.37787v2 Announce Type: replace Abstract: Adam is widely observed to remain stable even when the objective deviates significantly from global smoothness. Under the generalized smoothness framework, however, exi…
- arXiv cs.AIresearchA neural network that maintains and retrieves memories based on context
arXiv:2609.37791v1 Announce Type: new Abstract: Every day, people continuously infer situational context and adjust the way they understand and remember the world. Context, signaled by the prefrontal cortex, is known to…
- arXiv cs.AIresearchDIET: Deletion-response Expert Trimming for Video Diffusion Transformers
arXiv:2609.37829v1 Announce Type: new Abstract: Video diffusion transformers (DiTs) increasingly adopt mixture-of-experts (MoE) architectures to reduce active computation, but their full expert storage remains costly. Ex…
- arXiv cs.AIresearchCan a Cacheable Decision Model Follow Rules?
arXiv:2609.37832v1 Announce Type: new Abstract: Certo is a small non-generative decision model (Qwen3-4B): it scores candidate actions from their text and returns a probability, instead of generating an answer. The accur…
- arXiv cs.AIresearchActive Budget Can Kill Sensitivity: Diagnosing and Repairing TopK Sparse Autoencoder Reliability
arXiv:2609.37857v2 Announce Type: replace Abstract: Sparse autoencoders (SAEs) are increasingly scaled to wider dictionaries to recover fine-grained structure from large language model activations. However, a feature is…
- arXiv cs.AIresearchCo-PiLOT: Constrained Physics-Informed Latent Optimization for Target-Driven Inverse Design
arXiv:2609.37875v1 Announce Type: new Abstract: Inverse design of physical systems (molecules, devices, microstructures) often reduces to optimizing a high-dimensional structure against an expensive black-box simulator.…
- arXiv cs.AIresearchYou Cannot Pick a Provider From the Price List: Market-Aware Routing for Open-Weight LLM Inference
arXiv:2609.37902v1 Announce Type: new Abstract: Existing LLM routers choose among models using static per-model costs. We show that open-weight inference markets introduce a second, largely ignored decision axis: after c…
- arXiv cs.AIresearchGRFBrain: Graph-Structured Rectified Flows for EEG Dynamic Modeling
arXiv:2609.37934v1 Announce Type: new Abstract: Forecasting time-varying functional connectivity from electroencephalography (EEG) requires modeling both history-dependent trends and structured variability across channel…
- arXiv cs.AIresearchBrainNet Studio: A Unified Toolkit for Brain Network Construction, Intelligent Analysis, and Visualization
arXiv:2609.37956v1 Announce Type: new Abstract: Brain networks characterize structural and functional relationships among brain regions and support research on cognition, brain disorders, and brain-computer interfaces. T…
- arXiv cs.AIresearchKV-Kaizen: Learning Context-Adaptive Cache Compression Choices
arXiv:2609.37988v2 Announce Type: replace Abstract: As the context size of text processed with an LLM grows, the size of KV caches can outstrip the memory allocated for the original model weights. This impacts LLM throug…
- arXiv cs.AIresearchWhich Attention Heads are like the Human Head? Not the Ones that Compute
arXiv:2609.37991v1 Announce Type: new Abstract: Brain-AI alignment is often interpreted as a sign that model and brain perform similar computations. Whether the aligned units are causally involved in model computation is…
- arXiv cs.AIresearchHARISSA: Inference-Time Self-Checks for Efficient and Safe Local Language Model Deployment
arXiv:2609.38006v1 Announce Type: new Abstract: Running a language model locally offers advantages in privacy, latency, and cost, but local hardware fits only small models, which are less capable than frontier models. Th…
- arXiv cs.AIresearchPE-EK-PINN: Physics Embedding with Evolving Kernel for Scalable Physics-Informed Neural Networks
arXiv:2609.38023v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) embed governing equations into deep learning, but enforce them only through loss residuals, leaving highly oscillatory wave behavio…
- arXiv cs.AIresearchProbability is Not Enough: Exploring and Counting Divergent Tokens for Reasoning Uncertainty Quantification in LLMs
arXiv:2609.38070v1 Announce Type: new Abstract: As the chain-of-thought reasoning capabilities of large language models improve, evaluating and calibrating their reasoning confidence is becoming increasingly important fo…