AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchGenDSR: Transferring Representations from Video Generation Models for Dynamic Spatial Reasoning
arXiv:2610.41314v1 Announce Type: cross Abstract: Dynamic spatial reasoning is essential for connecting visual intelligence to thephysical world, yet remains challenging for vision-language models (VLMs), which must pres…
- arXiv cs.AIresearchTransferring Peptide-Target Interaction Patterns to Small Molecules by Joint Guidance for Diffusion and Flow Matching
arXiv:2610.41322v1 Announce Type: cross Abstract: Generating small molecules to target protein-protein interactions (PPIs) remains one of the most challenging yet high-reward frontiers in drug discovery. Unlike tradition…
- arXiv cs.AIresearchWith foundation-model features, aggregator complexity does not improve cross-hospital discrimination in gastric whole-slide image classification
arXiv:2610.41328v1 Announce Type: cross Abstract: Whole-slide image classifiers are usually developed within one hospital and often go untested at unseen hospitals. We assembled 5,036 haematoxylin-and-eosin-stained gastr…
- arXiv cs.AIresearchPinching Antennas for ISAC: Waveguide Architectures, Solutions, and Future Directions
arXiv:2610.41380v1 Announce Type: cross Abstract: Integrated sensing and communication (ISAC) is a core design principle for sixth-generation (6G) wireless systems, yet fixed antenna geometry limits its performance. Pinc…
- arXiv cs.AIresearchFrom prediction error to sparse events: Biologically inspired signed-dyadic coding for Mandarin keyword spotting
arXiv:2610.41381v1 Announce Type: cross Abstract: In continuous spiking keyword spotting, temporally correlated acoustic frames can repeatedly trigger memory access and neural computation at a dense input interface, even…
- arXiv cs.AIresearchProSearch: Benchmarking Multi-Constraint Protocol Retrieval in Experimental Science
arXiv:2610.41396v1 Announce Type: cross Abstract: In AI for Science scenarios, experimental design and method reproduction require retrieving protocols that satisfy multiple interdependent constraints, yet existing bench…
- arXiv cs.AIresearchDistilling Knowledge from Search: Decoder Model Factory for Quantum LDPC Codes
arXiv:2610.41398v1 Announce Type: cross Abstract: We introduce GraphMP, a learning-based message-passing decoder for quantum low-density parity-check codes that transfers without fine-tuning across code families, code si…
- arXiv cs.AIresearchSparseGDN: Chunk Sparse in Linear Attention
arXiv:2610.41420v1 Announce Type: cross Abstract: Gated Delta Networks (GDNs) offer linear-time sequence modeling, yet long-context prefill remains costly due to dense intra-chunk computation, recurrent-state updates, an…
- arXiv cs.AIresearchFrom Representation to Readout: Mechanistic Analysis of Membership Signals in Language Models
arXiv:2610.41445v1 Announce Type: cross Abstract: Membership inference aims to determine whether a particular example was included in a model's training data. Such membership signals are often broadly described as memori…
- arXiv cs.AIresearchSparse Text Diffusion under Dual Budgets: Context Connectivity and Expert Activation
arXiv:2610.41487v1 Announce Type: cross Abstract: Long-context diffusion repeatedly pays for both token interaction and feed-forward computation. We formulate these costs as two separate budgets: the edges available to a…
- arXiv cs.AIresearchORION: An Outcome-Driven LLM Advisor for RL-Based Multi-Domain 6G Slice Placement
arXiv:2610.41563v1 Announce Type: cross Abstract: Deploying 6G network slices across administrative domains requires an online decision for each arriving request: which domain hosts each virtual network function, how tra…
- arXiv cs.AIresearchVLA Policies Do Not Know When Not to Act: Unintended Task Execution and Start Suppression
arXiv:2610.41609v1 Announce Type: cross Abstract: A robot should act only on an instruction from a person or on an intent that the robot explicitly represents and generates. Unintended actions taken without such an instr…
- arXiv cs.AIresearchAttention Maps Know When to Think: Training-Free Difficulty-Aware Chain-of-Thought for Reasoning Segmentation
arXiv:2610.41618v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting has become a standard reasoning strategy in Multi-modal Large Language Models (MLLMs), showing strong performance in complex vision-langu…
- arXiv cs.AIresearchGRIT: Gradient Masking for Visually Misgrounded Tokens in Text-Rich Reasoning
arXiv:2610.41629v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has recently been extended to vision-language models (VLMs), yet its effectiveness remains limited for text-rich rea…
- arXiv cs.AIresearchWhen Steering Shifts Discourse: Identifying and Intervening on Discourse-Level Changes due to Activation Steering in LLMs
arXiv:2610.41661v1 Announce Type: cross Abstract: Activation steering is an inference-time technique used to control or modify the behavior, tone, or safety properties of a large language model (LLM) without retraining t…
- arXiv cs.AIresearchOT-CAL: Optimal-Transport-Guided Adaptive Sample Selection for Efficient Visual Learning
arXiv:2610.41677v1 Announce Type: cross Abstract: Deep neural networks for image recognition are typically trained using mini-batches, with a forward pass to compute predictions and losses followed by a more computationa…
- arXiv cs.AIresearchWhen the Past Shapes Action: Understanding Embodied Memory
arXiv:2610.41688v1 Announce Type: cross Abstract: Recent advances in generative foundation models have expanded the capabilities of embodied AI, enabling increasingly general navigation, manipulation, and long-horizon ta…
- arXiv cs.AIresearchSimple Features Beat Most AI Control Monitors
arXiv:2610.41713v1 Announce Type: cross Abstract: AI control relies on monitors to catch a capable model that sabotages its work. These monitors are evaluated on their ability to differentiate honest from backdoored code…
- arXiv cs.AIresearchDifferent Models, Shared Failures: Auditing Frozen-Encoder Pools for Medical Segmentation
arXiv:2610.41790v1 Announce Type: cross Abstract: Average segmentation accuracy and model count do not reveal whether models fail on the same cases. We present CoFailMed, an audit of correlated failures in pretrained-enc…
- arXiv cs.AIresearchImplicit Assumptions About Tasks in Continual Learning
arXiv:2610.41792v1 Announce Type: cross Abstract: Continual learning is the study of how a model accumulates knowledge from data received over time, rather than all at once. In practice, this is typically achieved by div…
- arXiv cs.AIresearchAccurate and Auditable ECG Classification via Visibility Graph Neural Networks
arXiv:2610.41795v1 Announce Type: cross Abstract: Physicians read electrocardiograms (ECGs) from waveform shape and timing, yet accurate deep-learning classifiers rarely show which waveform regions drive their prediction…
- arXiv cs.AIresearchBackpropagation-free Deep Learning for Time-Series Classification
arXiv:2610.41805v1 Announce Type: cross Abstract: Deep neural networks for time series classification (TSC) are trained almost exclusively by error backpropagation, which imposes update locking and the weight-transport p…
- arXiv cs.AIresearchAI evaluators favor job applications written by certain AI models, independent of candidate quality
arXiv:2610.41866v1 Announce Type: cross Abstract: Job applicants increasingly use large language models (LLMs) to write cover letters, while employers deploy LLMs to screen them. In this all-AI setting, we ask whether th…
- arXiv cs.AIresearchGeneral-purpose foundation models surpass specialized ones on chemical property prediction tasks
arXiv:2610.41899v1 Announce Type: cross Abstract: Foundation models (FMs) learn reusable representations from large collections of unlabeled data. In chemistry the corresponding data mainly consists of molecular structur…
- arXiv cs.AIresearchObservable-Specific Validity Tests for Scientific AI: Controlled Approximations and Intervention Response
arXiv:2610.41939v1 Announce Type: cross Abstract: An approximation can reproduce an energy while missing its response, or satisfy an error tolerance without being certified by a given bound. We formulate observable-speci…
- arXiv cs.AIresearchAligning MLLMs with the Latent Structure of Human Cognition via Behavior-Derived Semantic Dimensions
arXiv:2610.41951v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved impressive performance across a wide range of reasoning tasks, yet it remains unclear whether their internal repres…
- arXiv cs.AIresearchChronos-2: A foundation model for multivariate time series forecasting
arXiv:2610.41956v1 Announce Type: cross Abstract: Accurate forecasting with uncertainty quantification is critical in a variety of scientific applications ranging from epidemiology and economics to energy systems and ear…
- arXiv cs.AIresearchAutoAnalysis: An automated, self-updating meta-analysis of human-AI collaboration
arXiv:2610.42001v1 Announce Type: cross Abstract: As generative artificial intelligence (AI) becomes more capable and widely used, understanding when and how humans should work with these systems is becoming increasingly…
- arXiv cs.AIresearchKnow When the Equation Is Wrong: Structural Uncertainty for LLM-Guided Symbolic Regression under Extrapolation
arXiv:2610.42024v1 Announce Type: cross Abstract: Large language models (LLMs) can help discover mathematical equations from data, but most LLM-guided symbolic regression methods focus on a single best equation without q…
- arXiv cs.AIresearchThere Is No Free Loop: When Does Looping a Trained Model Pay for Its Inference Compute?
arXiv:2610.42030v1 Announce Type: cross Abstract: Looping re-applies a block of layers so that each token passes through the block more than once. Adding a loop to a trained model is attractive because it leaves the para…
- arXiv cs.AIresearchLearning Clean Generative Processes from Noisy Measurements
arXiv:2610.42036v1 Announce Type: cross Abstract: We study how to learn a generative model of clean images using only noisy observations. Diffusion processes and their generalizations to non-Gaussian noise require poster…
- arXiv cs.AIresearchNear-Optimal Second-Order Convex Finite-Sum Optimization under Individual Smoothness
arXiv:2610.42040v1 Announce Type: cross Abstract: We study the stochastic second-order oracle complexity of solving smooth convex finite-sum optimization with $n$ components. Very recently, Tsedenov \& Kovalev (2026) sho…
- arXiv cs.AIresearchCRoS: Causality Reward on Scrambled CoTs Improves Accuracy and Reasoning Faithfulness
arXiv:2610.42054v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become the standard method for training language models to reason. However, RLVR typically relies solely on outc…
- arXiv cs.AIresearchSubquadratic Attention with a Hierarchical Tree-Structured Indexer
arXiv:2610.42112v1 Announce Type: cross Abstract: The quadratic scaling of attention is a fundamental bottleneck limiting the long-context capabilities of large language models (LLMs). A variety of approaches have attemp…
- arXiv cs.AIresearchShared Modular Recurrence in Contextual MDPs for Universal Morphology Control
arXiv:2506.08630v4 Announce Type: replace Abstract: A universal controller for any robot morphology would greatly improve computational and data efficiency. Steps have been made towards such multi-robot control by utiliz…
- arXiv cs.AIresearchA Hybrid SMT-NRA Solver: Integrating 2D Cell-Jump-Based Local Search, MCSAT and OpenCAD
arXiv:2507.00557v3 Announce Type: replace Abstract: In this paper, we propose a hybrid framework for Satisfiability Modulo the Theory of Nonlinear Real Arithmetic (SMT-NRA for short). First, we introduce a two-dimensiona…
- arXiv cs.AIresearchThinkSafe: Self-Generated Safety Alignment for Reasoning Models
arXiv:2601.23143v5 Announce Type: replace Abstract: Large reasoning models achieve strong performance through reinforcement learning but can suffer safety degradation. External teacher distillation can restore safety yet…
- arXiv cs.AIresearchTeaching Large Language Models When Not to Know: Learning Temporal Critique for Ex-Ante Reasoning
arXiv:2605.14636v2 Announce Type: replace Abstract: Large language models (LLMs) often fail to reason under temporal cutoffs: when prompted to answer from the standpoint of an earlier time, they exploit knowledge that be…
- arXiv cs.AIresearchGENSTRAT: Toward a Science of Strategic Reasoning in Large Language Models
arXiv:2605.23238v2 Announce Type: replace Abstract: Fixed game suites for evaluating strategic reasoning in large language models (LLMs) saturate and are hard to keep free of contamination. We introduce GENSTRAT, a syste…
- arXiv cs.AIresearchFedPA-LoRA: Product-Aligned Framework for Mitigating Aggregation and Initialization Errors in Heterogeneous Federated LoRA
arXiv:2608.15381v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) enables efficient federated fine-tuning of large language models, but its factorization creates a tension between aggregation fidelity and lo…
- arXiv cs.AIresearchFrom Association to Causation: Improving Retrieval Precision of Retrieval-Augmented Generation via Causal Relations and an Attention Mechanism
arXiv:2608.21702v2 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) grounds LLM generation on retrieved documents, but the standard terminal retrieval stage--dense-vector similarity, optionally follo…
- arXiv cs.AIresearchSIRF: A Spec-Internalized Risk Foundation Model for Industrial Content Risk Control
arXiv:2609.11752v2 Announce Type: replace Abstract: For industrial content risk control, the real deployment constraint is not average accuracy but how much risk can be auto-handled under high precision and second-level…
- arXiv cs.AIresearchA Unified Per-Token Gating Family for On-Policy Distillation: FKL/RKL Mixing with Multi-Channel and Bias Coefficients
arXiv:2609.11768v2 Announce Type: replace Abstract: Per-token gating of forward/reverse KL losses has become a standard technique for on-policy knowledge distillation (OPD), but existing methods such as EOPD (Jin et al.,…
- arXiv cs.AIresearchTime Will Tell: Harness Time to Denoise Collaborative Filtering
arXiv:2411.09181v2 Announce Type: replace-cross Abstract: Neural graph collaborative filtering is a powerful tool for recommendation due to its superb capability of capturing the user-item interaction. Despite the substa…
- arXiv cs.AIresearchIntegrating AI for Human-Centric Breast Cancer Diagnostics: A Multi-Scale and Multi-View Swin Transformer Framework
arXiv:2503.13309v3 Announce Type: replace-cross Abstract: Despite advancements in Computer-Aided Diagnosis (CAD) systems, breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. Re…
- arXiv cs.AIresearchBeyond Public Access in LLM Pre-Training Data
arXiv:2505.00020v3 Announce Type: replace-cross Abstract: Using a legally obtained dataset of 34 copyrighted O'Reilly Media books (13,962 paragraphs), we apply the DE-COP membership inference attack to test whether OpenA…
- arXiv cs.AIresearchOptimizing Anytime Reasoning via Budget Relative Policy Optimization
arXiv:2505.13438v4 Announce Type: replace-cross Abstract: Scaling test-time compute is crucial for enhancing the reasoning capabilities of large language models (LLMs). Existing approaches typically employ reinforcement…
- arXiv cs.AIresearchLLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
arXiv:2505.14756v3 Announce Type: replace-cross Abstract: Bayesian optimization (BO) is a sequential decision-making tool widely used for optimizing expensive black-box functions. Recently, Large Language Models (LLMs) h…
- arXiv cs.AIresearchFully Offline Reinforcement Learning
arXiv:2505.22442v4 Announce Type: replace-cross Abstract: Offline RL (ORL) promises safe and sample-efficient deployment but existing methods rely on undocumented online interactions for hyperparameter tuning and lack re…
- arXiv cs.AIresearchVisualSphinx: Large-Scale Synthetic Vision Logic Puzzles for RL
arXiv:2505.23977v2 Announce Type: replace-cross Abstract: Vision language models (VLMs) are expected to perform effective multimodal reasoning and make logically coherent decisions, which is critical to tasks such as dia…