AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchMine, Sample, Render: Test-Time Diversity Alignment Of Generative Models
arXiv:2610.21185v1 Announce Type: new Abstract: Modern generative models produce high-quality individual outputs but struggle to sample diversely, even when explicitly instructed to do so. We introduce MINE-SAMPLE-RENDER…
- arXiv cs.AIresearchCondition-Contrastive Extrapolation for On-Policy Distillation of Language Models
arXiv:2610.21195v1 Announce Type: new Abstract: On-policy distillation (OPD) improves a student language model by training on its own generated trajectories with supervision from a stronger teacher. However, when conditi…
- arXiv cs.AIresearchEntertain, then Cancel: How Thinking Makes Reasoning Models Read Some and Or Literally
arXiv:2610.21204v1 Announce Type: new Abstract: When a speaker says ``some students passed'', listeners infer that not all of them did. Reasoning models now let users decide how long a model thinks before it answers, but…
- arXiv cs.AIresearchRubric Judgments Lack Uncertainty: Conformal Weighting for LLM Evaluation
arXiv:2610.21214v1 Announce Type: new Abstract: Rubric-based evaluation is widely used to assess large language model (LLM) outputs by decomposing quality into multiple criteria and assigning each a binary judgment from…
- arXiv cs.AIresearchThe Density of Thought: Predicting and Steering Reasoning in Large Language Models
arXiv:2610.21286v1 Announce Type: new Abstract: We investigate whether variation in information density across chain-of-thought (CoT) reasoning steps predicts answer correctness and can serve as a controllable inference-…
- arXiv cs.AIresearchMoney-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset
arXiv:2610.21314v1 Announce Type: new Abstract: Money-mule networks move criminal funds through many small transfers, each of which looks ordinary on its own. What marks an account as a mule lies outside any single trans…
- arXiv cs.AIresearchQuantum Versus Classical Machine Learning for Neuropsychiatric Disorders: A Feature-Matched Multi-Cohort Evaluation
arXiv:2610.21344v1 Announce Type: new Abstract: Resting-state functional connectivity is widely used in machine learning-based classification of neuropsychiatric disorders. Quantum machine learning models have been propo…
- arXiv cs.AIresearchHierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents
arXiv:2610.21350v1 Announce Type: new Abstract: Consistency within a document is a crucial feature indicative of its quality. Recently, within the vast amount of information produced across various media, there exists a…
- arXiv cs.AIresearchLearning to Reason with Selective Internal Feedback
arXiv:2610.21392v1 Announce Type: new Abstract: Reinforcement learning from internal feedback can improve language models without ground-truth answers or external verifiers. However, confidence and correctness can diverg…
- arXiv cs.AIresearchSelf-Guided LLM Reasoning for Error Diagnosis in Vehicle Routing Problems
arXiv:2610.21393v1 Announce Type: new Abstract: Vehicle Routing Problems (VRPs) are fundamental to many real-world applications. Recent advances in large language models (LLMs) have spurred progress in solving these NP-h…
- arXiv cs.AIresearchWatts If? A Benchmark for Counterfactual Reasoning About the AI Data Center Buildout
arXiv:2610.21397v1 Announce Type: new Abstract: An AI data center is committed years before it draws a watt: a few discrete choices (grid or on-site gas, evaporative or dry cooling, which county to file in) fix its capit…
- arXiv cs.AIresearchLearning from Interaction Experience via Localized Context Distillation
arXiv:2610.21427v1 Announce Type: new Abstract: Learning from interaction experience is essential for enabling large language models to continually improve during deployment. Context distillation offers a promising path:…
- arXiv cs.AIresearchIncentivizing Tool-Use Reinforcement Learning for Post-Arrival Emergency Care Prediction
arXiv:2610.21449v2 Announce Type: replace Abstract: Large language models (LLMs) can reason over both structured measurements and free-text records, which makes them attractive for emergency department (ED) prediction. H…
- arXiv cs.AIresearchOntology Refinement with Embeddings and LLMs: A Case Study on CASTON
arXiv:2610.21466v1 Announce Type: new Abstract: Ontologies are widely used to represent and organize complex knowledge in domains such as biomedicine and healthcare. However, constructing reliable ontologies remains chal…
- arXiv cs.AIresearchTool-Augmented Large Language Models for Reliable Medical Calculation from Patient Notes
arXiv:2610.21477v1 Announce Type: new Abstract: Medical calculators translate raw patient observations into clinically actionable values and scores. However, applying these calculators directly to free-form patient notes…
- arXiv cs.AIresearchAI-Driven Disaster Response and Displacement Monitoring
arXiv:2610.21482v1 Announce Type: new Abstract: ARTIFICIAL INTELLIGENCE (AI) has demonstrated its potential across a wide range of humanitarian tasks, offering scalable and data-driven solutions to some of the world's mo…
- arXiv cs.AIresearchFlashAttention with Statistics: Low-Overhead Attention Features for Compact Hallucination Detectors
arXiv:2610.21483v1 Announce Type: new Abstract: Large language models can produce fluent answers that the supplied context does not support. A compact frozen proxy model can detect such answers by reading the context and…
- arXiv cs.AIresearchDecomposing Brain-LLM Alignment with Interpretable Semantic Representations
arXiv:2610.21508v1 Announce Type: new Abstract: Large language models (LLMs) predict neural responses during naturalistic language comprehension, but the linguistic information underlying this brain alignment remains dif…
- arXiv cs.AIresearchSCRIBE: Scalable Contextual Representations of Interpretable Brain-Grounded Semantics
arXiv:2610.21518v1 Announce Type: new Abstract: Understanding how the human brain constructs meaning from real-world language remains a fundamental challenge, as semantics depend on context that varies in natural setting…
- arXiv cs.AIresearchFunctional Subnetwork Localization for Efficient Continual Learning in Large Language Models
arXiv:2610.21537v1 Announce Type: new Abstract: Biological intelligence continually adapts to new information while retaining prior skills through localized plasticity. In contrast, continual learning in large language m…
- arXiv cs.AIresearchReinforcement Learning-driven Diffusion Models: Language, Vision, and Beyond
arXiv:2610.21539v2 Announce Type: replace Abstract: Diffusion models (DMs) have steadily demonstrated outstanding performance in high-fidelity, controllable generation across diverse scenarios, following the "pretraining…
- arXiv cs.AIresearchLOADBench: A Large Open Analog Dataset and Benchmark for Machine Learning in IC Design
arXiv:2610.21591v2 Announce Type: replace Abstract: Machine learning (ML) for analog IC design is constrained by limited open data and the absence of standardized evaluation: results reported in different papers are typi…
- arXiv cs.AIresearchLoopAhead: Speculating Ahead for Faster Decoding in Looped Language Models
arXiv:2610.21594v1 Announce Type: new Abstract: Looped Transformers improve parameter efficiency by repeatedly applying the same Transformer layers. However, generating each token still involves sequential computation an…
- arXiv cs.AIresearchLearning Intrinsic Dynamics in Latent World Models
arXiv:2610.21612v1 Announce Type: new Abstract: Learning intrinsic dynamics---the relationship between actions and their consequences---is essential for world models. However, visual variations unrelated to the underlyin…
- arXiv cs.AIresearchIn-Context Preference-Conditioned Pareto Set Learning from Limited Offline Data
arXiv:2610.21736v1 Announce Type: new Abstract: Multi-objective optimization involves diverse preferences over conflicting objectives. Rather than returning a finite set of trade-off solutions, preference-conditioned Par…
- arXiv cs.AIresearchCo2In: a Bi-level Memory Incremental Learning Framework with Knowledge Encoding, Consolidation, and Integration
arXiv:2610.21804v1 Announce Type: new Abstract: Incremental learning (IL) aims to continually acquire new knowledge (plasticity) while retaining previously learned information (stability). However, striking a balance bet…
- arXiv cs.AIresearchCIM-Embed: Learning Instruction-Conditioned Multimodal Embeddings for Clustering
arXiv:2610.21805v1 Announce Type: new Abstract: Multimodal objects can require different partitions under different instructions, as when products in an e-commerce catalog are grouped by color, material, or both. Retriev…
- arXiv cs.AIresearchRoleQ: Role-Aligned Scales for Converting Language Models to Ternary
arXiv:2610.21808v1 Announce Type: new Abstract: Weight quantization below four bits shrinks memory and bandwidth at a perplexity that looks acceptable, but often at the cost of factual knowledge and task accuracy. Ternar…
- arXiv cs.AIresearchProgress Reward Model for Reinforcement Learning via Large Language Models
arXiv:2610.22056v1 Announce Type: new Abstract: Traditional reinforcement learning (RL) algorithms face significant limitations in handling long-term tasks with sparse rewards. Recent advancements have leveraged large la…
- arXiv cs.AIresearchPrism-1: Position- and Rival-Independent Scoring for System One Models
arXiv:2610.22066v2 Announce Type: replace Abstract: Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in one forward pass,…
- arXiv cs.AIresearchWhen Reasoning Is Not Enough: Proactive Inquiry for Cross-Modal Safety in Multimodal Large Language Models
arXiv:2610.22068v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can act unsafely when individually benign images and texts are interpreted jointly. Beyond implicit reasoning risk, where reasoning…
- arXiv cs.AIresearchLearning from Unverified Peers via Shared Residual Distillation
arXiv:2610.22080v1 Announce Type: new Abstract: A language model can generate several solutions to the same problem, some leading to correct answers and others to incorrect ones. Many post-training methods turn these tra…
- arXiv cs.AIresearchDropKV: Decoupling Residual-Output Perturbation for Near-Optimal KV-Cache Eviction
arXiv:2610.22130v1 Announce Type: new Abstract: Key-value (KV) cache management has become a critical bottleneck for scaling Large Language Models (LLMs) to long-context scenarios due to linear memory growth. While recen…
- arXiv cs.AIresearchCART: Compatible Routing for Multimodal Continual Instruction Tuning
arXiv:2610.22137v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance through instruction tuning, yet real-world applications often require them to continually acquire new ca…
- arXiv cs.AIresearchAre You Sure Your LLM Is Sure? Repairing and Auditing Confidence
arXiv:2610.22139v1 Announce Type: new Abstract: Confidence scores determine whether an LLM answer is accepted, reviewed, or regenerated, yet scalar recalibration can only correct errors visible through the score itself.…
- arXiv cs.AIresearchRAG-RST: Retrieved Exemplars as In-Context Discourse Schema for RST Parsing
arXiv:2610.22150v1 Announce Type: new Abstract: In Rhetorical Structure Theory (RST), each edge of a discourse tree carries a nuclearity label, marking which span holds the central content, and a relation drawn from a cl…
- arXiv cs.AIresearchMulti-sourced AI Tutor for Non-Native English Speaker Students
arXiv:2610.22163v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being adopted as teaching tools in computing education, particularly for developing AI tutors capable of answering student que…
- arXiv cs.AIresearchGNN-Guided Local Refinement for Efficient Layout Decomposition with DSA-MP
arXiv:2610.22214v1 Announce Type: new Abstract: Multiple patterning lithography (MPL) has been widely used to improve lithography resolution in advanced nodes, but it faces cost limits as feature sizes shrink. Directed s…
- arXiv cs.AIresearchDisagreement-Guided Weak-to-Strong Routing
arXiv:2610.22245v1 Announce Type: new Abstract: Small language models are increasingly deployed on edge devices and robotic systems to meet cost and latency constraints, yet their limited capabilities can make reliable d…
- arXiv cs.AIresearchEliteReplay: Preserving Diverse Solutions in RL Post-Training via Quality-Diversity Search
arXiv:2610.22290v1 Announce Type: new Abstract: Reinforcement learning has become a common post-training technique to improve the accuracy of LLMs on verifiable tasks. However, such training can narrow the set of solutio…
- arXiv cs.AIresearchPrivate Retrieval Augmented Generation via Random Projection
arXiv:2610.22297v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it can also introduce pri…
- arXiv cs.AIresearchDesigning Transformers for Continuous Diffusion Language Models
arXiv:2610.22330v1 Announce Type: new Abstract: Continuous diffusion language models (CDLMs) offer an alternative approach to parallel language generation; however, most existing studies directly use transformer architec…
- arXiv cs.AIresearchEvaluation Awareness Beyond English: Do LLMs Act Differently When Tested in Indic Languages?
arXiv:2610.22333v1 Announce Type: new Abstract: Large language models (LLMs) can often tell when they are being tested. This ability, known as evaluation awareness, can lead models to act more cautiously during safety ev…
- arXiv cs.AIresearchKULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context
arXiv:2412.07251v1 Announce Type: cross Abstract: Large language models have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models g…
- arXiv cs.AIresearchActive Learning of Fractional-Order Viscoelastic Model Parameters for Realistic Haptic Rendering
arXiv:2512.00667v2 Announce Type: cross Abstract: Effective medical simulators necessitate realistic haptic rendering of biological tissues that exhibit viscoelastic material properties, such as creep and stress relaxati…
- arXiv cs.AIresearchOne Is Not Enough: How People Use Multiple AI Models in Everyday Life
arXiv:2603.26107v1 Announce Type: cross Abstract: People increasingly use multiple Multimodal Large Language Models (MLLMs) concurrently, selecting each based on its perceived strengths. This cross-platform practice crea…
- arXiv cs.AIresearchBridging the Gap: Converting Read Text to Conversational Dialogue
arXiv:2605.18001v1 Announce Type: cross Abstract: In recent advancements within speech processing, converting read speech to conversational speech has gained significant attention. The primary challenge in this domain is…
- arXiv cs.AIresearchReal-time Chatbot Romance Scam Detection in Stage Classification
arXiv:2610.20631v1 Announce Type: cross Abstract: Romance scam has become a serious problem via social media, and the improvement of artificial intelligence enable to fake identify easily. In the study, a chatbot has dev…
- arXiv cs.AIresearchExploring the Design Space of Permutation Diffusion LLMs
arXiv:2610.20635v1 Announce Type: cross Abstract: Permutation generation with language models requires both text-conditioned ranking and a global one-to-one assignment between candidates and output positions. We study th…
- arXiv cs.AIresearchCoTS: Competitive Token Selection for Latent Reasoning in Large Language Models
arXiv:2610.20642v1 Announce Type: cross Abstract: Recent latent reasoning methods move beyond discrete Chain-of-Thought (CoT) by replacing sampled tokens with continuous soft embeddings or hidden states, enabling richer…