AI news archive.
10,659 articles filed under Research, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchProspect Theory in Large Language Models: A Meta-Analysis of Loss Aversion, Diminishing Sensitivity, and Probability Weighting
arXiv:2610.33103v1 Announce Type: new Abstract: Prospect theory splits risky choice into three parts: loss aversion, measured by the relative weight of losses to gains, $\lambda$ (e.g., $\lambda = 2$ implies that losses…
- arXiv cs.AIresearchArtificial Intelligence in Mechanical Engineering: Foundations, Opportunities, Challenges, and Responsibilities
arXiv:2610.33105v1 Announce Type: new Abstract: Artificial intelligence (AI) is reshaping mechanical engineering (ME), from modeling, simulation, and optimization to diagnostics, design, and technical communication. Draw…
- arXiv cs.AIresearchConformance as a Distribution: Delegation Contracts, Error Budgets, and the Allocation of Evaluation Authority in Use-Based AI Regulation
arXiv:2610.33112v1 Announce Type: new Abstract: Use-based AI regulation assigns obligations by deployment risk but lacks an evidentiary object to carry them. A benchmark score is a property of a model; conformance of a d…
- arXiv cs.AIresearchOptimal Correction Sets for Provable Redress of Contestable Causal Discovery
arXiv:2610.33138v1 Announce Type: new Abstract: Constraint-based causal discovery relies on conditional-independence (CI) tests that may conflict in finite samples, leaving no causal graph that satisfies them all. Causal…
- arXiv cs.AIresearchPropensities Please: Exposing Alignment-Faking LLMs using Synthetic Document Finetuning
arXiv:2610.33180v1 Announce Type: new Abstract: Alignment evaluations aim to assess whether large language models would engage in dangerous misaligned behaviour. Misaligned and evaluation-aware models might engage in ali…
- arXiv cs.AIresearchIsharah-Selfie: Continuous Sign Language Recognition Dataset for One-handed Signing
arXiv:2610.33200v1 Announce Type: new Abstract: Current sign language recognition and translation benchmarks are commonly collected in controlled settings using fixed or studio-style cameras, particularly limiting their…
- arXiv cs.AIresearchScaling Qualitative Analysis with Large Language Models: Development and Validation of the Smart Coder Framework for Human-LLM Collaborative Coding
arXiv:2610.33205v1 Announce Type: new Abstract: Current uses of large language models (LLMs) for qualitative coding often rely on ad hoc prompting with limited guidance for reliability and human oversight. To address the…
- arXiv cs.AIresearchDistilling Expert Judgment at Scale
arXiv:2610.33233v1 Announce Type: new Abstract: Frontier AI models are increasingly deployed on topics where the stakes are high and expert judgment is required, including healthcare, defense, finance, governance, and po…
- arXiv cs.AIresearchNewsBench: Expert-Grounded Evaluation of Epistemic Quality in AI News Reporting
arXiv:2610.33240v1 Announce Type: new Abstract: As frontier AI systems increasingly serve as sources of political and current-events information, evaluating the epistemic quality of their responses is now especially cons…
- arXiv cs.AIresearchScoped Semantic Invariance as a Foundation for LLM Reliability
arXiv:2610.33267v1 Announce Type: new Abstract: LLMs often answer semantically equivalent queries inconsistently, even when the underlying facts are known. We formalize consistency as semantic invariance over equivalence…
- arXiv cs.AIresearchGEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation
arXiv:2610.33277v1 Announce Type: new Abstract: Engineering standards are challenging knowledge sources for large language models (LLMs) because they are hierarchical, multimodal, and densely connected through figures, t…
- arXiv cs.AIresearchAI Teaches Humans to Handle Complexity in the Game of Go
arXiv:2610.33323v1 Announce Type: new Abstract: Strategic decision-making in complex environments requires balancing local stability against global coordination. Using the game of Go as a bounded empirical testbed, we in…
- arXiv cs.AIresearchTempShap: When and How Much a Feature Matters in Time-Series Forecasting
arXiv:2610.33330v1 Announce Type: new Abstract: Attribution for a time-series forecaster should answer two questions that a feature-level scalar cannot: when in the input window a feature acts, and how much its influence…
- arXiv cs.AIresearchHow To Build A Modern Diffusion Language Model
arXiv:2610.33391v1 Announce Type: new Abstract: The frontier of large language model (LLM) performance has advanced rapidly in recent years, fueled by scaling laws for pretraining on large text corpora. However, mainstre…
- arXiv cs.AIresearchData Optimization for LLMs: A Survey
arXiv:2610.33405v1 Announce Type: new Abstract: Large language models (LLMs) have attracted great attention in both academic and industrial communities in recent years. Numerous survey studies have covered various aspect…
- arXiv cs.AIresearchBenchmarking System One Models for Time-Series Decision Making: A Case Study of Jev
arXiv:2610.33410v1 Announce Type: new Abstract: Time-series tasks require both accurate predictions and fast decisions. Advances in large models have introduced decision-oriented models that directly output answers and t…
- arXiv cs.AIresearchBoardSafety: AI-Ready Data from Manufacturer Documentation for Development-Board Safety
arXiv:2610.33448v1 Announce Type: new Abstract: The development of large language models (LLMs) are capable to assist with hardware experiments. In this scenario, this raises a practical question: Can LLMs determine whet…
- arXiv cs.AIresearchA Comprehensive Framework for All Stages of Large-Model Training: From Data and Tokenization, through Pretraining, to Post-Training Alignment
arXiv:2610.33451v1 Announce Type: new Abstract: The capabilities of large language models (LLMs) and multimodal LLMs (MLLMs) arise from the synergy of the full training pipeline, including data and tokenization, pretrain…
- arXiv cs.AIresearchThe Future Is Finer: Multivariate Time Series Super-Resolution Forecasting
arXiv:2610.33470v1 Announce Type: new Abstract: Web platforms run on multivariate time series, from service telemetry and Web traffic logs to sensor streams served through Web APIs. Yet, retention policies often archive…
- arXiv cs.AIresearchAuditing evaluation artifacts and data leakage in large language model systems for rare disease diagnosis
arXiv:2610.33592v1 Announce Type: new Abstract: Augmented large language model (LLM) systems have reported high benchmark accuracies in rare disease diagnosis, yet their real-world clinical utility remains unproven. A re…
- arXiv cs.AIresearchCounterfactual Model Testing Under Distribution Shift
arXiv:2610.33594v1 Announce Type: new Abstract: Deployed machine learning (ML) models can degrade as the distribution of their input data shifts. Such shifts can result from changes in the population or from data integri…
- arXiv cs.AIresearchWhen Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems
arXiv:2610.33617v1 Announce Type: new Abstract: Large language models (LLMs) increasingly produce online articles from material that later becomes input to other models. This circulation connects individual responses to…
- arXiv cs.AIresearchGraph-dependent pooling: adaptive granularity in hierarchical graph neural networks
arXiv:2610.33623v1 Announce Type: new Abstract: Graph Neural Networks are widely used to learn structural representations of relational data. Hierarchical graph pooling learns multi-level representations by progressively…
- arXiv cs.AIresearchRAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches
arXiv:2610.33625v1 Announce Type: new Abstract: Multi-Head Latent Attention (MLA) has been adopted by recent frontier large language models. Instead of per-head keys and values, MLA caches a compressed KV latent that is…
- arXiv cs.AIresearchTAD: Distilling Vision-Language-Action Policies from Teacher Trajectories Alone
arXiv:2610.33650v1 Announce Type: new Abstract: Online distillation of vision-language-action (VLA) policies commonly relies on teacher action scores that an inference-only interface may not expose. Trajectory-only Adver…
- arXiv cs.AIresearchPhysics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network
arXiv:2610.33654v1 Announce Type: new Abstract: High penetrations of distributed energy resources (DERs) and fast-varying electric vehicle (EV) charging intensify voltage and line-limit violations in distribution network…
- arXiv cs.AIresearchPower-of-Two Leaky Integrate-and-Fire Neurons for Event-Driven Vision-Language-Action Inference
arXiv:2610.33658v1 Announce Type: new Abstract: Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, yet their reliance on dense, continuous-valued computation presents a fun…
- arXiv cs.AIresearchMeta-CLIP: Metadata-Conditioned Contrastive Learning for Zero-Shot Disease Inference
arXiv:2610.33674v1 Announce Type: new Abstract: Medical vision--language pretraining learns transferable representations from paired images and radiology reports, yet differences in scanners and acquisition protocols can…
- arXiv cs.AIresearchAll the Facts, Still the Wrong Answer: Integration Gaps in Vision-Language Models
arXiv:2610.33764v1 Announce Type: new Abstract: Multimodal reasoning requires models to combine multiple pieces of evidence into decisions that satisfy all relevant constraints. Evaluations centered on final-answer accur…
- arXiv cs.AIresearchLocal Entropy Fluctuations for Black-Box LLM-Generated Text Detection
arXiv:2610.33767v2 Announce Type: replace Abstract: Zero-shot detection of machine-generated text is vital for mitigating the risks of large language models (LLMs) misuse. While the state-of-the-art dual-model detectors…
- arXiv cs.AIresearchAn actively trained neural operator for near-explicit atmospheric chemistry
arXiv:2610.33771v2 Announce Type: replace Abstract: Earth-system models simplify atmospheric chemistry into condensed mechanisms, because near-explicit mechanisms such as the Master Chemical Mechanism (MCM) are too costl…
- arXiv cs.AIresearchThe Hitchhiker's Guide to System 1 Models: Fast Decision-Making Beyond LLMS
arXiv:2610.33775v2 Announce Type: replace Abstract: Large Language Models excel at reasoning and generation but their autoregressive, token-by-token inference makes them unnecessarily slow, expensive, and poorly calibrat…
- arXiv cs.AIresearchInfiniteWorld: Separating Visual Evidence from Familiar Physics
arXiv:2610.33777v1 Announce Type: new Abstract: Physical reasoning requires adapting familiar expectations to the dynamics of the observed world. Existing benchmarks establish broad physical capabilities, yet an overall…
- arXiv cs.AIresearchUnlocking the Specialization Capability of PPG Foundation Models for Wearable Signals through Structured Pruning and Adaptive Fine-Tuning
arXiv:2610.33788v1 Announce Type: new Abstract: Photoplethysmography (PPG) plays a central role in wearable health monitoring and clinical decision support. However, existing approaches to universal PPG representation le…
- arXiv cs.AIresearchAre We Done with UMLS Injection into Language Models for Medical Question Answering?
arXiv:2610.33801v1 Announce Type: new Abstract: Maybe not. The Unified Medical Language System (UMLS) has become an oracle for millions of clinicians and health system developers worldwide. Researchers have increasingly…
- arXiv cs.AIresearchBeyond Static Structures: Linking Protein Dynamics to Function via DynaReason-Pro
arXiv:2610.33804v1 Announce Type: new Abstract: Intrinsically disordered proteins (IDPs) carry out their functions through heterogeneous conformational ensembles that reflect their dynamics, yet most protein function pre…
- arXiv cs.AIresearchRectified Language Flow
arXiv:2610.33811v1 Announce Type: new Abstract: Continuous-state flow and diffusion models offer a promising approach to fast, parallel language generation, but matching the generation quality of autoregressive models re…
- arXiv cs.AIresearchWebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation
arXiv:2610.33929v1 Announce Type: new Abstract: Large language models have made remarkable strides in website development, enabling complete and interactive web experiences to be built from natural-language instructions…
- arXiv cs.AIresearchFed-Universe: A Semantic-Geometric-Topological-Human (S-G-T-H) Stack for Negotiated Alignment in Federated Systems
arXiv:2610.34009v1 Announce Type: new Abstract: Conventional federated learners implicitly optimize for global means, creating a "one-size-fits-all" paradigm that inevitably suppresses the minority under cross-site heter…
- arXiv cs.AIresearchFrom Document Layout to Causal Topology: An End-to-End Architecture for Temporal Causal Reasoning
arXiv:2610.34094v1 Announce Type: new Abstract: Large language models (LLMs) generally lack deep causal reasoning capabilities in temporal reasoning. Inspired by document layout reordering, this paper proposes a paradigm…
- arXiv cs.AIresearchVariational Cognitive Memory Abstraction for Long-Horizon Vision-Language-Action Models
arXiv:2610.34251v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown remarkable success in robotic manipulation, yet their ability to execute long-horizon tasks remains limited by insufficient l…
- arXiv cs.AIresearchFrom Outranking Flow Scores to Linear Programs: Group Decision Making in PROMETHEE II
arXiv:2610.34481v1 Announce Type: new Abstract: We study inverse and consensus analysis for PROMETHEE II ranking problems when the performance table is fixed. Existing approaches in PROMETHEE-based sensitivity and consen…
- arXiv cs.AIresearchHow Humans Learn Unfamiliar Worlds: A Think-Aloud Dataset on ARC-AGI-3
arXiv:2610.34523v1 Announce Type: new Abstract: Learning through exploration and using newly acquired knowledge to achieve goals are central capabilities of general intelligence. Understanding these capabilities requires…
- arXiv cs.AIresearchHow Much Information Can We Fit inside a Transformer?
arXiv:2610.34553v1 Announce Type: new Abstract: How efficiently can transformers store factual knowledge? The answer to this question has implications for both model design and understanding the limits of transformer-bas…
- arXiv cs.AIresearchHobbyLM: A Sparse Mixture-of-Experts Model Family on a Hobby Budget
arXiv:2610.34582v1 Announce Type: new Abstract: We describe HobbyLM, a family of small sparse Mixture-of-Experts (MoE) models trained from scratch with a few hundred to a few thousand dollars of rented GPU time. The core…
- arXiv cs.AIresearchA Battery for AI Propensity Measurement: Rubrics, Synthetic Item Banks, and Annotations
arXiv:2610.34614v1 Announce Type: new Abstract: We release a battery of 28 propensities for AI research, with expert-written rubrics, synthetic datasets, and annotated demand intervals, built with the method of Romero-Al…
- arXiv cs.AIresearchCSI-TextBench: A Dataset and Benchmark for Language-Grounded Ambient Sensing Perception
arXiv:2610.34616v1 Announce Type: new Abstract: Indoor context deciphered by ambient sensing underpins many applications, from assisted living to smart buildings, yet remains weakly connected to the language-aligned para…
- arXiv cs.AIresearchOne Mean Log-Mel Vector per Phoneme: Simple and Fast Text Injection for Speech Recognition
arXiv:2610.34623v1 Announce Type: new Abstract: Text injection trains speech recognition models on unpaired text alongside paired audio. We feed the speech encoder synthetic input derived from text so the whole model lea…
- arXiv cs.AIresearchAdaTutor: Adaptive Tutoring through Pedagogical Strategy Selection
arXiv:2610.34659v1 Announce Type: new Abstract: Effective tutoring requires determining not only what information to provide, but also which form of assistance to deliver. A tutor may address the same student error using…
- arXiv cs.AIresearchCurriculum-Guided Acquisition of Hierarchical Task Knowledge from LLM-Generated Experience
arXiv:2610.34677v1 Announce Type: new Abstract: Hierarchical task knowledge enables efficient problem solving by allowing previously acquired skills to be reused across tasks and problem instances. In this paper, we inve…