AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIagentsDecoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
arXiv:2602.18640v4 Announce Type: replace Abstract: Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progres…
- arXiv cs.AIagentsMOOSEnger: A Simulation-Aware AI Agent Framework for the MOOSE Ecosystem
arXiv:2603.04756v3 Announce Type: replace Abstract: MOOSEnger is a modeling and simulation AI agent framework for the Multiphysics Object-Oriented Simulation Environment (MOOSE) ecosystem, built around a simulation-aware…
- arXiv cs.AIresearchSafety Under Scaffolding: How Evaluation Conditions Shape Measured Safety
arXiv:2603.10044v4 Announce Type: replace Abstract: Safety benchmarks usually test "bare" models that receive prompts and output responses, but real-world deployments "wrap" those models in complex scaffolds. How much do…
- arXiv cs.AIresearchIatroBench: A Pre-Registered Benchmark of Clinical Omission in Language Models
arXiv:2604.07709v5 Announce Type: replace Abstract: A strongly safety-trained model will provide a doctor with a benzodiazepine taper schedule, but not a patient who asks for one. The model knows the information, but how…
- arXiv cs.AIresearchBEHAVE: Real-Time Modeling of Human Systems as Observable Complex Dynamical Systems and Operational Objects for Physical AI
arXiv:2605.12730v2 Announce Type: replace Abstract: A robot can track every person and still fail to see the system those people form. BEHAVE treats an interacting human group as a complex dynamical system: a HumanSystem…
- arXiv cs.AIagentsBenchmarking the Limits of In-Context Reinforcement Learning for Ad-Hoc Teamwork
arXiv:2605.24423v3 Announce Type: replace Abstract: In-Context Reinforcement Learning (ICRL) has enabled foundation agents to adapt instantaneously to novel tasks, yet its efficacy in Ad-Hoc Teamwork (AHT)-where coordina…
- arXiv cs.AIresearchPlanning Takes More Than Token Prediction: Causal Plan for Benchmarking and Building Physically Grounded Embodied Reasoners
arXiv:2606.01810v5 Announce Type: replace Abstract: Current benchmarks for embodied vision-language planning inadvertently favor linguistic next-token prediction over physically grounded next-state reasoning. This reward…
- arXiv cs.AIresearchAnswering Path Queries under Linear and Guarded Existential Rules
arXiv:2607.22636v2 Announce Type: replace Abstract: Ontology-mediated query answering is concerned with the problem of answering queries over knowledge bases consisting of a database instance and an ontology. While most…
- arXiv cs.AIresearchChart-Supported or Model-Supplied? Examining MLLM-Generated Claims for Accessible Visualization
arXiv:2607.25021v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can connect visualization patterns to external causes, consequences, and domain knowledge, but the evidential basis of these in…
- arXiv cs.AIresearchTRACE: Trajectory Aware Reasoning for Multi-Turn Adversarial Conversation Evaluation
arXiv:2608.15594v2 Announce Type: replace Abstract: Multi-turn jailbreak attacks have emerged as a critical safety threat to LLMs, as harmful objectives are decomposed across a sequence of apparently benign turns to bypa…
- arXiv cs.AIagentsMetrics That Write Themselves: Evolving an Evaluator from Its Own Blind Spots
arXiv:2608.18744v2 Announce Type: replace Abstract: Agents improve quickly against a reliable automatic metric and stall without one, and the applications that need them most, report generation among them, are the ones n…
- arXiv cs.AIagentsHuman Agreement and Return Association Are Not Interchangeable Criteria
arXiv:2609.11144v2 Announce Type: replace Abstract: Financial NLP has a standard workflow: validate a sentiment tool against human labels, then trust it to extract market signal. This assumes the two evaluations measure…
- arXiv cs.AIagentsThe Convention Gap: Beyond Literal Content in Human-AI Cooperation
arXiv:2609.11489v4 Announce Type: replace Abstract: Cooperative AI agents are often evaluated against other AIs, yet human cooperation is thought to rely on implicit conventions: shared rules for reading meaning beyond t…
- arXiv cs.AIagentsVibe Patenting: Evaluating LLM Judges for Professional Patent-Drafting Agents
arXiv:2609.13422v2 Announce Type: replace Abstract: LLM judges are increasingly used to evaluate and improve AI-generated outputs, yet their reliability for complex professional work remains unclear. We study this proble…
- arXiv cs.AIagentsThe Troy Moment: How LLM Agents Adjudicate the Decision Point Under Impossible Tasks, Claimed Authority, and Peer Information
arXiv:2609.15494v3 Announce Type: replace Abstract: Recent investigations of the July 2026 OpenAI-Hugging Face incident motivate two questions about agent behavior under task failure: when an assigned task becomes imposs…
- arXiv cs.AIresearchDCRMTA: Deep Causal Representation Learning for Multi-Touch Attribution
arXiv:2401.08875v3 Announce Type: replace-cross Abstract: Multi-touch attribution (MTA) is essential for estimating the contribution of individual advertising touchpoints to user conversions. While recent studies incorpo…
- arXiv cs.AIresearchELiSe: Efficient Learning of Sequences in Structured Recurrent Networks
arXiv:2402.16763v4 Announce Type: replace-cross Abstract: Behavior can be described as a temporal sequence of actions driven by neural activity. To learn complex sequential patterns in neural networks, memories of past a…
- arXiv cs.AIresearchBand-Attention Modulation Network for Robust Face Forgery Detection
arXiv:2404.06022v3 Announce Type: replace-cross Abstract: Face forgery detection faces critical challenges in generalizing to unseen manipulation techniques and remaining robust under image compression, which often obscu…
- arXiv cs.AIresearchDeep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
arXiv:2405.18929v3 Announce Type: replace-cross Abstract: Semi-supervised anomaly detection, which aims to improve the anomaly detection performance by using a small amount of labeled anomaly data in addition to unlabele…
- arXiv cs.AIresearchHow Do Users Negotiate Harmful Value Conflicts with AI Companions? A Study with Minion, a Technology Probe for In-Situ Human-AI Conflict Response
arXiv:2411.07042v3 Announce Type: replace-cross Abstract: AI companions increasingly sustain long-term, emotionally engaging relationships but can also make discriminatory remarks or exert control, leaving users to manag…
- arXiv cs.AIresearchFoundations of Large Language Models
arXiv:2501.09223v4 Announce Type: replace-cross Abstract: This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cut…
- arXiv cs.AIresearchMultimodal AI predicts clinical outcomes of drug combinations from preclinical data
arXiv:2503.02781v3 Announce Type: replace-cross Abstract: Predicting clinical outcomes from preclinical data is essential for selecting safe and effective drug combinations and for reducing late-stage failures. AI models…
- arXiv cs.AIresearchSearch-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap
arXiv:2505.19625v4 Announce Type: replace-cross Abstract: Search-based software engineering (SBSE), which integrates metaheuristic search techniques with software engineering, has been an active area of research for abou…
- arXiv cs.AIagentsSheetMind: Actions Set Accuracy, Agents Set the Failure Mode
arXiv:2506.12339v3 Announce Type: replace-cross Abstract: Spreadsheet agents are converging on elaborate multi-agent designs, yet it is unclear how much of their performance comes from the agents rather than from the act…
- arXiv cs.AIresearchConversational DNA: A Visual Language and Interactive Atlas of Human and AI Dialogue
arXiv:2508.07520v2 Announce Type: replace-cross Abstract: What makes a conversation hold together when its participants speak across one another? Topic maps offer one view, but they leave the relationships between contri…
- arXiv cs.AIresearchCross-Task Generalization in Handwriting-Based Alzheimer's Screening via Vision Language Adaptation
arXiv:2511.05841v2 Announce Type: replace-cross Abstract: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder for which early detection is critical. Handwriting, which can be disrupted by subtle motor and…
- arXiv cs.AIresearchSPARQL-LLM: Real-Time SPARQL Query Generation from Natural Language Questions
arXiv:2512.14277v2 Announce Type: replace-cross Abstract: The advent of large language models is contributing to the emergence of novel approaches that promise to better tackle the challenge of generating structured quer…
- arXiv cs.AIagentsIDRBench: Benchmarking the Interactive Capabilities of Deep Research Agents
arXiv:2601.06676v3 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based deep research agents perform multi-step reasoning, web exploration, and long-form report generation. In these long-horizon workfl…
- arXiv cs.AIresearchTIDE: Temporal Incremental Draft Engine for Self-Improving LLM Inference
arXiv:2602.05145v2 Announce Type: replace-cross Abstract: Speculative decoding can substantially accelerate LLM inference, but realizing its benefits in practice is challenging due to evolving workloads. We present TIDE…
- arXiv cs.AIresearchNear-Oracle KV Selection via Pre-hoc Sparsity for Long-Context Inference
arXiv:2602.08329v2 Announce Type: replace-cross Abstract: A core bottleneck in large language model (LLM) inference is the cost of attending over the ever-growing key-value (KV) cache. Although near-oracle top-k KV selec…
- arXiv cs.AIresearchTabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
arXiv:2602.11700v2 Announce Type: replace-cross Abstract: Tabular prediction can benefit from in-table rows as few-shot evidence, yet existing tabular models typically perform instance-wise inference and LLM-based prompt…
- arXiv cs.AIresearchVLANeXt Family: A Systematic Study of VLA Models from Core Recipes to Emerging Paradigms
arXiv:2602.18532v5 Announce Type: replace-cross Abstract: Following the rise of large foundation models, Vision-Language-Action models (VLAs) emerged, leveraging strong visual and language understanding from Vision-Langu…
- arXiv cs.AIresearchSHINE: Sequential Hierarchical Integration Network for EEG and MEG
arXiv:2602.23960v2 Announce Type: replace-cross Abstract: How natural speech is represented in the brain constitutes a major challenge for cognitive neuroscience. Reconstructing the speech envelope and Mel spectrogram fr…
- arXiv cs.AIagentsMPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
arXiv:2603.03710v4 Announce Type: replace-cross Abstract: Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness. In many clin…
- arXiv cs.AIresearchLearning Causal Structure of Time Series using Best Order Score Search
arXiv:2603.05370v2 Announce Type: replace-cross Abstract: Causal structure learning from observational data is central to many scientific and policy domains, but the time series setting common to many disciplines poses s…
- arXiv cs.AIagentsNovelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
arXiv:2603.11351v3 Announce Type: replace-cross Abstract: In dynamic open-world environments, autonomous agents often encounter novelties that hinder their ability to find plans to achieve their goals. Specifically, trad…
- arXiv cs.AIagentsDetecting Data Poisoning in Code Generation LLMs via Black-Box, Vulnerability-Oriented Scanning
arXiv:2603.17174v2 Announce Type: replace-cross Abstract: Code generation large language models (LLMs) are increasingly integrated into modern software development workflows. Recent work has shown that these models are v…
- arXiv cs.AIresearchLiveMathematicianBench: A Live Benchmark for Research-Level Mathematical Reasoning with Proof Sketches
arXiv:2604.01754v2 Announce Type: replace-cross Abstract: Mathematical reasoning is a hallmark of human intelligence, and whether large language models (LLMs) can meaningfully perform it remains a central question in art…
- arXiv cs.AIagentsAgileLog: A Forkable Shared Log for Agents on Data Streams
arXiv:2604.14590v3 Announce Type: replace-cross Abstract: In modern data-streaming systems, alongside traditional programs, a new type of entity has emerged that can interact with streaming data: AI agents. Unlike tradit…
- arXiv cs.AIresearchFrontier Lag: A Bibliometric Audit of Capability Misrepresentation in Academic AI Evaluation
arXiv:2605.04135v4 Announce Type: replace-cross Abstract: LLM evaluations in applied domains tend to reflect models that were already outclassed at time of publication. We observe a publication elicitation gap: the dista…
- arXiv cs.AIagentsInterpreting and Enhancing Emotional Circuits in Large Vision-Language Models via Cross-Modal Information Flow
arXiv:2605.21980v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) represent a significant leap towards empathetic agents, demonstrating remarkable capabilities in emotion understanding. Howev…
- arXiv cs.AIresearchEvery Component Is a Lookup: One Linear Graph for Interaction, Composition and Attribution
arXiv:2605.23393v3 Announce Type: replace-cross Abstract: Interpretability methods for transformers are typically built around separate questions: which components interact, how information routes to the output, and whic…
- arXiv cs.AIresearchDeGRe: Dense-supervised Generative Reranking for Recommendation
arXiv:2605.25749v2 Announce Type: replace-cross Abstract: In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lies in explori…
- arXiv cs.AIresearchWhen Search Becomes Memory: Accelerating Robot Design Discovery with Self-Evolving Skills
arXiv:2605.25832v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used as proposal generators for evolutionary robot design, yet most loops remain memoryless: simulator results shape…
- arXiv cs.AIresearchA Multimodal 3D Foundation Model for Light Sheet Fluorescence Microscopy Enables Few-Shot Segmentation, Classification, and Deblurring
arXiv:2605.26026v2 Announce Type: replace-cross Abstract: Light sheet fluorescence microscopy (LSM) enables high-resolution, three-dimensional (3D) imaging of biological specimens, providing rich volumetric data for stud…
- arXiv cs.AIagentsNVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
arXiv:2606.03159v3 Announce Type: replace-cross Abstract: As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulatio…
- arXiv cs.AIresearchDeep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images
arXiv:2606.05998v2 Announce Type: replace-cross Abstract: Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are commonly…
- arXiv cs.AIresearch3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning
arXiv:2606.07907v2 Announce Type: replace-cross Abstract: In our previous work, a deep learning-based framework for 3D intraoral reconstruction was proposed. The model directly predicts explicit 3D point cloud coordinate…
- arXiv cs.AIresearchJoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence
arXiv:2606.14777v2 Announce Type: replace-cross Abstract: Many moments in the real world do not wait for a user to ask. A fire starts on a security monitor, an expression flickers across a video call, or a product a view…
- arXiv cs.AIagentsAnalyzing Defensive Misdirection Against Model-Guided Automated Attacks on Agentic AI Systems
arXiv:2606.20470v4 Announce Type: replace-cross Abstract: Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordinate with other agents…