AI news archive.
19,861 articles, newest first, from the outlets listed on the sources page.
- arXiv cs.AIresearchOn the Rate of Convergence of GD in Non-linear Neural Networks: An Adversarial Robustness Perspective
arXiv:2603.02095v2 Announce Type: replace-cross Abstract: We study the convergence dynamics of Gradient Descent (GD) in a minimal binary classification setting, consisting of a two-neuron ReLU network and two training in…
- arXiv cs.AIagentsAgentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharing
arXiv:2603.03753v2 Announce Type: replace-cross Abstract: The ongoing shift of Artificial Intelligence (AI) from centralized cloud application programming interfaces (APIs) to local inference on edge devices is enabling…
- arXiv cs.AIresearchCORE-Seg: Reasoning-Driven Segmentation for Complex Lesions via Reinforcement Learning
arXiv:2603.05911v2 Announce Type: replace-cross Abstract: Medical image segmentation is undergoing a paradigm shift from conventional visual pattern matching to cognitive reasoning analysis. Although Multimodal Large Lan…
- arXiv cs.AIresearchMixture-Greedy for Online Generative Model Selection: Is UCB Necessary in Diversity-Aware Multi-Armed Bandits?
arXiv:2603.21716v3 Announce Type: replace-cross Abstract: Efficient selection among multiple generative models is increasingly important in modern generative AI, where sampling from suboptimal models is costly. This prob…
- arXiv cs.AIagentsAnomalyAgent: Agentic Industrial Anomaly Synthesis via Tool-Augmented Reinforcement Learning
arXiv:2604.07900v2 Announce Type: replace-cross Abstract: Industrial anomaly generation is a crucial method for alleviating the data scarcity problem in anomaly detection tasks. Most existing anomaly synthesis methods re…
- arXiv cs.AIresearchPhysics-guided surrogate learning enables zero-shot control of turbulent wings
arXiv:2604.09434v3 Announce Type: replace-cross Abstract: Turbulent boundary layers over aerodynamic surfaces are a major source of aircraft drag, yet their control remains challenging due to multiscale dynamics and spat…
- arXiv cs.AIresearchConvex Optimization of Spiking Neural Networks with Global and Approximation Guarantees
arXiv:2605.08022v3 Announce Type: replace-cross Abstract: Spiking neural networks (SNNs) have been proposed as energy-efficient alternatives to deep neural networks. Their training is non-convex and involves a non-differ…
- arXiv cs.AIresearchBeyond Pairs: Your Language Model is Secretly Optimizing a Preference Graph
arXiv:2605.08037v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) aligns language models using pairwise preference comparisons, offering a simple and effective alternative to Reinforcement Le…
- arXiv cs.AIresearchFactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
arXiv:2605.09081v5 Announce Type: replace-cross Abstract: Industrial machines are actuated systems, yet most open time-series corpora record sensor outcomes alone; the few that do log commanded signals expose them in ven…
- arXiv cs.AIagentsMemReread: Enhancing Agentic Long-Context Reasoning via Memory-Guided Rereading
arXiv:2605.10268v2 Announce Type: replace-cross Abstract: Long-context reasoning tasks have motivated agent-memory approaches that process document chunks sequentially while maintaining a dynamically updated memory, avoi…
- arXiv cs.AIagentsSampling-Based Safe Reinforcement Learning
arXiv:2605.19469v2 Announce Type: replace-cross Abstract: Safe exploration remains a fundamental challenge in reinforcement learning (RL), limiting the deployment of RL agents in the real world. We propose Sampling-Based…
- arXiv cs.AIresearchStochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent
arXiv:2605.21282v3 Announce Type: replace-cross Abstract: Combining entropy regularisation with mirror descent in online off-policy reinforcement learning requires expressive policies with efficient sampling and explicit…
- arXiv cs.AIresearchVITAL: Visual-Semantic Dual Supervision for Enhanced and Interpretable Latent Reasoning in Medical MLLMs
arXiv:2605.28422v2 Announce Type: replace-cross Abstract: Latent reasoning enables reasoning over continuous hidden states rather than explicit tokens, avoiding the language bottleneck and inference overhead of chain-of-…
- arXiv cs.AIresearchMyoSem: Aligning Electromyography with Natural-Language Action Semantics
arXiv:2606.00174v2 Announce Type: replace-cross Abstract: Electromyography (EMG) records muscle activation for hand gesture recognition, prosthetic control, and wearable interaction. Existing EMG methods usually formulat…
- arXiv cs.AIresearchWhen Should the Teacher Move? Temporal Coupling and Stability in Self On-Policy Distillation
arXiv:2606.03532v2 Announce Type: replace-cross Abstract: Self on-policy distillation trains a student policy against a teacher derived from its own parameter history, yet the teacher's update schedule -- which governs t…
- arXiv cs.AIresearchAutomatic-Differentiation-Enabled Finite Element Model Updating: Learning Constitutive Neural Networks from Structural Deformation Data
arXiv:2606.05199v2 Announce Type: replace-cross Abstract: The identification of constitutive neural network models from heterogeneous full-field deformation data provides a robust alternative to traditional calibration m…
- arXiv cs.AIresearchThe Invisible Hand of Physics: When Video Diffusion Models Know More Than They Show
arXiv:2606.05328v2 Announce Type: replace-cross Abstract: Modern video diffusion models generate increasingly realistic and temporally coherent videos, motivating their use as candidate world simulators. Yet it remains u…
- arXiv cs.AIagentsLet Large Language Models Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering
arXiv:2606.15419v2 Announce Type: replace-cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA). Method: We designed a…
- arXiv cs.AIagentsPACT: Privileged Trace Co-Training for Multi-Turn Tool-Use Agents
arXiv:2606.16215v2 Announce Type: replace-cross Abstract: Multi-turn tool-use agents must reason, call tools, and adapt to observations across several interaction turns. Post-training such agents is challenging, as reinf…
- arXiv cs.AIresearchScalable Circuit Learning for Interpreting Large Language Models
arXiv:2606.16939v2 Announce Type: replace-cross Abstract: A prominent research direction in mechanistic interpretability is learning sparse circuits over LLM components to reveal how they jointly produce model behavior.…
- arXiv cs.AIagentsBistable by Construction: Wall-Clock-Calibrated State Monitors Have No Moment-Detection Regime at Agent Cadence
arXiv:2606.19386v2 Announce Type: replace-cross Abstract: Runtime monitors for autonomous agents commonly threshold an accumulated internal state - a behavioural baseline, a drift statistic, or, in our prior work, a mode…
- arXiv cs.AIagentsDecoupling the Declarative from the Procedural in Vision-Language-Action Models
arXiv:2606.21496v2 Announce Type: replace-cross Abstract: Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-specific demons…
- arXiv cs.AIresearchRoboMME-Interference: Benchmarking Robot Memory Under Interference
arXiv:2606.22338v4 Announce Type: replace-cross Abstract: Robots deployed in realistic settings will accumulate experience across many sessions and tasks over their deployment. The robot's tasks may often require it to r…
- arXiv cs.AIresearchErased, but Not Gone: Output Forgetting Is Not True Forgetting
arXiv:2606.25001v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced membership inference. But if output-level success can…
- arXiv cs.AIagentsThe Spec Growth Engine: Spec-Anchored, Code-Coupled, Drift-Enforced Architecture for AI-Assisted Software Development
arXiv:2606.27045v2 Announce Type: replace-cross Abstract: AI coding agents dramatically accelerate implementation speed but introduce two structural failure modes that existing spec-driven approaches do not fully solve:…
- arXiv cs.AIresearchImproving Certified Robustness via Adversarial Distillation
arXiv:2606.31653v2 Announce Type: replace-cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on th…
- arXiv cs.AIresearchRobustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids
arXiv:2607.16004v2 Announce Type: replace-cross Abstract: Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requir…
- arXiv cs.AIagentsLearning from All-Failed Groups via First-Visit Coverage in Long-Horizon Agentic Tasks
arXiv:2607.22724v2 Announce Type: replace-cross Abstract: Group-based policy optimization has been increasingly used to train large language model (LLM) agents from sparse outcome rewards by comparing trajectories or ste…
- arXiv cs.AIresearchFirst-Order In-Context Shapley: Scalable Data Auditing for LLM Alignment and Evaluation
arXiv:2607.22766v2 Announce Type: replace-cross Abstract: Post-training alignment of Large Language Models is increasingly bottlenecked by data quality: preference and instruction-tuning corpora accumulate label noise, s…
- arXiv cs.AIresearchWhat Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)
arXiv:2608.06202v2 Announce Type: replace-cross Abstract: Large language model (LLM) benchmark evaluations are routinely used to support claims about model safety, reliability, and deployment readiness. Yet most evaluati…
- arXiv cs.AIagentsSTAIR: Effective Incident Response Using an End-to-End Agentic Planning Framework
arXiv:2608.09524v2 Announce Type: replace-cross Abstract: Incident response planning is critical for restoring compromised software systems. Existing AIOps methods automate fault diagnosis and remediation and adapt respo…
- arXiv cs.AIresearchDASC: Decay-Aware State Compression for Hybrid Linear-Attention Serving
arXiv:2608.30386v2 Announce Type: replace-cross Abstract: Hybrid linear-attention architectures have recently scaled to large open-weight models, offering quality competitive with full attention while substantially reduc…
- arXiv cs.AIagentsDefense-as-Skill: Evolving Runtime Guard Skill for Skill-Augmented Agents
arXiv:2609.01487v4 Announce Type: replace-cross Abstract: Skill-augmented agents load reusable skills as persistent runtime context, improving task performance but also giving malicious skills a durable channel for steer…
- arXiv cs.AIresearchProtocol effects on feature-based hardware-Trojan detection across Trust-Hub families
arXiv:2609.07199v2 Announce Type: replace-cross Abstract: Trust-Hub reuses host circuits: several files differ mainly in the inserted Trojan. When gates from sibling variants enter both training and test folds, a detecto…
- arXiv cs.AIresearchTDDN: Text-aligned Diffused DINO Network for Puzzle Understanding
arXiv:2609.07937v2 Announce Type: replace-cross Abstract: Structured visual reasoning, such as image puzzles, demands fine-grained visual perception, which current Vision Language Models (VLMs) often lack. VLMs built on…
- arXiv cs.AIresearchSUN: Reaching for Novelty in Reinforcement Learning
arXiv:2609.08642v2 Announce Type: replace-cross Abstract: Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broader state c…
- arXiv cs.AIresearchWhat Makes Adversarial Examples Transfer Across Deepfake Detectors?
arXiv:2609.10002v2 Announce Type: replace-cross Abstract: Deepfake detectors remain vulnerable to transfer-based black-box attacks, in which adversarial examples are generated on a source surrogate model and transferred…
- LessWrongresearchCan Decision Models (Like Jev) Plan Ahead?
Introducion Can decision models like Jev plan ahead? If so, how can we quantify their planning ability? This is the question that BeelineBench seeks to answer. In this article, I'll explain my methodology and share a few…
- LessWrongmodelsGame Design Taste evals
A personal eval I run on new AI models is “design a novel game, with mechanics that have never been seen before.” I do this because this is a domain I have enough taste in that I think I can tell if they are doing someth…
- LessWrongresearchGame Design Creativity evals
A personal eval I run on new AI models is “design a novel game, with mechanics that have never been seen before.” I do this because this is a domain I have enough taste in that I think I can tell if they are doing someth…
- SiliconANGLE AIstartupsBotMan AI uses knowledge graphs for legacy modernization
Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it. BotMan AI uses an AI-powered migration pipeline that maps an application’s dependencies, b…
- LessWrongmodelsA problem with three correct answers. Which, how, and when does a model choose?
TL;DR: A look at how models (3 sizes of the Qwen 2.5 model family) behave when posed with Bertrand's paradox. 18 phrasings, up to 30 answers each. For each, we check which of the three famous responses it outputs, whethe…
- Apple Machine Learning ResearchresearchLarge Language Models for Machine Translation Quality Annotation: Humans and Models Are Both Challenged
Large Language Models (LLMs) are considered to be a more efficient and cost-effective alternative to human judgment for Machine Translation (MT) evaluation. With MT evaluation spanning a large number of language pairs, d…
- Apple Machine Learning ResearchresearchMinions: A Dynamic and Distributed Virtual GPU Cloud Built on Idle Apple Silicon Devices
The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under resource constraints. At…
- Apple Machine Learning ResearchresearchSelf-Compacting Language Model Agents
Long agent traces composed of chains of thought and tool calls accumulate stale content that anchor subsequent generations, and eventually outgrow the context window. Existing scaffolds mitigate it with fixed-interval co…
- LessWrongcommunityMy Buddy Mike, Efficiency Gains From AI, and The World’s Most Boringly Benign Criminal Conspiracy
Note: details have been obfuscated and changed to protect – not the innocent, who are invariably vindicated by the end of act three – but the guilty, for whom the narrative provides no such guarantees. My buddy Mike is o…
- BBC TechnologypolicyAI is less regulated than milk, says Microsoft president
Brad Smith calls for global rules on AI, and says the UK is in the "best position" to lead the charge.
- Guardian AIpolicyUK to strengthen cyber defences as minister calls for AI firms to increase safety efforts
Kanishka Narayan to say Britain cannot rely entirely on tech companies amid rising global concern over AI dangers Companies building the most advanced AI systems must increase safety efforts, the UK’s AI minister will sa…
- SiliconANGLE AIstartupsNvidia reportedly in early talks to buy, acquihire or invest more in Reflection AI
Nvidia Corp. is in early talks to acquire open-weight model developer Reflection AI Inc. or invest more in the startup, according to the Financial Times. People familiar with the matter told the paper that an agreement c…
- LessWrongresearchIf possible mind-space is vast, we should assume the same applies to consciousness-space
It's been almost twenty years since the classic EY post The Design Space of Minds-In-General came out, reminding the humble rationalist reader to never assume that future alien minds have to be copies of the familiar hum…