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even by the standards of the last few years, we are in some truly insane territory here.

even by the standards of the last few years, we are in some truly insane territory here. “Unserious, empty, hallucinatory, and borderline dishonest” - the prospectus for a company that the S&P 500 is

safetygary-marcus--x

Model Releases

everybody talks about the china->us catchup not enough people talking about the us-> china catchup great job @o_lacombe et al, @robert_mchar…

DGX agent

everybody talks about the china->us catchup not enough people talking about the us-> china catchup great job @o_lacombe et al, @robert_mchardy et al! [AINews 3 Apr 2026] Gemma 4: The world's best smal

model-releasesswyx--x

26 May 2026

everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my mind, it's a system that obser…

tutorialsharrison-chase--x

everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my mind, it's a system that observes it's own outputs, evaluates them, and uses that signal t

Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution

arXiv:2605.26032v1 Announce Type: cross Abstract: Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical difference

Evidence-Linked Radiology Reporting: A Human-Supervised Reference Architecture for Structured Imaging Intelligence

arXiv:2605.25120v1 Announce Type: cross Abstract: Radiology reports remain the primary mechanism by which imaging findings are communicated to clinical teams. However, much of the structured informati

Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS

arXiv:2605.25389v1 Announce Type: cross Abstract: While Large Language Model-based Multi-Agent Systems (LLM-MAS) demonstrate remarkable capabilities in solving complex tasks by orchestrating specializ

agentsarxiv-cs-ai

EvoCode-Bench: Evaluating Coding Agents in Multi-Turn Iterative Interactions

arXiv:2605.24110v1 Announce Type: new Abstract: Coding agents are increasingly used as iterative development partners, but most benchmarks still evaluate one specification followed by one final assess

EvoEGF-Mol: Evolving Exponential Geodesic Flow for Structure-based Drug Design

model-releasesarxiv-cs-lg

arXiv:2601.22466v2 Announce Type: replace Abstract: Structure-Based Drug Design (SBDD) aims to discover bioactive ligands. Conventional approaches construct probability paths separately in Euclidean a

Evolutionary Enhanced Multi-Agent Reinforcement Learning for Cooperative Air Combat

arXiv:2605.25091v1 Announce Type: new Abstract: As modern air combat evolves toward beyond-visual-range (BVR) multi-aircraft cooperative engagements, autonomous decision-making for unmanned combat aer

Evolutionary Physics-Informed Temporal Fusion for Lane-Change Intention Prediction

arXiv:2512.24075v5 Announce Type: replace Abstract: Early lane-change intention prediction is essential for autonomous driving and ADAS, but it remains challenging because lane-changing behavior depen

agentsarxiv-cs-lg

Evolving Causal Regulatory Networks (ECR-Net)

arXiv:2605.25211v1 Announce Type: new Abstract: Modern machine learning models excel at pattern recognition but remain brittle, often failing to generalize out of distribution (OOD) because they captu

local-aiarxiv-cs-lg

Evolving Robustness--Exploration Trade-off in Online Reinforcement Learning via Quantile Bayesian Risk MDPs

arXiv:2605.24345v1 Announce Type: new Abstract: In online reinforcement learning, data scarcity creates epistemic uncertainty that makes robustness important early in learning, whereas sufficient expl

safetyarxiv-cs-lg

EvoSci: A Bio-Inspired Multi-Agent Framework for the Evolution of Scientific Discovery

arXiv:2605.24018v1 Announce Type: new Abstract: Large language models (LLMs), have shown strong potential in scientific discovery, yet existing methods still face substantial challenges in the design

agentsarxiv-cs-ai

Exclusive: Canyon Code closes on $5M pre-seed round to bring visibility to agentic application workflows

Agentic artificial intelligence startup Canyon Code stepped out of the shadows today after closing on a 5 million pre-seed funding round. Its mission is to give enterprises the granular controls requi

agentssiliconangle

EXOTIC: An Exact, Optimistic, Tree-Based Algorithm for Min-Max Optimization

arXiv:2508.12479v2 Announce Type: replace-cross Abstract: Min-max optimization arises in many domains such as game theory, adversarial machine learning, etc. For these problems, gradient-based methods

Explainable Attention-Guided Stacked Graph Neural Networks for Malware Detection

arXiv:2508.09801v3 Announce Type: replace-cross Abstract: Malware detection in modern computing environments demands models that are not only accurate but also interpretable and robust to evasive tech

Explainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study

arXiv:2605.24913v1 Announce Type: cross Abstract: Retinal imaging provides a non-invasive window into systemic microvascular health and has emerged as a potential biomarker for systemic diseases. Howe

Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes

arXiv:2605.24912v1 Announce Type: cross Abstract: Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic

Explaining Too Much? Understanding How Large Language Model Reasoning Traces Influence Performance and Metacognition

arXiv:2605.25856v1 Announce Type: cross Abstract: Large Language Model interfaces are increasingly verbose, exposing intermediate reasoning traces alongside final answers. Traces are framed as transpa

ExplainReduce: Generating global explanations from many local explanations

arXiv:2502.10311v3 Announce Type: replace-cross Abstract: Most commonly used non-linear machine learning methods are closed-box models, uninterpretable to humans. The field of explainable artificial i

Exploration of Perceptual Speech Features for Clinical Decision-Support in Mental Health Care

arXiv:2605.24678v1 Announce Type: new Abstract: Speech and language technologies offer valuable opportunities for supporting mental health assessment through objective and interpretable cues. We prese

Explore Before You Solve: The Speed--Depth Trade-off in Epistemic Agents for ARC-AGI-3

arXiv:2605.25931v1 Announce Type: new Abstract: We systematically investigate all 25 public ARC-AGI-3 games and find that every one is reachable through non-intelligent strategies: 10 in a single blin

Exploring Profiles of Cognitive Distortions Associated with Mental Health Disorders

arXiv:2605.24996v1 Announce Type: new Abstract: Cognitive distortions, distorted patterns of thinking, have been increasingly studied in computational mental health research. Although they are related

researcharxiv-cs-cl

EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models

arXiv:2605.25477v1 Announce Type: cross Abstract: The ability to efficiently and reliably learn new tasks has been a foundational challenge in robotics. Vision-Language-Action (VLA) models have demons

tutorialsarxiv-cs-ai

Extending Embodied Question Answering from Perception to Decision

model-releasesarxiv-cs-ro

arXiv:2605.25813v1 Announce Type: new Abstract: Embodied Question Answering (EQA) connects perception, reasoning, and interaction within embodied environments. However, existing datasets and benchmark

Extracting Training Data from Diffusion Language Models via Infilling

arXiv:2605.24173v1 Announce Type: cross Abstract: Memorization in large language models has been studied almost exclusively through prefix-conditioned extraction, a natural choice for autoregressive m

Extreme Region Policy Distillation

arXiv:2605.25582v1 Announce Type: cross Abstract: Reinforcement learning for large language models faces a fundamental trade-off between sample efficiency and asymptotic performance: strictly on-polic

Extreme-value forest fire prediction A study of the Loss Function in an Ordinality Scheme

arXiv:2601.03327v3 Announce Type: replace-cross Abstract: Wildfires are highly imbalanced natural hazards in both space and severity, making the prediction of extreme events particularly challenging.

F-GRPO: Don't Let Your Policy Learn the Obvious and Forget the Rare

arXiv:2602.06717v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is commonly based on group sampling to estimate advantages and stabilize policy updates.

Factored Latent Action World Models

arXiv:2602.16229v2 Announce Type: replace Abstract: Learning latent actions from action-free video has emerged as a powerful paradigm for scaling up controllable world model learning. Latent actions p

safetyarxiv-cs-lg

Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting

arXiv:2605.24911v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-au

tutorialsarxiv-cs-ai

FairJudge: Abstention-Aware Multimodal Judges for Fairness and Alignment Evaluation in Text-to-Image Models

arXiv:2510.22827v3 Announce Type: replace-cross Abstract: Evaluating text-to-image (T2I) systems requires judging not only whether an image matches a prompt, but also whether socially salient attribut

safetyarxiv-cs-lg

Faithful or Fabricated? A Causal Framework for Rationalization Bias in LLM Judges

arXiv:2605.23970v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as automatic judges for summarization and dialogue evaluation. Prior work has documented biases such

safetyarxiv-cs-cl

Faithfulness as Information Flow: Evaluating and Training Faithful Chain-of-Thought Reasoning

arXiv:2605.24286v1 Announce Type: cross Abstract: Chain-of-thought (CoT) reasoning is useful for monitoring language models only when the reasoning trace faithfully reflects the computation that produ

safetyarxiv-cs-cl

Faithfulness Metrics Don't Measure Faithfulness: A Meta-Evaluation with Ground Truth

model-releasesarxiv-cs-cl

arXiv:2605.25052v1 Announce Type: new Abstract: Chains of thought (CoTs) have become central in interpreting and auditing behaviors of large language models. Yet growing evidence suggests that these t

False Fixed Points: Kantian Feedback, Stable Miscalibration, and Representational Compression in LLMs

arXiv:2510.14925v4 Announce Type: replace Abstract: High-confidence errors in large language models are often treated as fragile failures. We study an alternative: some errors may be false fixed point

Feature Learning in Wide Neural Networks under muP: Identifiability and Sparse-Dictionary Decomposition of the Mean-Field Limit

model-releasesarxiv-cs-lg

arXiv:2605.24710v1 Announce Type: new Abstract: We establish four structural results for feature learning in wide two-layer neural networks under the Maximal Update Parametrization (muP). First, we pr

Feature Lottery? A Bifurcation Theory of Concept Emergence

arXiv:2605.24057v1 Announce Type: cross Abstract: Neural networks acquire structured representations at specific moments during training, yet identifying these transitions typically relies on retrospe

Feature Resemblance: Towards a Theoretical Understanding of Analogical Reasoning in Transformers

arXiv:2603.05143v3 Announce Type: replace Abstract: Understanding reasoning in large language models is complicated by evaluations that conflate multiple reasoning types. We isolate analogical reasoni

researcharxiv-cs-cl

Federated Learning over Human-Body Communication for On-Body Edge Intelligence: A Survey, Taxonomy, and BODYFED-HBC Scheduling Vignette

arXiv:2605.24062v1 Announce Type: cross Abstract: Human-body communication (HBC) is a promising physical substrate for wearable body-area networks because it can localize communication around the body

Federated Sketching LoRA: A Flexible Framework for Heterogeneous Collaborative Fine-Tuning of LLMs

arXiv:2501.19389v4 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) on resource-constrained clients remains a challenging problem. Recent works have fused low-rank adaptation

researcharxiv-cs-lg

Fermi-Dirac machines as quantizations of neurons

arXiv:2605.24386v1 Announce Type: cross Abstract: Fermi-Dirac machines were proposed recently as an approach to solving semidefinite optimization problems on quantum computers. Here, we reinterpret th

tutorialsarxiv-cs-lg

Few-Shot Neural Differentiable Simulator: Real-to-Sim Rigid-Contact Modeling

arXiv:2603.06218v2 Announce Type: replace Abstract: Accurate physics simulation is essential for robotic learning and control, yet analytical simulators often fail to capture complex contact dynamics,

safetyarxiv-cs-ro

FG-CLIP 2: A Bilingual Fine-grained Vision-Language Alignment Model

arXiv:2510.10921v3 Announce Type: replace-cross Abstract: Fine-grained vision-language understanding requires precise alignment between visual content and linguistic descriptions, a capability that re

Industry

Filing: Monzo reports its 'refer a friend' payouts grew 40% YoY to £29.5M for the 12 months ending March 2026, as part of a broader £143M marketing spend (Financial Times)

Financial Times: Filing: Monzo reports its “refer a friend” payouts grew 40% YoY to £29.5M for the 12 months ending March 2026, as part of a broader £143M marketing spend — London-based fintech record

industrytechmeme

Filtered Posterior Mean Collections: A Unified Framework for Analytical Models of Diffusion Generalization

arXiv:2605.24192v1 Announce Type: cross Abstract: The neural-network denoising functions which form the backbone of image diffusion models are remarkably consistent in their generalization behaviour a

Fine-Tuning and Serving Gemma 4 31B on Google Cloud TPU: A Technical Comparison with GPU Baselines

arXiv:2605.25645v1 Announce Type: cross Abstract: We present the first end-to-end demonstration of fine-tuning and serving Google's Gemma 4 31B model on TPU hardware, providing an empirical comparison

Fine-Tuning Language Models to Know What They Know

arXiv:2602.02605v2 Announce Type: replace-cross Abstract: Evaluating true metacognition in Large Language Models (LLMs) is difficult due to biases and heuristics. This paper presents a framework to me