Search: “models”

Chrome Enterprise rolls out AI agents and automation to streamline security management

model-releasessiliconangle

Google LLC today launched new enhancements to Chrome Enterprise, the company’s enterprise version of its Chrome browser, designed to provide greater administrative and security control to information

Model Releases

Claude Opus 4.8 is now available in Windsurf and Devin CLI

DGX agent

Claude Opus 4.8 has been made available for use in both Windsurf (Cognition AI's code editor) and Devin CLI (their command-line interface). This release expands access to Anthropic's latest Claude mod

model-releasescognition-ai--x

28 May 2026

ClinConsensus: A Physician-Calibrated Benchmark for Evaluating Clinical Rubric Coverage in Chinese Medical LLMs

model-releasesarxiv-cs-cl

arXiv:2603.02097v5 Announce Type: replace Abstract: Open-ended medical LLM evaluation remains weakly grounded in physician-calibrated coverage of clinically relevant response criteria, especially in l

ClothTransformer: Unified Latent-Space Transformers for Scalable Cloth Simulation

arXiv:2605.27852v1 Announce Type: cross Abstract: Unified and scalable Transformers have recently achieved remarkable success in modeling diverse phenomena traditionally associated with computer graph

researcharxiv-cs-cv

CogPortrait: Fine-Grained Eye-Region Control in Portrait Animation via Hierarchical Agent Planning

model-releasesarxiv-cs-cv

arXiv:2605.28056v1 Announce Type: new Abstract: Portrait animation methods have achieved substantial visual quality and lip synchronization, but fine-grained manipulation of the eye region still faces

Conditionally Site-Independent Neural Evolution of Antibody Sequences

arXiv:2602.18982v4 Announce Type: replace Abstract: Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independ

researcharxiv-cs-lg

Applications

Context Features Are Cheap: Rank-Aware Decomposition for Efficient Feature Interaction in Recommender Systems

arXiv:2605.27450v1 Announce Type: cross Abstract: Modern industrial recommender systems use a deep ranking model to score N candidates against the same user and context features. Standard implementati

applicationsarxiv-cs-lg

CORE: Contrastive Reflection Enables Rapid Improvements in Reasoning

arXiv:2605.28742v1 Announce Type: new Abstract: Language models can use verifiable rewards to improve at a wide variety of reasoning tasks. However, both parametric (e.g. RLVR) and non-parametric (e.g

Safety

COTTA: Context-Aware Transfer Adaptation for Trajectory Prediction in Autonomous Driving

arXiv:2604.00402v2 Announce Type: replace-cross Abstract: Developing robust models to accurately predict the trajectories of surrounding agents is fundamental to autonomous driving safety. However, mo

safetyarxiv-cs-ai

DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants

arXiv:2512.00252v4 Announce Type: replace-cross Abstract: Data assimilation (DA) is a cornerstone of scientific and engineering applications, combining model forecasts with sparse and noisy observatio

researcharxiv-cs-lg

Dark Quest II: A Wide-Coverage Neural Network Emulator of the Nonlinear Matter Power Spectrum Across Extended Cosmologies

arXiv:2605.28596v1 Announce Type: cross Abstract: extsc{DarkEmulator2} is a neural network emulator of the nonlinear matter power spectrum in a nine-dimensional w_0 w_a nu o CDM parameter space, devel

DecomposeRL: Learning to Ask Useful, Informative, and Diverse Questions for Semi-Supervised, Traceable Claim Verification

arXiv:2605.27858v1 Announce Type: cross Abstract: Claim verification splits between end-to-end classifiers that are accurate but yields no inspectable traces, and decomposition-based methods produce i

Safety

DREAM-R: Multimodal Speculative Reasoning with RL-Based Refined Drafting, Precise Verification, and Fully Parallel Execution

arXiv:2605.28678v1 Announce Type: new Abstract: Speculative reasoning has recently been proposed as a means to accelerate reasoning-intensive generation in large multimodal models, but its effectivene

safetyarxiv-cs-ai

DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation

model-releasesarxiv-cs-cl

arXiv:2603.21465v2 Announce Type: replace Abstract: Developing efficient CUDA kernels is a fundamental yet challenging task in the generative AI industry. Recent research leverages Large Language Mode

Agents

E^3-Agent: An Executable and Evolving Agent for Resource Management of Edge Generative Inference

arXiv:2605.27428v1 Announce Type: new Abstract: Edge deployments of generative inference increasingly face two practical realities: per-device per-model performance is often unknown at deployment time

agentsarxiv-cs-lg

EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents

arXiv:2605.27820v1 Announce Type: new Abstract: As AI agents increasingly operate in open, real-world environments, they require a deep synergy of multimodal perception, tool invocation with multi-hop

Energy-Structured Low-Rank Adaptation for Continual Learning

arXiv:2605.27482v1 Announce Type: cross Abstract: While orthogonal subspace methods try to mitigate task interference in Continual Learning (CL), they often suffer from energy diffusion across the bas

ESC-Skills: Discovering and Self-Evolving Skills for Emotional Support Conversations

arXiv:2605.27908v1 Announce Type: cross Abstract: Existing emotional support conversation (ESC) systems mainly rely on end-to-end response generation or coarse strategy supervision, offering limited i

Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects

model-releasesarxiv-cs-cv

arXiv:2605.28270v1 Announce Type: new Abstract: Estimating the 9D pose of everyday objects from a single real-world image remains challenging. This is largely due to the lack of large-scale supervisio

Applications

From Causal Discovery to Dynamic Causal Inference in Neural Time Series

arXiv:2603.20980v2 Announce Type: replace-cross Abstract: Time-varying causal models provide a powerful framework for studying dynamic scientific systems, yet most existing approaches assume that the

applicationsarxiv-cs-ai

From Fact Overwriting to Knowledge Evolution: Causal Editing via On-Policy Self-Distillation

arXiv:2605.28303v1 Announce Type: new Abstract: While Knowledge Editing (KE) enables efficient updates, its dominant Static Fact Overwriting paradigm treats LLMs as discrete databases, forcibly inject

Safety

Geometry of Human Perceptual Domains Emerges Transiently in LLM Representations

arXiv:2605.27970v1 Announce Type: new Abstract: While large language models (LLMs) are trained purely on textual data, prior work has shown that their internal representations can exhibit rich geometr

safetyarxiv-cs-ai

glad to know Mythos' safety concerns have been addressed right as Anthropic also secured tens of billions in inference compute 👍

model-releasesjeremy-howard--x

glad to know Mythos' safety concerns have been addressed right as Anthropic also secured tens of billions in inference compute 👍 JUST IN: Anthropic announces it will roll out Claude Mythos “in the com

Applications

GraD-IBD: Graph Representation Learning from Diagnosis Trajectories for Early Detection of Inflammatory Bowel Disease

arXiv:2605.27799v1 Announce Type: new Abstract: International Classification of Diseases (ICD) is a globally recognized coding system that records diagnostic events during each patient encounter, prov

applicationsarxiv-cs-ai

Graph-of-Skills: Dependency-Aware Structural Retrieval for Massive Agent Skills

arXiv:2604.05333v3 Announce Type: replace Abstract: Modern LLM agents increasingly rely on reusable skills, and as they interact with personal applications, web browsers, and other interfaces, skill l

HRBench: Benchmarking and Understanding Thinking-Mode Switch Strategies in Hybrid-Reasoning LLMs

arXiv:2605.28398v1 Announce Type: new Abstract: Hybrid-reasoning large language models (LLMs) expose explicit controls over reasoning effort, allowing users or systems to trade off answer quality agai

HumanoidMimicGen: Data Generation for Loco-Manipulation via Whole-Body Planning

arXiv:2605.27724v1 Announce Type: cross Abstract: Imitation learning is a promising approach for training humanoid robots to both walk and manipulate, but it requires a large number of demonstrations,

I had early access to Opus 4.8. Was impressed by it. Here is Opus 4.8's one shot of 'create a visually interesting shader that can run in tw…

model-releasessonya-huang--x

I had early access to Opus 4.8. Was impressed by it. Here is Opus 4.8's one shot of 'create a visually interesting shader that can run in twigl, make it like an infinite city of neo-gothic towers part

I tried the liteparse's web browser version today to convert a couple of PDF to text and was shocked at the speed. I had to recheck twice to…

model-releasesjerry-liu--x

I tried the liteparse's web browser version today to convert a couple of PDF to text and was shocked at the speed. I had to recheck twice to see whether it even did the complete processing or not 😅 ht

Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following

model-releasesarxiv-cs-ro

arXiv:2605.28736v1 Announce Type: new Abstract: This study presents the first evaluation of general-purpose imitation learning for surgeon-robot collaborative assistance in open surgery, targeting sut

InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

arXiv:2510.15859v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has driven recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be compute

MACReD: A Multi-Agent Collaborative Reasoning Framework for Reaction Diagram Parsing

arXiv:2605.28077v1 Announce Type: new Abstract: Parsing chemical reaction diagrams from scientific literature is challenging due to heterogeneous layouts, intertwined visual elements, and the difficul

Mahalanobis PatchCore: Covariance-Aware and Streaming-Compatible Industrial Anomaly Detection

arXiv:2605.27748v1 Announce Type: cross Abstract: Industrial visual anomaly detection is usually one-class: normal images are abundant, while defects are rare, heterogeneous, and often unavailable dur

MangaFlow: An End-to-End Agentic Framework for Controllable Story to Manga Generation

model-releasesarxiv-cs-cv

arXiv:2605.28173v1 Announce Type: new Abstract: End-to-end manga generation is a structured visual storytelling task that requires story decomposition, recurring character and scene grounding, page la

Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs

arXiv:2605.28388v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Reward (RLVR) is empirically shown to notably enhance the reasoning performance of large language models (LLMs),

Meta-Attention: Bayesian Per-Token Routing for Efficient Transformer Inference

arXiv:2605.28384v1 Announce Type: new Abstract: Standard transformer architectures apply a single attention mechanism uniformly across all tokens and sequence positions, irrespective of local context

Mitigating Staleness in Asynchronous Pipeline Parallelism via Basis Rotation

arXiv:2602.03515v2 Announce Type: replace-cross Abstract: Asynchronous pipeline parallelism maximizes hardware utilization by eliminating the pipeline bubbles inherent in synchronous execution, offeri

Local Ai

MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Poisoning Attacks

arXiv:2502.17832v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has become a common practice in multimodal large language models (MLLM) to enhance factual grounding and

local-aiarxiv-cs-ai

Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Data

arXiv:2602.23602v2 Announce Type: replace-cross Abstract: Heteroscedasticity -- where the variance of a variable changes with other variables -- is pervasive in real data, and elucidating why it arise

Path Channels and Plan Extension Kernels: a Mechanistic Description of Planning in a Sokoban RNN

arXiv:2506.10138v3 Announce Type: replace-cross Abstract: We partially reverse-engineer a convolutional recurrent neural network (RNN) trained with model-free reinforcement learning to play the box-pu

Pattern Recognition Tasks with Personalized Federated Learning

arXiv:2605.27816v1 Announce Type: new Abstract: Personalized Federated Learning (PFL) constitutes a novel paradigm that tailors Machine Learning (ML) models to individual clients, thereby furnishing p

researcharxiv-cs-cv

PEAR: Pairwise Evaluation for Automatic Relative Scoring in Machine Translation

model-releasesarxiv-cs-cl

arXiv:2601.18006v2 Announce Type: replace Abstract: We present PEAR (Pairwise Evaluation for Automatic Relative Scoring), a supervised quality estimation (QE) metric family that reframes reference-fre

Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control

arXiv:2601.15015v2 Announce Type: replace Abstract: Reinforcement learning (RL) has shown promising results in active flow control (AFC), yet progress in the field remains difficult to assess as exist

Pressure-Testing Deception Probes in LLMs: Scaling, Robustness, and the Geometry of Deceptive Representations

arXiv:2605.27958v1 Announce Type: cross Abstract: Linear probes trained on LLM activations are increasingly proposed as deception-detection metrics, yet report AUROC exceeding 0.96 on clean benchmarks

Tutorials

PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation

arXiv:2605.28634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising paradigm for generalist robotic policies, yet their adaptation is hindered by data inefficiency an

tutorialsarxiv-cs-ro

Privately Estimating Monotone Statistics in Polynomial Time

arXiv:2605.27912v1 Announce Type: cross Abstract: We study efficient differentially private algorithms for estimating monotone statistics, i.e., statistics that are monotone under the addition of new

Proprio: Latent Self-Scoring and Inference-Time Refinement for Physically Plausible Video Generation

arXiv:2605.28230v1 Announce Type: new Abstract: Modern video generative models produce visually impressive results, yet frequently violate basic physical principles. We propose Proprio, a training-fre

researcharxiv-cs-cv

ProvMind: Provenance-grounded reasoning for materials synthesis

arXiv:2605.28487v1 Announce Type: new Abstract: Materials process optimization requires reasoning over routes, conditions, tools and causal dependencies, yet most computational formulations flatten sy