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Anthropic launches Claude for Small Business, featuring a host of automated services like bookkeeping functions, business insights, and tools for ad campaigns (Lucas Ropek/TechCrunch)

Lucas Ropek / TechCrunch: Anthropic launches Claude for Small Business, featuring a host of automated services like bookkeeping functions, business insights, and tools for ad campaigns — Anthropic is

model-releasestechmeme

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Anthropic unveils Claude Agent SDK credits for paid plans, which users can allocate for programmatic use of third-party agents like OpenClaw, starting June 15 (Carl Franzen/VentureBeat)

Carl Franzen / VentureBeat: Anthropic unveils Claude Agent SDK credits for paid plans, which users can allocate for programmatic use of third-party agents like OpenClaw, starting June 15 — Good news,

model-releasestechmeme

Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information

arXiv:2605.11609v1 Announce Type: cross Abstract: On-policy self-distillation, where a student is pulled toward a copy of itself conditioned on privileged context (e.g., a verified solution or feedbac

safetyarxiv-cs-cl

Industry

Anyone else feel like ChatGPT is weirdly good at understanding what you mean even when your prompt is messy?

This Reddit discussion explores users' observations about ChatGPT's ability to interpret and respond appropriately to poorly formatted, unclear, or incomplete prompts. The thread likely contains commu

industryr-chatgpt

AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors In Optical Inspection

arXiv:2605.12430v1 Announce Type: new Abstract: Segmentation models in automated optical inspection of wire-bonded semiconductors are typically device-specific and must be re-trained when new devices

researcharxiv-cs-cv

Approximating Simple ReLU Networks based on Spectral Decomposition of Fisher Information

arXiv:2505.17907v2 Announce Type: replace-cross Abstract: Properties of Fisher information matrices of 2-layer neural ReLU networks with random hidden weights are studied. For these networks, it is kn

Approximation of Maximally Monotone Operators : A Graph Convergence Perspective

arXiv:2605.12301v1 Announce Type: new Abstract: Operator learning has been highly successful for continuous mappings between infinite-dimensional spaces, such as PDE solution operators. However, many

Approximation Theory of Laplacian-Based Neural Operators for Reaction-Diffusion System

arXiv:2605.12025v1 Announce Type: new Abstract: Neural operators provide a framework for learning solution operators of partial differential equations (PDEs), enabling efficient surrogate modeling for

Arbitrated Indirect Treatment Comparisons

arXiv:2510.18071v2 Announce Type: replace-cross Abstract: Matching-adjusted indirect comparison (MAIC) has been increasingly employed in health technology assessments (HTA). By reweighting subjects fr

arepsilon-Good Action Identification in Fixed-Budget Monte Carlo Tree Search

arXiv:2605.11324v1 Announce Type: new Abstract: We study the fixed-budget max-min action identification problem in depth-2 max-min trees, an important special case of Monte Carlo Tree Search. A learne

As President Trump meets President Xi this week, a call to the American AI community: If your startup, lab, non-profit or company benefits f…

model-releasesclem-delangue--x

As President Trump meets President Xi this week, a call to the American AI community: If your startup, lab, non-profit or company benefits from open international AI - especially Chinese (Deepseek, Qw

ASD-Bench: A Four-Axis Comprehensive Benchmark of AI Models for Autism Spectrum Disorder

arXiv:2605.11091v1 Announce Type: new Abstract: Automated ASD screening tools remain limited by single-architecture evaluations, axis-restricted assessment, and near-exclusive focus on adult cohorts,

ASIP-Planner: Adaptive Planning for UAV Surface Inspection in Partially Known Indoor Environments

arXiv:2605.11119v1 Announce Type: new Abstract: Indoor infrastructure inspection, such as tunnels and industrial facilities, requires systematic surface coverage to ensure that all inspection targets

applicationsarxiv-cs-ro

Assessment of cloud and associated radiation fields from a GAN stochastic cloud subcolumn generator

arXiv:2605.11968v1 Announce Type: cross Abstract: Modern Earth System Models (ESMs) operate on horizontal scales far larger than typical cloud features, requiring stochastic subcolumn generators to re

safetyarxiv-cs-lg

Asymmetric Advantage Modulation Calibrates Entropy Dynamics in RLVR

arXiv:2604.04894v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of large language models (LLMs), but it often

safetyarxiv-cs-cl

Industry

Asymmetry of capabilities is when there is great risk. Diffusion of capabilities maintains an adversarial equilibrium and stability. Don't l…

Asymmetry of capabilities is when there is great risk. Diffusion of capabilities maintains an adversarial equilibrium and stability. Don't let the fear-mongerers win. We asked the CEO of HuggingFace @

industryclem-delangue--x

Agents

Attacks and Mitigations for Distributed Governance of Agentic AI under Byzantine Adversaries

arXiv:2605.12364v1 Announce Type: cross Abstract: Agentic AI governance is a critical component of agentic AI infrastructure ensuring that agents follow their owner's communication and interaction pol

agentsarxiv-cs-lg

Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild Video

arXiv:2512.12165v3 Announce Type: replace Abstract: Understanding camera motion is a fundamental problem in embodied perception and 3D scene understanding. While visual methods have advanced rapidly,

applicationsarxiv-cs-cv

Augmented Lagrangian Method for Last-Iterate Convergence for Constrained MDPs

arXiv:2605.11694v1 Announce Type: new Abstract: We study policy optimization for infinite-horizon, discounted constrained Markov decision processes (CMDPs). While existing theoretical guarantees typic

safetyarxiv-cs-lg

Hardware

AutoLLMResearch: Training Research Agents for Automating LLM Experiment Configuration -- Learning from Cheap, Optimizing Expensive

arXiv:2605.11518v1 Announce Type: cross Abstract: Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and beyond, is crucial f

hardwarearxiv-cs-cl

AutoMonitor-Bench: Evaluating the Reliability of LLM-Based Misbehavior Monitor

model-releasesarxiv-cs-cl

arXiv:2601.05752v3 Announce Type: replace Abstract: We introduce AutoMonitor-Bench, the first benchmark designed to systematically evaluate the reliability of LLM-based misbehavior monitors across div

Autoregressive Learning in Joint KL: Sharp Oracle Bounds and Lower Bounds

arXiv:2605.12316v1 Announce Type: new Abstract: We study the fundamental and timely problem of learning long sequences in autoregressive modeling and next-token prediction under model misspecification

safetyarxiv-cs-lg

Tools

ayy @teej_m thanks for reading

This is a casual thank-you post from Swyx acknowledging @teej_m for reading his content on X (formerly Twitter). The post appears to be a brief, informal social media acknowledgment without substantiv

toolsswyx--x

B4DL: A Benchmark for 4D LiDAR LLM in Spatio-Temporal Understanding

arXiv:2508.05269v2 Announce Type: replace Abstract: Understanding dynamic outdoor environments requires capturing complex object interactions and their evolution over time. LiDAR-based 4D point clouds

Local Ai

b9129

local-aillama-cpp-releases

b9129 is a build release of llama.cpp, a C/C++ implementation for LLM inference . As part of the llama.cpp project's release cycle, this build represents an intermediate development version containing

Local Ai

b9134

local-aillama-cpp-releases

B9134 is a recent build release of llama.cpp, an open-source C/C++ library for efficient large language model inference. The release was tagged on May 13, 2026, representing an ongoing development ite

Backbone-Equated Diffusion OOD via Sparse Internal Snapshots

arXiv:2605.11014v1 Announce Type: new Abstract: Fair comparison between diffusion-based OOD detectors is challenging, as conclusions can vary with backbone choice, corruption parameterization, and tes

BARISTA: A Multi-Task Egocentric Benchmark for Compositional Visual Understanding

arXiv:2605.12074v1 Announce Type: new Abstract: Scene understanding is central to general physical intelligence, and video is a primary modality for capturing both state and temporal dynamics of a sce

Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy

arXiv:2412.11875v3 Announce Type: replace-cross Abstract: Surrogate models are often used as computationally efficient approximations to complex simulation models, enabling tasks such as solving inver

applicationsarxiv-cs-lg

BEExformer: A Fast Inferencing Binarized Transformer with Early Exits

model-releasesarxiv-cs-cl

arXiv:2412.05225v3 Announce Type: replace Abstract: Large Language Models (LLMs) based on transformers achieve cutting-edge results on a variety of applications. However, their enormous size and proce

Behavioral Mode Discovery for Fine-tuning Multimodal Generative Policies

arXiv:2605.11387v1 Announce Type: new Abstract: We address the problem of fine-tuning pre-trained generative policies with reinforcement learning (RL) while preserving the multimodality of their actio

Beyond GRPO and On-Policy Distillation: An Empirical Sparse-to-Dense Reward Principle for Language-Model Post-Training

arXiv:2605.12483v1 Announce Type: new Abstract: In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated carefully. The standard practice

Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images

arXiv:2605.12413v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) show strong visual perception, yet remain limited in reasoning about space under changing viewpoints. We study

Agents

Beyond Manual Curation: Augmenting Targeted Protein Degradation Databases via Agentic Literature Extraction Workflows

arXiv:2605.11221v1 Announce Type: cross Abstract: Predictive models in biomedicine depend on structured assay data locked in the text, tables, and supplements of primary publications. This bottleneck

agentsarxiv-cs-lg

Beyond Masks: The Case for Medical Image Parsing

arXiv:2605.11438v1 Announce Type: new Abstract: Medical imaging research has spent a decade getting very good at one thing: producing per-voxel masks. Masks tell us size, volume, and location, and a d

researcharxiv-cs-cv

Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs

arXiv:2605.11857v1 Announce Type: new Abstract: Federated fine-tuning of large language models is commonly formulated as a parameter aggregation problem. However, even parameter-efficient methods requ

Beyond Point Estimates: Distributional Uncertainty in Machine Learning Performance Evaluation

arXiv:2501.16931v2 Announce Type: replace Abstract: Machine learning models are often evaluated using point estimates of performance metrics such as accuracy, F1 score, or mean squared error. Such sum

Beyond Point-wise Neural Collapse: A Topology-Aware Hierarchical Classifier for Class-Incremental Learning

arXiv:2605.11904v1 Announce Type: new Abstract: The Nearest Class Mean (NCM) classifier is widely favored in Class-Incremental Learning (CIL) for its superior resistance to catastrophic forgetting com

safetyarxiv-cs-cv

Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification

arXiv:2605.11460v1 Announce Type: new Abstract: System identification (SysID) is critical for modeling dynamical systems from experimental data, yet traditional approaches often fail to capture nonlin

Beyond Similarity: Temporal Operator Attention for Time Series Analysis

arXiv:2605.11287v1 Announce Type: new Abstract: A persistent paradox in time-series forecasting is that structurally simple MLP and linear models often outperform high-capacity Transformers. We argue

Beyond Text Prompts: Visual-to-Visual Generation as A Unified Paradigm

arXiv:2605.12271v1 Announce Type: new Abstract: Humans often specify and create through visual artifacts: typography sheets, sketches, reference images, and annotated scenes. Yet modern visual generat

Agents

big dbay 👀 What’s everyone most excited for

This X post from Harrison Chase likely discusses anticipated features or developments in a major product update or release, inviting community feedback on what users are most excited about. The post a

agentsharrison-chase--x

Bin Latent Transformer (BiLT): A shift-invariant autoencoder for calibration-free spectral unmixing of turbid media

arXiv:2605.11829v1 Announce Type: cross Abstract: The accurate recovery of constituent-level optical properties from integrating sphere measurements is a central analytical challenge in pharmaceutical

Birds of a Feather Flock Together: Background-Invariant Representations via Linear Structure in VLMs

arXiv:2605.11107v1 Announce Type: new Abstract: Vision-language models (VLMs), such as CLIP and SigLIP 2, are widely used for image classification, yet their vision encoders remain vulnerable to syste

applicationsarxiv-cs-cv

BitLM: Unlocking Multi-Token Language Generation with Bitwise Continuous Diffusion

arXiv:2605.11577v1 Announce Type: new Abstract: Autoregressive language models generate text one token at a time, yet natural language is inherently structured in multi-token units, including phrases,

researcharxiv-cs-cl

BLOCK-EM: Preventing Emergent Misalignment via Latent Blocking

arXiv:2602.00767v2 Announce Type: replace Abstract: Emergent misalignment can arise when a language model is fine-tuned on a narrowly scoped supervised objective: the model learns the target behavior,

Block-R1: Rethinking the Role of Block Size in Multi-domain Reinforcement Learning for Diffusion Large Language Models

arXiv:2605.11726v1 Announce Type: new Abstract: Recently, reinforcement learning (RL) has been widely applied during post-training for diffusion large language models (dLLMs) to enhance reasoning with

Industry

Blue Origin may need external funding to hit ambitious launch targets

Blue Origin CEO Dave Limp told employees that the company needs external capital to significantly increase launch frequency, stating that achieving established launch goals requires 'a large amount of

industryars-technica