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Rebecca Torrence / Bloomberg: Sources: Fireworks AI, which helps companies run AI models, is in talks to raise funding at a 15B valuation after being valued at 4B in October 2025 — Fireworks AI, a sta

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Squeezing Capacity from Multimodal Large Language Models for Subject-driven Generation

arXiv:2605.26111v1 Announce Type: cross Abstract: Subject-driven image generation aims to synthesize new images that preserve the identity of the given subject while following textual instructions. Ex

researcharxiv-cs-ai

The Impact of Large Language Models on Open-source Innovation: Evidence from GitHub Copilot

arXiv:2409.08379v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are reshaping knowledge work, yet their impact on voluntary, self-guided open innovation forums (contributors cho

researcharxiv-cs-ai

Understanding and Mitigating Premature Confidence for Better LLM Reasoning

arXiv:2605.24396v1 Announce Type: new Abstract: Long chains of thought (CoT) from current language models frequently contain logical gaps and unjustified leaps, limiting the gains from additional test

Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation

arXiv:2605.25903v1 Announce Type: new Abstract: Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each mo

When In-Distribution Gains Fail: Evaluating Weak-to-Strong Reward Models under Preference Shift

arXiv:2605.25629v1 Announce Type: new Abstract: Weak-to-strong (W2S) generalization is a promising framework for scalable oversight, yet existing evaluations often test students under matched train--t

Why Agentic Theorem Prover Works: A Statistical Provability Theory of Mathematical Reasoning Models

arXiv:2602.10538v3 Announce Type: replace-cross Abstract: Agentic theorem provers combine a reasoning model, retrieval, search, and a proof assistant verifier, yet it remains unclear which components

agentsarxiv-cs-lg

World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy

arXiv:2602.06508v2 Announce Type: replace Abstract: Reinforcement learning (RL) can refine Vision-Language-Action (VLA) policies beyond behavior cloning, but real-world RL remains expensive due to ext

safetyarxiv-cs-ro

Zero-Shot Parkinson's Disease Detection from Speech: Comparing Large Audio and Language Models

arXiv:2605.24806v1 Announce Type: cross Abstract: Large audio and language models have recently demonstrated zero-shot reasoning capabilities across various domains. However, it remains unclear how th

researcharxiv-cs-ai

A look at the UK's AI Safety Institute, whose researchers probe AI models for safety gaps, as its work becomes a blueprint for other governments' AI policies (New York Times)

New York Times: A look at the UK's AI Safety Institute, whose researchers probe AI models for safety gaps, as its work becomes a blueprint for other governments' AI policies — The government's A.I. Se

safetytechmeme

ChainFlow-VLA: Causal Flow Planning with Vision-Language Models

arXiv:2605.23270v1 Announce Type: cross Abstract: Current end-to-end autonomous driving systems are fundamentally limited by a mismatch between temporal causal reasoning and global trajectory consiste

safetyarxiv-cs-ai

Convex Compositional Reasoning Models

arXiv:2605.23395v1 Announce Type: new Abstract: Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constra

Decomposing and Measuring Evaluation Awareness

arXiv:2605.23055v1 Announce Type: cross Abstract: Frontier language models sometimes recognize that they are being evaluated and adjust their behavior, undermining validity of benchmark results. Yet t

From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills

arXiv:2605.23899v1 Announce Type: new Abstract: Language agents increasingly improve by reusing skills -- structured procedural artifacts distilled from past experience. In particular, domain-level an

agentsarxiv-cs-ai

Hierarchical Concept Geometry in Language Models Emerges from Word Co-occurrence

model-releasesarxiv-cs-cl

arXiv:2605.23821v1 Announce Type: new Abstract: We propose a distributional theory of how hypernymy -- the ``is-a'' relation between general and specific concepts -- is encoded geometrically in langua

Is Dimensionality a Barrier for Retrieval Models?

arXiv:2605.23556v1 Announce Type: new Abstract: Why does the low dimensionality of representations, typically dapprox 1000, not prevent modern embedding-based retrieval models from scaling to billions

It's the humans, not the data: Geopolitical bias in LLMs originates in post-training, amplified by the language of the prompt

arXiv:2605.23825v1 Announce Type: cross Abstract: It has generally been assumed that geopolitical bias in language models originates from the training data used during the pre-training phase. We teste

MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models

arXiv:2605.23007v1 Announce Type: cross Abstract: We explore the application of LLM-driven algorithm optimization to several common tasks in quantitative finance. MadEvolve, a general-purpose algorith

Next-Latent Prediction Transformers Learn Compact World Models

arXiv:2511.05963v2 Announce Type: replace Abstract: Transformers replace recurrence with a memory that grows with sequence length and self-attention that enables ad-hoc lookups over past tokens. Conse

safetyarxiv-cs-lg

Reading Calibrated Uncertainty from Language Model Trajectories

arXiv:2605.22864v1 Announce Type: new Abstract: The maximum softmax probability (MSP) represents a default approach when evaluating uncertainty quantification for language model generation with struct

WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents

arXiv:2605.23220v1 Announce Type: new Abstract: Despite the growing use of world models as decision-making agents, their adversarial robustness remains underexplored due to the lack of dedicated autom

World Machine: Towards Generative World Modeling for Time-Series

arXiv:2605.23025v1 Announce Type: new Abstract: World models represent a paradigm shift in generative AI, pursuing predictive understanding and controllable simulation of environments in a structured

Local Ai

My daily average local model token burn is 17M They have become tremendously useful.

Clem Delangue reports consuming approximately 17 million tokens daily when running local language models, indicating substantial usage of on-device AI inference. He expresses satisfaction with the uti

local-aiclem-delangue--x

24 May 2026

Thinking Machines is impressive. In a couple hours I just fine tuned my own Qwen3.5-397B model this afternoon. Fast usable multimodal is als…

researchsoumith-chintala--x

Thinking Machines is impressive. In a couple hours I just fine tuned my own Qwen3.5-397B model this afternoon. Fast usable multimodal is also going to enable very mind-blowing personal AI. People talk

24 May 2026

Industry

A 6-person team is building task-specific AI models that are 4-8x faster than anything from OpenAI or Anthropic. 500K downloads on HuggingFa…

A 6-person team is building task-specific AI models that are 4-8x faster than anything from OpenAI or Anthropic. 500K downloads on HuggingFace. No hype. Just better engineering winning on the merits.

industryclem-delangue--x

[AINews] All Model Labs are now Agent Labs

Latent Space reports on a rebranding or reorganization where Model Labs have been renamed or converted into Agent Labs, reflecting a shift in focus toward AI agent development and capabilities. This c

agentslatent-space

Local Ai

Disentanglement Beyond Generative Models with Riemannian ICA

arXiv:2605.22531v1 Announce Type: new Abstract: There is a gap between the theoretical foundations of disentanglement and the practice of modern representation learning. Existing theoretical framework

local-aiarxiv-cs-lg

Local Ai

EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning

arXiv:2605.22506v1 Announce Type: cross Abstract: Federated learning faces increasing threats from model poisoning attacks, which harms its application to improve privacy. Existing defense methods typ

local-aiarxiv-cs-lg

Finite-Particle Convergence Rates for Conservative and Non-Conservative Drifting Models

arXiv:2605.22795v1 Announce Type: cross Abstract: We propose and analyze a conservative drifting method for one-step generative modeling. The method replaces the original displacement-based drifting v

Generative Modeling by Value-Driven Transport

arXiv:2605.22507v1 Announce Type: new Abstract: We propose a new framework for generative modeling based on a discrete-time stochastic control formulation of measure transport. Adapting classic result

safetyarxiv-cs-lg

Google’s new anything-to-anything AI model is wild

model-releasesthe-verge-ai

Last year I deepfaked my kid's stuffed animal to make it look like his plush deer was on vacation. It was an experiment to see if I could re-create the events depicted in a Gemini ad Google was runnin

it’s truly astonishing to see the entire field move in the neurosymbolic and world model directions I advocated in 2019 and 2020 and then si…

it’s truly astonishing to see the entire field move in the neurosymbolic and world model directions I advocated in 2019 and 2020 and then simultaneously see people claim (invariably without specific e

safetygary-marcus--x

SepsisAI Orchestrator: A Containerized and Scalable Platform for Deploying AI Models and Real-Time Monitoring in Early Sepsis Detection

arXiv:2605.22331v1 Announce Type: new Abstract: Despite strong predictive results in the clinical machine learning literature, the translation of these models into bedside use remains limited by syste

Tutorials

A Tutorial on Diffusion Theory: From Differential Equations to Diffusion Models

arXiv:2605.22586v1 Announce Type: cross Abstract: This tutorial develops diffusion models from the viewpoint of differential equations. We begin with the conditional Gaussian forward process and show

tutorialsarxiv-cs-cl

An Open Multi-Center Whole-Body FDG PET/CT Foundation Model for Tumor Segmentation

arXiv:2605.21835v1 Announce Type: cross Abstract: The synergistic interpretation of anatomical information from computed tomography (CT) and metabolic information from positron emission tomography (PE

researcharxiv-cs-cv

Cohere Command A+ is now available in Microsoft Foundry as a Managed Compute offer. Cohere’s latest open-source model is built for enterpris…

Cohere Command A+ is now available in Microsoft Foundry as a Managed Compute offer. Cohere’s latest open-source model is built for enterprise-grade agentic AI workloads, bringing together reasoning, m

agentscohere--x

Tutorials

Conceptualizing Embeddings: Sparse Disentanglement for Vision-Language Models

arXiv:2605.22679v1 Announce Type: new Abstract: Vision-language models learn powerful multimodal embeddings, yet their internal semantics remain opaque. While sparse autoencoders (SAEs) can extract in

tutorialsarxiv-cs-cv

From Correlation to Cause: A Five-Stage Methodology for Feature Analysis in Transformer Language Models

arXiv:2605.22462v1 Announce Type: new Abstract: We propose a five-stage methodology for causal feature analysis in transformer language models (probe design, feature extraction, causal validation, rob

Tutorials

i need someone at @OpenAI and @AnthropicAI to teach the models that while prototyping, backwards compatibility is just a bad idea

tutorialsjeremy-howard--x

Jeremy Howard argues that AI model developers at OpenAI and Anthropic should prioritize breaking backwards compatibility during the prototyping phase rather than maintaining it, suggesting that backwa

In Silico Modeling of the RAMPHO Buffer: Dissociating Informational and Energetic Masking via Phonetic Entropy in Deep Neural Networks

arXiv:2605.22465v1 Announce Type: new Abstract: The fundamental challenge of listening in multi-talker environments is a cognitive bottleneck, defined by the Ease of Language Understanding (ELU) model

NEW paper worth reading. A full agentic workflow can be distilled into model weights and run at roughly 100x lower inference cost while pres…

NEW paper worth reading. A full agentic workflow can be distilled into model weights and run at roughly 100x lower inference cost while preserving near-frontier task quality. The workflow includes mul

agentsdair-ai--x

3D Reconstruction and Knowledge Distillation to Improve Multi-View Image Models to Explore Spike Volume Estimation in Wheat

arXiv:2605.20940v1 Announce Type: new Abstract: Accurate estimation of wheat spike volume is important for yield component analysis and stress resilience assessment, yet field-based measurement remain

safetyarxiv-cs-cv

Hardware

Accelerating Video Inverse Problem Solvers with Autoregressive Diffusion Models

arXiv:2605.20624v1 Announce Type: new Abstract: Diffusion models provide powerful priors for zero-shot video inverse problems, but their real-time deployment is hindered by two inefficiencies: high in

hardwarearxiv-cs-cv

AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation

arXiv:2605.20303v1 Announce Type: new Abstract: Airfoil shape design is a fundamental task in aerospace engineering, with a direct impact on flight stability and fuel consumption. Deep learning has re

Applications

CoarseSoundNet: Building a reliable model for ecological soundscape analysis

arXiv:2605.21143v1 Announce Type: cross Abstract: A soundscape is composed of three types of sound: biophony (sounds made by animals), geophony (natural abiotic sounds) and anthropophony (sounds made

applicationsarxiv-cs-lg

Efficient Learning of Deep State Space Models via Importance Smoothing

arXiv:2605.21108v1 Announce Type: new Abstract: Latent state space systems are ubiquitous in statistical modelling, arising naturally when a time series is observed through a noisy measurement functio

Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models

arXiv:2605.20950v1 Announce Type: new Abstract: Vision-Language Models (VLMs) face a bottleneck of prohibitive computational costs arising from massive visual token sequences during inference. Existin

researcharxiv-cs-cv

Generation of Heterogeneous PET Images from Uniform Organ Activity Maps Using a Pretrained Domain-Adapted Diffusion Model

arXiv:2605.20267v1 Announce Type: new Abstract: Synthetic PET images are valuable for quantitative imaging workflow development, scalable virtual imaging trials, and deep learning model training, but

researcharxiv-cs-cv