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DTop-p MoE: Sparsity-Controlled Dynamic Top-p MoE for Foundation Model Pre-training

arXiv:2512.13996v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts architectures are essential for scaling model capacity efficiently, yet the standard Top-k routing imposes a rigid sparsit

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Dual Mechanisms of Value Expression: Intrinsic vs. Prompted Values in Large Language Models

DGX agent

arXiv:2509.24319v4 Announce Type: replace-cross Abstract: Large language models can express values in two main ways: (1) intrinsic expression, reflecting the model's inherent values learned during tra

safetyarxiv-cs-ai

1 Jun 2026

Dynamical local Frechet curve regression in manifolds

arXiv:2505.05168v3 Announce Type: replace-cross Abstract: Under mild conditions, this paper derives a least-squares local linear Frechet curve predictor for response and regressor evaluated in a separ

researcharxiv-cs-lg

DynaTree: Dynamic Agentic Retrieval Tree for Time-Sensitive News Retrieval

arXiv:2605.31377v1 Announce Type: cross Abstract: Agentic Retrieval-Augmented Generation improves retrieval by integrating planning, tool use, and iterative reasoning, but existing agentic RAG methods

Early Prediction of Future Behavioral Strategy from Process Traces

arXiv:2605.30550v1 Announce Type: new Abstract: Adaptive systems often need to make task-specific decisions about people from limited evidence: a tutor may need to anticipate how a learner will approa

researcharxiv-cs-lg

EBuddy: a workflow orchestrator for industrial human-machine collaboration

arXiv:2603.28579v2 Announce Type: replace Abstract: This paper presents EBuddy, a voice-guided workflow orchestrator for natural human-machine collaboration in industrial environments. EBuddy targets

researcharxiv-cs-ro

EchoRL: Reinforcement Learning via Rollout Echoing

arXiv:2605.31228v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards is an effective route for post-training to strengthen the reasoning capability of large language models

EEmo-Logic: A Unified Dataset and Multi-Stage Framework for Comprehensive Image-Evoked Emotion Assessment

arXiv:2602.01173v3 Announce Type: replace Abstract: Understanding the multi-dimensional attributes and intensity nuances of image-evoked emotions is pivotal for advancing machine empathy and empowerin

researcharxiv-cs-cv

Eero P. Simoncelli, CDS founding member and professor, was elected to the National Academy of Sciences, recognizing “distinguished and conti…

Eero P. Simoncelli, CDS founding member and professor, was elected to the National Academy of Sciences, recognizing “distinguished and continuing achievements in original research.” He studies how bra

researchyann-lecun--x

Effective Biological Representation Learning by Masking Gene Expression

arXiv:2605.31562v1 Announce Type: new Abstract: RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many appli

researcharxiv-cs-lg

Effective Reasoning Chains Reduce Intrinsic Dimensionality

arXiv:2602.09276v2 Announce Type: replace-cross Abstract: Chain-of-thought (CoT) reasoning and its variants have substantially improved the performance of language models on complex reasoning tasks, y

Efficient and Uncertainty-Aware Diffusion Framework for Offline-to-Online Reinforcement Learning

arXiv:2605.30776v1 Announce Type: new Abstract: Offline-to-Online Reinforcement Learning (O2O-RL) leverages an offline, pre-trained policy to minimize costly online interactions. Although data-efficie

safetyarxiv-cs-lg

Efficient Diffusion LLMs via Temporal-Spatial Parallel Decoding and Confidence Extrapolation

arXiv:2605.30753v1 Announce Type: new Abstract: Diffusion-based large language models (dLLMs) support parallel text generation via iterative denoising, yet inference remains latency-heavy because many

EGOSTREAM: A Diagnostic Benchmark for Streaming Episodic Memory in Egocentric Vision

model-releasesarxiv-cs-cv

arXiv:2605.31557v1 Announce Type: new Abstract: Continuous episodic memory is a core capability for autonomous agents operating in dynamic, real-world environments, yet current streaming video benchma

EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs

arXiv:2605.30637v1 Announce Type: new Abstract: Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future he

Tutorials

Eigenvectors of Experts are Training-free Non-collapsing Routers

arXiv:2605.30992v1 Announce Type: new Abstract: Sparse Mixture of Experts (SMoE) architectures improve the training efficiency of Large Language Models (LLMs) by routing input tokens to a selected sub

tutorialsarxiv-cs-lg

ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation

arXiv:2605.30484v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown promise for robotic manipulation, yet most existing policies operate reactively by directly regressing ac

safetyarxiv-cs-ro

Tools

Elastic Build Machines now protect against out of memory builds

Vercel has enhanced its Elastic Build Machines with protections against out-of-memory errors during builds, improving build reliability and preventing failures caused by insufficient memory allocation

toolsvercel-blog

Hardware

Elastic ViTs from Pretrained Models without Retraining

arXiv:2510.17700v2 Announce Type: replace Abstract: Vision foundation models achieve remarkable performance but are only available in a limited set of pre-determined sizes, forcing sub-optimal deploym

hardwarearxiv-cs-cv

ElasticMem: Latent Memory as a Learnable Resource for LLM Agents

arXiv:2605.30690v1 Announce Type: new Abstract: Long-term memory is essential for LLM agents to reason coherently across extended interactions, personalize responses, and reuse past experience. Howeve

Elon was right that what OpenAI did in its for profit turn was shameful. But what he is doing with the SpaceX, at the likely expense of many…

Elon was right that what OpenAI did in its for profit turn was shameful. But what he is doing with the SpaceX, at the likely expense of many people’s retirement funds, is just as shameful. SpaceX bein

safetygary-marcus--x

EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents

arXiv:2605.30924v1 Announce Type: new Abstract: MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. However, existing approaches lack explicit mechanisms for i

EMCEE: Improving Multilingual Capability of LLMs via Bridging Knowledge and Reasoning with Extracted Synthetic Multilingual Context

arXiv:2503.05846v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved impressive progress across a wide range of tasks, yet their heavy reliance on English-centric train

Emergent Languages in Populations of Language Model Agents: From Token Efficiency to Oversight Evasion

arXiv:2605.31170v1 Announce Type: cross Abstract: Monitoring autonomous language model agents currently relies mostly on surface behavior. But what happens when agent populations invent new languages

Empirical Characterization of Inference-Time Elicited Probability Transformations in Large Language Models

arXiv:2603.19262v2 Announce Type: replace-cross Abstract: Large language models increasingly rely on inference-time procedures such as chain-of-thought reasoning, self-refinement, retrieval augmentati

End-to-End Compression for Tabular Foundation Models

arXiv:2602.05649v2 Announce Type: replace Abstract: The long-standing dominance of gradient-boosted decision trees for tabular data has recently been challenged by in-context learning tabular foundati

Industry

Endlich konnte ich FSD in Deutschland testen. Wir sind fast eine Stunde durch Dörfer, über Landstraßen und Autobahnen gefahren. Alles lief p…

Endlich konnte ich FSD in Deutschland testen. Wir sind fast eine Stunde durch Dörfer, über Landstraßen und Autobahnen gefahren. Alles lief perfekt. @PSchnieder, wann retten wir Leben in Deutschland?🇩🇪

industryelon-musk--x

Applications

Enhancing Computer Vision Model Generalization in Warehouse Facilities: A Case Study on Anomaly Detection in Vertical Material Handling Systems

arXiv:2605.31487v1 Announce Type: new Abstract: Deploying computer vision models in Warehouse Facilities traditionally requires extensive resources for camera mounting, image collection, annotation, t

applicationsarxiv-cs-cv

Enhancing Human-Likeness in Reinforcement Learning Agents via Hierarchical Macro Action Quantization

arXiv:2605.30928v1 Announce Type: new Abstract: Human-like agents are a long-standing goal of artificial intelligence. Despite strong performance, most reinforcement learning (RL) agents remain reward

researcharxiv-cs-ro

Enhancing Regime Shift Detection Using Unstructured Data: A Study on the Treasury Market

arXiv:2605.30363v1 Announce Type: cross Abstract: Regime shifts in financial markets reorganise the joint dynamics of asset prices and macro variables, breaking any single-regime calibration. They are

Entropic Projection Alignment: Estimating, Explaining, and Improving Model Performance Under Distribution Shift

arXiv:2605.31250v1 Announce Type: cross Abstract: We propose a unified framework for addressing three key challenges of distribution shift: (1) estimating a model's performance on an unlabeled target

Envisioning Beyond the Few: Disentangled Semantics and Primitives for Few-Shot Atypical Layout-to-Image Generation

arXiv:2605.31266v1 Announce Type: cross Abstract: The layout-to-image (L2I) task enables fine-grained control over image generation via object categories and spatial layouts. However, existing L2I met

Equivariant Latent Alignment via Flow Matching under Group Symmetries

arXiv:2605.30705v1 Announce Type: new Abstract: Geometry-aware generative models and novel view synthesis approaches have shown strong potential in visual fidelity and consistency. In parallel, equiva

safetyarxiv-cs-cv

ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models

arXiv:2605.31251v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to th

Error Amplification Limits ANN-to-SNN Conversion in Continuous Control

arXiv:2601.21778v2 Announce Type: replace-cross Abstract: Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANN

Tutorials

Esoteric Language Models: A Family of Any-Order Diffusion LLMs

arXiv:2506.01928v4 Announce Type: replace Abstract: Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation. Withi

tutorialsarxiv-cs-cl

EUDAIMONIA: Evaluating Undesirable Dynamics in AI

arXiv:2605.30654v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but t

Evaluating Factual Density in Multi-Source RAG: A Study in Medical AI Accuracy

arXiv:2605.31506v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is the current industry standard for grounding AI in real-world facts. Traditional retrieval methods rely on keyw

Evaluating using Mock Tool Calls to Quarantine Untrusted Prompt Inputs

arXiv:2605.30521v1 Announce Type: new Abstract: Large language models must frequently process untrusted inputs, such as judging an answer from another model or running tasks like spam and harm classif

Tools

Every month I publish the now-a-month-old previous newsletter so people can see what they're missing out on. Here's what you would have got …

Every month I publish the now-a-month-old previous newsletter so people can see what they're missing out on. Here's what you would have got a month ago covering what happened in LLMs in April: https:/

toolssimon-willison--x

Evidence for systematic semantic structure in individual phonemes

arXiv:2603.17306v3 Announce Type: replace Abstract: A foundational assumption in linguistics holds that sound-meaning relations are largely arbitrary. Here we show that this assumption fails at the le

EvoDefense: Co-Evolving Black-Box Defense with Large Language Models

arXiv:2605.31140v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain highly vulnerable to diverse attacks, particularly in black-box settings where the internals of target models are

EvoGens: A Population-Based Heuristic Search Framework for Scientific Idea Generation

arXiv:2605.30961v1 Announce Type: new Abstract: Generating novel research ideas is fundamental to scientific progress. While Large Language Models (LLMs) show promise in assisting this process, existi

Evolutionary Algorithm for Reservoir Learning and Yielding

arXiv:2605.30372v1 Announce Type: cross Abstract: Reservoir computing, a type of recurrent neural network, is a promising approach for temporal learning as it separates dynamic processing from the tra

Expand Neurons, Not Parameters

arXiv:2510.04500v2 Announce Type: replace Abstract: This work demonstrates how increasing the number of neurons in a network without increasing its total number of non-zero parameters improves perform

Expert Merging in Sparse Mixture of Experts with Nash Bargaining

arXiv:2510.16138v2 Announce Type: replace Abstract: Existing expert merging strategies for Sparse Mixture of Experts (SMoE) typically rely on input-dependent or input-independent averaging of expert p

ExpGraph: Model-Agnostic Experience Learning with Graph-Structured Memory for LLM Agents

arXiv:2605.30712v1 Announce Type: new Abstract: Large language model (LLM) agents have shown strong capabilities in reasoning, tool use, and multi-step interaction, but they often solve tasks from scr

Exploiting Chordal Sparsity for Globally Optimal Estimation with Factor Graphs

arXiv:2605.30617v1 Announce Type: new Abstract: Robust and efficient state estimation is crucial for perception, navigation, and control in robotics. State estimation problems are conveniently modeled

safetyarxiv-cs-ro