All entries

Learning Category-level Last-meter Navigation from RGB Demonstrations of a Single-instance

arXiv:2512.11173v4 Announce Type: replace Abstract: Achieving precise positioning of the mobile manipulator's base is essential for successful manipulation actions that follow. Most of the RGB-based n

Learning Dexterous Manipulation Using Contact Wrench Guidance From Human Demonstration

DGX agent

arXiv:2607.00033v1 Announce Type: cross Abstract: Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains ch

model-releasesarxiv-cs-ai

2 Jul 2026

Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression

model-releasesarxiv-cs-lg

arXiv:2607.00257v1 Announce Type: new Abstract: Accurate prediction of complex dynamical systems from noisy measurements remains a significant challenge in scientific computing. Kernel ridge regressio

Learning Expert Strategy for Autonomous Robotic Endovascular Intervention via Decoupled Procedural Execution

arXiv:2607.00066v1 Announce Type: new Abstract: Endovascular interventions are high-stakes procedures requiring precise device operation within complex and tortuous vascular anatomies. Autonomous endo

Learning from Demonstration via Spatiotemporal Tubes for Unknown Euler-Lagrange Systems

arXiv:2607.00534v1 Announce Type: new Abstract: We present STT-LfD, a unified Learning from Demonstration (LfD) framework that integrates motion learning with control for unknown Euler-Lagrange system

Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications

arXiv:2607.00442v1 Announce Type: cross Abstract: Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that limit both interpret

Learning Generalizable Skill Policy with Data-Efficient Unsupervised RL

arXiv:2607.00392v1 Announce Type: cross Abstract: Unsupervised Reinforcement Learning (URL) aims to pre-train scalable, skill-conditioned policies without extrinsic rewards, serving as a foundation fo

Learning Structured Reasoning via Tractable Trajectory Control

researchapple-ml-research

Large language models can exhibit emergent reasoning behaviors, often manifested as recurring lexical patterns (e.g., “wait,” indicating verification). However, complex reasoning trajectories remain s

Learning to Compose: Revisiting Proxy Task Design for Zero-Shot Composed Image Retrieval

arXiv:2607.00374v1 Announce Type: cross Abstract: Composed Image Retrieval (CIR) retrieves a target image from a reference image and a textual modification. While supervised CIR relies on costly tripl

Learning to Watch: Active Video Anomaly Understanding via Interleaved Policy Optimization

arXiv:2607.00622v1 Announce Type: new Abstract: Video anomaly understanding (VAU) relies on sparse, context-dependent cues. However, existing passive paradigms suffer from observational aliasing, wher

local-aiarxiv-cs-cv

Learning Unmasking Policies for Diffusion Language Models

researchapple-ml-research

Diffusion (Large) Language Models (dLLMs) now match the downstream performance of their autoregressive counterparts on many tasks, while holding the promise of being more efficient during inference. O

Learning User-Aware Recall: Personalized Retrieval in Long-Term Conversational Memory

arXiv:2607.00017v1 Announce Type: cross Abstract: Long-term conversational agents are expected to remember past interactions, but memory is useful only when the right evidence is recalled for the righ

Learning When to Listen: Gated Affect Fusion for Human Motion Prediction

arXiv:2607.00296v1 Announce Type: cross Abstract: Human motion forecasting in unconstrained real-world videos remains challenging due to the ambiguity of future behaviors and the presence of noisy mul

applicationsarxiv-cs-ai

LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning

arXiv:2607.00958v1 Announce Type: new Abstract: Time series are central to modern data mining applications, from industrial telemetry and server metrics to finance and physiology, yet time-series self

applicationsarxiv-cs-lg

Letter: chip sector group SEMI, including Micron and Samsung, warns Scott Bessent US policies affecting prices or production capacity would worsen the shortage (Maggie Eastland/Bloomberg)

Maggie Eastland / Bloomberg: Letter: chip sector group SEMI, including Micron and Samsung, warns Scott Bessent US policies affecting prices or production capacity would worsen the shortage — Governmen

applicationstechmeme

Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility

arXiv:2607.00228v1 Announce Type: cross Abstract: Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions

researcharxiv-cs-lg

Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurring

arXiv:2607.00251v1 Announce Type: cross Abstract: While most image deblurring techniques directly restore the spatial image variable, we propose an amplitude and phase decomposition recognizing the im

LeVLJEPA: End-to-End Vision-Language Pretraining Without Negatives

arXiv:2607.00784v1 Announce Type: cross Abstract: Vision-language pretraining remains dominated by contrastive objectives, whereas vision-only self-supervised learning has largely adopted non-contrast

Libra: Training the Environment for Agentic Information Retrieval

arXiv:2607.00016v1 Announce Type: cross Abstract: Information localization within massive repositories is a cornerstone of agentic LLM systems. While synthetic data-driven optimization has proven succ

local-aiarxiv-cs-ai

Linguistic Relative Policy Optimization for Video Anomaly Reasoning

model-releasesarxiv-cs-cv

arXiv:2607.00654v1 Announce Type: new Abstract: Video anomaly detection (VAD) with multimodal large language models has shown strong potential, yet most existing methods still depend on large-scale an

Linkify: Learning from Interface-Augmented Assembly Graphs

model-releasesarxiv-cs-cv

arXiv:2607.01205v1 Announce Type: new Abstract: We present Linkify, a framework for learning from interface-augmented assembly graphs to enable context-aware part retrieval in mechanical assemblies. W

LIST3R: Long-sequence Instance-aware 3D Reconstruction

arXiv:2607.00375v1 Announce Type: new Abstract: We present LIST3R, an instance-aware framework for long-sequence 3D reconstruction inspired by the way humans organize spatial memory around stable and

safetyarxiv-cs-cv

LiteParse runs anywhere, including inside agent runtimes. To prove it, we built an email processing assistant using @flueai (the agentic fra…

model-releasesjerry-liu--x

LiteParse runs anywhere, including inside agent runtimes. To prove it, we built an email processing assistant using @flueai (the agentic framework by @astrodotbuild), @resend webhooks, and @tursodatab

Tools

LIVE at 12p PT/3p ET: AI’s cleanest stories are getting messy. Meta has excess compute. AI-heavy companies are hiring, not shrinking. And op…

LIVE at 12p PT/3p ET: AI’s cleanest stories are getting messy. Meta has excess compute. AI-heavy companies are hiring, not shrinking. And open models keep getting better. @DanielTNiles on what this me

toolstogether-ai--x

llm-coding-agent 0.1a0

model-releasessimon-willison

Release: llm-coding-agent 0.1a0 Another Fable 5 experiment. Now that my LLM library has evolved into more of an agent framework it's time to see what a simple coding agent would look like built on it.

LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data

arXiv:2607.00733v1 Announce Type: cross Abstract: Mechanistic modeling via ordinary differential equations (ODEs) provides interpretable descriptions of complex dynamics and enables inference of under

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of A…

LLM Wikis are being slept on. I argue that creating knowledge bases with LLMs or coding agents is one of the most valuable applications of AI today. It's about being intentional in building and scalin

model-releasesdair-ai--x

LLMs in the Real World: Evaluating 'AI' in Emergency Contexts

arXiv:2607.00019v1 Announce Type: cross Abstract: This paper offers a call to action. We urge our colleagues in the research community to play a greater role in the articulation of our findings to the

applicationsarxiv-cs-ai

LLVM-Bench: Benchmarking and Advancing Large Language Models for LLVM Compiler Issue Resolution

arXiv:2607.00700v1 Announce Type: cross Abstract: LLVM is a widely used compiler infrastructure whose scale and complexity make issue resolution labor-intensive and challenging. Although large languag

Local Motion Matters: A Deconstruct-Recompose Paradigm for Reinforcement Learning Pre-training from Videos

arXiv:2607.00808v1 Announce Type: new Abstract: Pre-training on large-scale videos to improve reinforcement learning efficiency is promising yet remains challenging. Existing methods typically treat t

local-aiarxiv-cs-lg

Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads

arXiv:2607.01002v1 Announce Type: cross Abstract: In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pastin

LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models

arXiv:2607.01086v1 Announce Type: cross Abstract: The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs). Existing video quality ben

Loss Smoothing for Stable Adaptation Under Distribution Shift

arXiv:2607.00634v1 Announce Type: cross Abstract: In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods ty

Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition

arXiv:2607.00090v1 Announce Type: cross Abstract: Urban-scale Visual Place Recognition (VPR) aims to identify the geographic location of a query image by matching it against a geo-tagged database. Whi

Industry

Lots of people are advocating for more American open-source models these days which is amazing but very few people do anything about it! Lat…

Lots of people are advocating for more American open-source models these days which is amazing but very few people do anything about it! Latest example, Alex Karp came out advocating for American open

industryclem-delangue--x

Low Perplexity is Repetition: A One-Dimensional Self-Conditioning Attractor in Continuous Diffusion LMs

arXiv:2607.00588v1 Announce Type: new Abstract: Continuous diffusion language models such as ELF report record-low generative perplexity (Gen-PPL). We find a catch: these models repeat far more than h

researcharxiv-cs-cl

LRAT-Catcher: Importing SAT Solver Certificates into Lean4 by Reflection

arXiv:2607.00815v1 Announce Type: cross Abstract: SAT solvers settle combinatorial problems beyond the reach of interactive theorem provers and produce LRAT certificates for independent verification.

LUMA: Benchmarking Segmentation via a Lightweight Universal Mask Adapter

arXiv:2607.00687v1 Announce Type: cross Abstract: Comparing transformer backbones for image segmentation is confounded: each is paired with a different decoder, recipe, and pretraining, so reported di

LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data

model-releasesarxiv-cs-cl

arXiv:2510.24434v3 Announce Type: replace Abstract: The effectiveness of instruction-tuned Large Language Models (LLMs) is often limited in low-resource linguistic settings due to a lack of high-quali

LV-ROVER: Multi-Stream Tesseract Voting for Maltese Paragraph OCR

model-releasesarxiv-cs-cl

arXiv:2607.00250v1 Announce Type: new Abstract: Maltese has decent text corpora and pretrained language models, but, like many languages outside the handful with large OCR benchmarks, only a single kn

Magic-MM-Embedding: Towards Visual-Token-Efficient Universal Multimodal Embedding with MLLMs

arXiv:2602.05275v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown immense promise in universal multimodal retrieval, which aims to find relevant items of various

researcharxiv-cs-cv

Agents

Making Failure Safe: A Constrained, Verifiable Agent Framework for Open-Web Data Collection

arXiv:2607.00035v1 Announce Type: new Abstract: LLMs and agents can generate web scrapers from natural-language requirements, but direct generation remains unreliable because of dependency errors, bro

agentsarxiv-cs-ai

MalariAI: A Label-Resilient Decoupled Framework for Universal Cell Segmentation and Explainable Stage Classification in Dense Malaria Blood Smears

arXiv:2607.00385v1 Announce Type: cross Abstract: Automated malaria diagnosis from blood smear microscopy is a critical challenge in global health AI; in resource-limited settings, the scarcity of exp

local-aiarxiv-cs-ai

Managed Autonomy at Runtime: Gear-Based Safety and Governance for Single- and Multi-Agent Cyber-Physical Systems

arXiv:2607.00334v1 Announce Type: new Abstract: Autonomous agents, whether LLM-driven software agents or robotic physical agents, face a common class of failure modes when operating without continuous

Manifold-constrained Hamilton-Jacobi Reachability Learning for Decentralized Multi-Agent Motion Planning

arXiv:2511.03591v2 Announce Type: replace Abstract: Safe multi-agent motion planning (MAMP) under task-induced constraints is a critical challenge in robotics. Many real-world scenarios require robots

Mapping the Evaluation Frontier: An Empirical Survey of the Bias-Reliability Tradeoff Across Eleven Evaluator-Agent Conditions

arXiv:2607.00304v1 Announce Type: cross Abstract: The bias-reliability tradeoff conjectures that LLM evaluation systems are constrained in (gamma, H, CV) space, where evaluator coupling (gamma), strat

Tools

Massive THANK YOU to @swyx and the @aiDotEngineer crew for the wonderful birthday surprise yesterday. So super thoughtful. And thanks to a…

Massive *THANK YOU* to @swyx and the @aiDotEngineer crew for the wonderful birthday surprise yesterday. So super thoughtful. And thanks to all the attendees for singing so beautifully :) (My wife kept

toolsswyx--x

Measuring Dead Directions: Decomposing and Classifying Singular Structure off Canonical Alignment

arXiv:2607.00603v1 Announce Type: new Abstract: We give a descent-free, alignment-free measurement of singular structure on trained networks. At a single frozen checkpoint the read recovers the order

safetyarxiv-cs-lg