All entries

FormalEvolve: Neuro-Symbolic Evolutionary Search for Diverse Autoformalization

arXiv:2603.19828v3 Announce Type: replace Abstract: Autoformalization aims to produce formal statements that compile and faithfully preserve the intended meaning of informal mathematics. Yet standard

Formalizing Mathematics at Scale

arXiv:2605.29955v1 Announce Type: new Abstract: We present AutoformBot, a multi-agent system for building an Autoformalized Textbook Library At Scale (Atlas) in Lean 4. AutoformBot orchestrates thousa

agentsarxiv-cs-ai

29 May 2026

Former Tesla data labelers say FSD relies on laborious mapping for hazards; crash data analysis shows Tesla exaggerates FSD's safety via flawed methodology (Reuters)

Reuters: Former Tesla data labelers say FSD relies on laborious mapping for hazards; crash data analysis shows Tesla exaggerates FSD's safety via flawed methodology — Tesla says its Full Self-Driving

safetytechmeme

FormInv: A Measurement Protocol for Semantic Invariance in Mathematical Reasoning Benchmarks

arXiv:2605.29001v1 Announce Type: cross Abstract: A paraphrase-quality audit of MathCheck (ICLR 2025) detected 4 semantically incorrect paraphrases in 129 groups (3.1%); removing them drops GPT-4o fro

FPLIER: Federated Pathway-Level Information Extractor

model-releasesarxiv-cs-lg

arXiv:2605.29587v1 Announce Type: cross Abstract: In transcriptomics, gene-set-aware factorization methods such as the Pathway Level Information Extractor (PLIER) are most effective when trained on la

FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes

arXiv:2605.29318v1 Announce Type: cross Abstract: We present a novel formulation for mesh-free, reduced-order simulation of deformable hyperelastic objects. Existing work in reduced-order elastodynami

researcharxiv-cs-cv

From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals

arXiv:2605.29555v1 Announce Type: new Abstract: As candidate generation and high-throughput experimentation advance, the primary bottleneck in materials discovery is shifting from property prediction

agentsarxiv-cs-cl

From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale

arXiv:2605.28826v1 Announce Type: new Abstract: In modern LLMs, linguistic features function not as stylistic artifacts but as probes of probability mass, allocated under training alignment objectives

safetyarxiv-cs-cl

From Data to Insights: Exploring Program-of-Thoughts Prompting for Chart Summarization

arXiv:2605.28874v1 Announce Type: new Abstract: Charts play a critical role in conveying numerical data insights through structured visual representations. However, semantic visual understanding and n

researcharxiv-cs-cl

From General Vision to Reliable Traversability Estimation: Adapting Vision Foundation Models for Unstructured Outdoor Environments

arXiv:2605.29565v1 Announce Type: new Abstract: Vision-based approaches have become the dominant paradigm for traversability estimation in unstructured outdoor environments, typically adapting vision

From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs

arXiv:2605.30014v1 Announce Type: new Abstract: Urban trajectories play a crucial role in modeling urban dynamics and supporting various smart city applications. However, privacy concerns restrict acc

applicationsarxiv-cs-ai

From Meta-Thought to Execution: Cognitively Aligned Post-Training for Generalizable and Reliable LLM Reasoning

arXiv:2601.21909v2 Announce Type: replace Abstract: Current LLM post-training methods optimize complete reasoning trajectories through Supervised Fine-Tuning (SFT) followed by outcome-based Reinforcem

Tutorials

From petabytes to predictions: Easy BigQuery insights in Google Sheets

Many organizations’ single source of truth is data that resides in BigQuery, Google’s governed, secure and petabyte-scale data platform. However, the 'last mile' of ad-hoc analysis, modeling, and repo

tutorialsgoogle-cloud-ai

From Prompts to Context: An Ontology-Driven Framework for Human-Generative AI Collaboration

arXiv:2605.29675v1 Announce Type: cross Abstract: Collaborations with Generative AI often begin with a short prompt and end with an opaque output, leaving implicit who was involved, what task was bein

agentsarxiv-cs-ai

From Rubrics to Reliable Scores: Evidence-Grounded Text Evaluation with LLM Judges

arXiv:2601.08654v2 Announce Type: replace-cross Abstract: Rubric-based text evaluation increasingly uses large language models (LLMs) as scalable judges, but aligning frozen black-box models with huma

Local Ai

From Short Histories to Long Futures: Horizon-Aware Graph Neural Networks for Long Horizon Forecasting

arXiv:2605.29952v1 Announce Type: new Abstract: Accurate long-range prediction of geophysical systems is difficult due to strongly nonlinear dynamics, the high computational cost of full-physics simul

local-aiarxiv-cs-lg

From Sublinear to Linear: Local Convergence in Finite-Width Networks via Locally Polyak-Lojasiewicz Regions

model-releasesarxiv-cs-lg

arXiv:2507.21429v3 Announce Type: replace-cross Abstract: We study local linear convergence of gradient descent for finite-width feedforward networks under the squared empirical loss. Prior work shows

From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks

arXiv:2605.29768v1 Announce Type: new Abstract: Existing traffic forecasting benchmarks assume a fixed sensor set, but real road-sensor networks grow continuously as the road network changes year by y

Frontier LLM-based agents can overcome the ontology curation bottleneck for natural phenotypes

arXiv:2605.28965v1 Announce Type: new Abstract: Linking free-text phenotype descriptions to ontology terms, typically referred to as phenotype annotation, is essential for the cross-study integration

FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Views

arXiv:2605.29997v1 Announce Type: new Abstract: We present FRUC, a feed-forward 3D Gaussian splatting framework for dynamic scene reconstruction from uncalibrated collaborative driving views. Existing

agentsarxiv-cs-cv

Industry

Fuck the police.

Fuck the police. @smg1664 Our officers were misled at the scene, including denial of weapon use. They quickly switched to life-saving aid within minutes but, as laid out in our statement, the medical

industryelon-musk--x

fully agree!

fully agree! Artificial intelligences do not undergo experiences, do not possess a body, do not feel joy or pain, do not mature through relationships, and do not know from within what love, work, frie

safetygary-marcus--x

fun fact many people don’t know:

applicationsgary-marcus--x

fun fact many people don’t know: LLMs actually produce bullshit for many reasons, and would continue to do so (though less) even they weren’t trained on bullshit. legal citation hallucinations are a n

Tools

Function invocations now billed per unit

Vercel updated its billing model to charge for serverless function invocations on a per-unit basis rather than previous pricing structures. This change affects how customers are charged for executing

toolsvercel-blog

Future Forcing: Future-aware Training-free KV Cache Policy for Autoregressive Video Generation

arXiv:2605.30083v1 Announce Type: new Abstract: Autoregressive (AR) video generation has emerged as a promising paradigm for long-horizon video synthesis, where each frame is generated conditioned on

Gaga: Group Any Gaussians via 3D-aware Memory Bank

arXiv:2404.07977v4 Announce Type: replace Abstract: We introduce Gaga, a framework that reconstructs and segments open-world 3D scenes by leveraging inconsistent 2D masks predicted by zero-shot class-

GAP3D: Generative Alignment of VLM Latents to Patch-Level Embeddings for 3D Generation

arXiv:2605.28995v1 Announce Type: new Abstract: Recent approaches integrating vision-language models (VLMs) as prompt encoders for generative model conditioning typically rely on expensive end-to-end

GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering

arXiv:2605.29584v1 Announce Type: new Abstract: Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe

safetyarxiv-cs-cl

GASS: Geometry-Aware Spherical Sampling for Disentangled Diversity Enhancement in Text-to-Image Generation

arXiv:2602.17200v2 Announce Type: replace Abstract: Despite high semantic alignment, modern text-to-image (T2I) generative models still struggle to synthesize diverse images from a given prompt. In th

Tutorials

Gated Graph Attention Networks with Learnable Temperature

arXiv:2605.29803v1 Announce Type: new Abstract: Graph attention networks learn neighbor importance through data-dependent coefficients, but standard layers lack explicit control over unreliable featur

tutorialsarxiv-cs-lg

Gaze2Act: Gaze-Conditioned Vision-Language-Action Policies for Interactive Robot Manipulation

arXiv:2605.30282v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have recently shown strong potential for robot learning by following language instructions. However, in practice, la

safetyarxiv-cs-ro

GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models

arXiv:2605.29398v1 Announce Type: cross Abstract: Reinforcement learning (RL) can be used to improve the policy (denoiser) of diffusion large language models (dLLMs), while being hindered by the intra

safetyarxiv-cs-ai

GenClaw: Code-Driven Agentic Image Generation

arXiv:2605.30248v1 Announce Type: new Abstract: Image generation models have evolved from text-conditioned pixel synthesis toward multimodal agents endowed with visual comprehension and tool invocatio

agentsarxiv-cs-cv

GenEraser: Generalizable Video Object Removal via Balanced Text-Mask Guidance and Decoupled Locator-Preserver

model-releasesarxiv-cs-cv

arXiv:2605.30045v1 Announce Type: new Abstract: Video object removal frequently struggles to simultaneously eliminate target objects and their associated physical effects (e.g., smoke, reflections, li

Generative Spatiotemporal Intent Sequence Recommendation via Implicit Reasoning in Amap

arXiv:2605.28888v1 Announce Type: cross Abstract: Real-world user behavior rarely consists of isolated actions; instead, it often forms intent flows governed by spatiotemporal dependencies. To provide

applicationsarxiv-cs-lg

GenesisFunc: Multi-Agent Data Generation for Accurate and Generalizable Function-Calling

arXiv:2605.28835v1 Announce Type: cross Abstract: Large Language Models (LLMs) extend their capabilities through function-calling (FC), which relies on training data with high quality, diversity, and

agentsarxiv-cs-ai

Genetically Aligned Patient Representations Improve Hematological Diagnosis

arXiv:2605.29980v1 Announce Type: cross Abstract: Multimodal alignment of histopathology encoders with transcriptomic and genomic data has been shown to significantly improve performance in downstream

safetyarxiv-cs-ai

GEO-Bench: Benchmarking Ranking Manipulation in Generative Engine Optimization

arXiv:2605.29107v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly rank products, documents, and recommendations for user queries, which makes manipulating these rankings a gr

GeoMag: Geometric-Aware Video Motion Magnification via State Space Model

arXiv:2605.29762v1 Announce Type: new Abstract: Video Motion Magnification (VMM) reveals imperceptible dynamics but often suffers from structural inconsistencies under complex geometric transformation

Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning

arXiv:2605.29661v1 Announce Type: new Abstract: Monocular 3D shape recovery is fundamental to geometric understanding, yet achieving robust generalization across arbitrary viewpoints and unseen object

Tutorials

Geometry Matters: 3D Foundation Priors for Learning Semantic Correspondence

arXiv:2605.30093v1 Announce Type: new Abstract: Foundation features from self-supervised vision models and text-to-image diffusion models have proven effective for semantic correspondence estimation.

tutorialsarxiv-cs-cv

GeRaF: Neural Geometry Reconstruction from Radio Frequency Signals

arXiv:2605.29097v1 Announce Type: new Abstract: GeRaF is the first method to use neural implicit learning for near-range 3D geometry reconstruction from radio frequency (RF) signals. Unlike RGB or LiD

Gesture-Aware Indoor THz ISAC Systems for Adaptive Resource Allocation

arXiv:2605.29913v1 Announce Type: cross Abstract: This paper investigates a multi-user indoor integrated sensing and communication (ISAC) system operating in the terahertz (THz) band, designed for ada

researcharxiv-cs-lg

Getting to the Point: Pointing Improves LVLMs at Counting

arXiv:2603.21746v2 Announce Type: replace Abstract: Pointing-based methods decompose complex tasks as sequential grounding and reasoning steps. Given a query, the model first grounds the relevant obje

researcharxiv-cs-cv

GiPL: Generative augmented iterative Pseudo-Labeling for Cross-Domain Few-Shot Object Detection

arXiv:2605.29539v1 Announce Type: cross Abstract: Vision-language foundation models have shown promising zero-shot generalization for Cross-Domain Few-Shot Object Detection (CD-FSOD). However, they fa

Give it Space! Explicit Disentangling of Positional and Semantic Representations in Encoders

arXiv:2605.30022v1 Announce Type: cross Abstract: Positional encoding (PE) underpins how permutation-invariant Transformers represent sequence order, yet how positional information is processed and st

GMOS: Grounding Moving Object Segmentation in 3D Space and Time

arXiv:2605.30352v1 Announce Type: new Abstract: Moving Object Segmentation (MOS) aims to discover, segment, and track objects that move independently of the camera. Current MOS methods, however, exhib

Industry

God bless Rupert Lowe, champion of our people. He will defend our children and save the nation.

God bless Rupert Lowe, champion of our people. He will defend our children and save the nation. I want to be really clear in my response. I am not going to retract anything, I stand by every single wo

industryelon-musk--x