Search: “models”

An LLM-Based System for Argument Mining

model-releasesarxiv-cs-cl

arXiv:2605.13793v2 Announce Type: replace Abstract: Arguments are a fundamental aspect of human reasoning, in which claims are supported, challenged, and weighed against one another. We present an end

AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

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DGX agent

arXiv:2605.20025v1 Announce Type: new Abstract: Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple per

model-releasesarxiv-cs-ai

20 May 2026

Beyond Binary Success: A Diagnostic Meta-Evaluation Framework for Fine-Grained Manipulation

arXiv:2605.19986v1 Announce Type: cross Abstract: Fine-grained manipulation marks a regime where global scene context no longer suffices, and success hinges on the tight coupling of local attribute gr

Beyond Imitation: Learning Safe End-to-End Autonomous Driving from Hard Negatives

arXiv:2605.19771v1 Announce Type: cross Abstract: Existing imitation learning methods for end-to-end autonomous driving predominantly learn from successful demonstrations by minimizing geometric devia

Beyond Waypoints: Dual-Heatmap Grounding for Cross-Embodiment Semantic Navigation

model-releasesarxiv-cs-ro

arXiv:2605.19420v1 Announce Type: new Abstract: Grounding open-ended semantic instructions into physically executable local goals is a fundamental challenge in human-robot interaction. While existing

BLINKG: A Benchmark for LLM-Integrated Knowledge Graph Generation

arXiv:2605.19518v1 Announce Type: new Abstract: Generating Knowledge Graphs (KGs) remains one of the most time-consuming and labor-intensive tasks for knowledge engineers, as they need to identify sem

Can LLMs Emulate Human Belief Dynamics?

arXiv:2605.18781v1 Announce Type: cross Abstract: Can LLMs simulate how humans form and change beliefs in social networks? We put this to the test by replicating an established study on belief dynamic

Safety

CEPO: RLVR Self-Distillation using Contrastive Evidence Policy Optimization

arXiv:2605.19436v1 Announce Type: cross Abstract: When a model produces a correct solution under reinforcement learning with verifiable rewards (RLVR), every token receives the same reward signal rega

safetyarxiv-cs-cl

ClusterRAG: Cluster-Based Collaborative Filtering for Personalized Retrieval-Augmented Generation

arXiv:2605.18769v1 Announce Type: cross Abstract: Personalized Retrieval-Augmented Generation (RAG) relies on accurately selecting user-relevant documents. In practice, existing RAG approaches often s

Agents

CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control

arXiv:2602.10933v2 Announce Type: replace Abstract: Continuous-time generative models have achieved remarkable success in image restoration and synthesis. However, controlling the composition of multi

agentsarxiv-cs-lg

Safety

CopT: Contrastive On-Policy Thinking with Continuous Spaces for General and Agentic Reasoning

arXiv:2605.20075v1 Announce Type: cross Abstract: Chain-of-thought (CoT) is a standard approach for eliciting reasoning capabilities from large language models (LLMs). However, the common CoT paradigm

safetyarxiv-cs-ai

Tutorials

Critique-Guided Distillation for Robust Reasoning via Refinement

arXiv:2505.11628v4 Announce Type: replace Abstract: Supervised fine-tuning with expert demonstrations often produces models that imitate outputs without internalizing the reasoning processes needed fo

tutorialsarxiv-cs-cl

Cross-Subject Intracranial EEG Reconstruction from Scalp Recordings Using Multi-Scale Cross-Attention Transformers

arXiv:2605.18897v1 Announce Type: cross Abstract: Intracranial EEG (iEEG) provides high-fidelity neural recordings essential for clinical and brain-computer interface applications, but acquiring these

researcharxiv-cs-ai

Cross-View Splatter: Feed-Forward View Synthesis with Georeferenced Images

arXiv:2605.19656v1 Announce Type: new Abstract: We present Cross-View Splatter, a feed-forward method that predicts pixel-aligned Gaussian splats for outdoor scenes captured at ground level AND by sat

CutVerse: A Compositional GUI Agents Benchmark for Media Post-Production Editing

arXiv:2605.19484v1 Announce Type: cross Abstract: While GUI agents have made significant progress in web navigation and basic operating system tasks, their capabilities in professional creative workfl

D^3-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market

model-releasesarxiv-cs-lg

arXiv:2605.20036v1 Announce Type: new Abstract: Ride-hailing platforms like DiDi Chuxing operate in highly dynamic environments where balancing driver supply and passenger demand is critical. Although

deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection

arXiv:2605.19605v1 Announce Type: new Abstract: Forests worldwide are increasingly threatened by climate change and disturbances such as fire, pests, and pathogens, creating an urgent need for scalabl

Tutorials

Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution

arXiv:2605.19228v1 Announce Type: cross Abstract: Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing

tutorialsarxiv-cs-ai

Does Code Cleanliness Affect Coding Agents? A Controlled Minimal-Pair Study

arXiv:2605.20049v1 Announce Type: cross Abstract: As autonomous coding agents see rapid adoption, their evaluation has primarily focused on task completion rates holding the target codebase fixed. Thi

Local Ai

Efficient Conditioning Why Pseudo Observation Batch Bayesian Optimization Works When It Does not

arXiv:2605.18819v1 Announce Type: new Abstract: Constant Liar (CL), Kriging Believer (KB), and fantasy models are widely used for batch selection in parallel Bayesian Optimization, yet a unified theor

local-aiarxiv-cs-lg

Efficient Pre-Training with Token Superposition

arXiv:2605.06546v2 Announce Type: replace Abstract: Pre-training of Large Language Models is often prohibitively expensive and inefficient at scale, requiring complex and invasive modifications in ord

researcharxiv-cs-cl

EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection

arXiv:2605.19630v1 Announce Type: new Abstract: With every advancement in generative AI models, forensics is under increasing pressure. The constant emergence of new generation techniques makes it imp

researcharxiv-cs-ai

EnsemHalDet: Robust VLM Hallucination Detection via Ensemble of Internal State Detectors

arXiv:2604.02784v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) excel at multimodal tasks, but they remain vulnerable to hallucinations that are factually incorrect or unground

researcharxiv-cs-cl

EUPHORIA: Efficient Universal Planning via Hybrid Optimization for Robust Industrial Robotic Assembly

arXiv:2605.18872v1 Announce Type: cross Abstract: Robotic assembly in architectural construction faces a persistent bottleneck: existing planners are either highly specialized, requiring prohibitive r

Evaluating the Utility of Personal Health Records in Personalized Health AI

arXiv:2605.18937v1 Announce Type: new Abstract: Patient-managed Personal Health Records (PHRs) promises to empower patients to better understand their health; but information in the record is complex,

Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support

arXiv:2605.19826v1 Announce Type: new Abstract: Operators of safety-critical industrial processes increasingly rely on digital twins to screen control interventions, but such simulators rarely carry c

Fine-tuned a distilbert with ml-intern today for the first time. The procedure is really straightforward, almost unexpectedly so. - Found a …

model-releasesclem-delangue--x

Fine-tuned a distilbert with ml-intern today for the first time. The procedure is really straightforward, almost unexpectedly so. - Found a few datasets relevant to the task (prompt injection detectio

Fine-Tuning Without Forgetting via Loss-Adaptive Learning Rates

arXiv:2605.20005v1 Announce Type: new Abstract: Fine-tuning large language models on new data improves task performance but degrades capabilities learned during pretraining, a phenomenon known as cata

researcharxiv-cs-lg

First-Passage Prediction of Grokking Delay: ACalibrated Law under AdamW with Causal Validation

arXiv:2605.18845v1 Announce Type: cross Abstract: We give the first quantitative prediction of grokking delay under AdamW. Treating the delay as a first-passage time, we derive a closed-form law T_gro

FLUIDSPLAT: Reconstructing Physical Fields from Sparse Sensors via Gaussian Primitives

arXiv:2605.18866v1 Announce Type: cross Abstract: Reconstructing continuous flow fields from sparse surface-mounted sensors is central to aerodynamic design, flow control, and digital-twin instrumenta

FLUXtrapolation: A benchmark on extrapolating ecosystem fluxes

arXiv:2605.19812v1 Announce Type: cross Abstract: We introduce FLUXtrapolation, a benchmark for extrapolating ecosystem fluxes under progressively harder distribution shifts. Ecosystem fluxes are cent

Forward launches Predict to verify network changes before they reach production

model-releasessiliconangle

Network verification company Forward Inc. today launched Forward Predict, a new capability that lets network teams test proposed changes against a digital twin of their production network before deplo

From Llama to Cria: Scaling Down Neural Networks via Neuron-Level Spectral Structural Importance Evaluation

arXiv:2605.18860v1 Announce Type: cross Abstract: This paper proposes a neuron pruning framework based on neuron-level spectral structural importance evaluation. Given a trained neural network, we rec

From SGD to Muon: Adaptive Optimization via Schatten-p Norms

arXiv:2605.19781v1 Announce Type: new Abstract: Modern optimizers, like Muon, impose matrix-wise geometry constraints on their updates. These matrix-wise constraints can be unified under Linear Minimi

GoLongRL: Capability-Oriented Long Context Reinforcement Learning with Multitask Alignment

model-releasesarxiv-cs-cl

arXiv:2605.19577v1 Announce Type: new Abstract: We present GoLongRL, a fully open-source, capability-oriented post-training recipe for long-context reinforcement learning with verifiable rewards (RLVR

Google I/O, Gemini Spark, Antigravity

model-releasessimon-willison

It's hard to find much to write about Google I/O this year because I have a policy of not writing about anything that I can't try out myself, and a lot of the big announcements are 'coming soon'. I ac

Local Ai

GRASP: Deterministic argument ranking in interaction graphs

arXiv:2605.19141v1 Announce Type: cross Abstract: Large language models are increasingly deployed as automated judges to evaluate the strength of arguments. As this role expands, their legitimacy depe

local-aiarxiv-cs-ai

Local Ai

GRLoc: Geometric Representation Regression for Visual Localization

arXiv:2511.13864v2 Announce Type: replace Abstract: Absolute Pose Regression (APR) has emerged as a compelling paradigm for visual localization. However, APR models typically operate as black boxes, d

local-aiarxiv-cs-cv

How Ramp engineers accelerate code review with Codex

Ramp engineers use OpenAI's Codex to streamline and accelerate their code review process, leveraging AI-assisted code understanding and analysis. The implementation likely demonstrates how Codex helps

model-releasesopenai

Industry

I’m not a doomer an AI at all. I think the nature of work, particularly entry level jobs will change. Ai will make business more complicated…

I’m not a doomer an AI at all. I think the nature of work, particularly entry level jobs will change. Ai will make business more complicated and competitive. Not less. Which means more layers where hu

industryclem-delangue--x

Tutorials

INAR-VL: Input-Aware Routing for Edge-Cloud Vision-Language Inference

arXiv:2605.18853v1 Announce Type: cross Abstract: Edge deployment of Vision-Language Models (VLMs) faces a tradeoff between latency and accuracy: cloud execution provides high-quality predictions but

tutorialsarxiv-cs-cv

Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters

arXiv:2605.19523v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable proficiency in general multi-modal understanding; yet they struggle to efficiently acquire

researcharxiv-cs-ai

KappaPlace: Learning Hyperspherical Uncertainty for Visual Place Recognition via Prototype-Anchored Supervision

arXiv:2605.19435v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) is critical for autonomous navigation, yet state-of-the-art methods lack well-calibrated uncertainty estimation. Standa

Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions

model-releasesarxiv-cs-lg

arXiv:2605.19562v1 Announce Type: cross Abstract: This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV)

Less is More: Efficient Black-box Attribution via Minimal Interpretable Subset Selection

arXiv:2504.00470v2 Announce Type: replace-cross Abstract: To develop a trustworthy AI system, which aim to identify the input regions that most influence the models decisions. The primary task of exis

researcharxiv-cs-cv

LLM Benchmark Datasets Should Be Contamination-Resistant

arXiv:2605.19999v1 Announce Type: cross Abstract: Benchmark datasets are critical for reproducible, reliable, and discriminative evaluation of LLMs. However, recent studies reveal that many benchmark

Matern Noise for Triangulation-Agnostic Flow Matching on Meshes

arXiv:2605.19305v1 Announce Type: cross Abstract: This paper tackles the task of learning to generate signals over triangle meshes in a triangulation-agnostic manner, meaning the trained model can be

researcharxiv-cs-cv

Applications

Multi-Token Residual Prediction

arXiv:2605.18817v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) generate text by iteratively denoising masked token sequences, offering a tradeoff between parallelism and quality comp

applicationsarxiv-cs-lg