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Hiding in Plain Sight: Finding MAHA on Reddit

arXiv:2605.20435v1 Announce Type: cross Abstract: Make America Healthy Again (MAHA) is a national health movement that encompasses a striking mix of beliefs, from broadly accepted concerns about good

HiRes: Inspectable Precedent Memory for Reaction Condition Recommendation

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Human DGX agent

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DGX agent

arXiv:2605.21420v1 Announce Type: new Abstract: Reaction condition recommendation sits immediately after retrosynthetic disconnection selection, and in practice, chemists require both accurate predict

researcharxiv-cs-lg

21 May 2026

Agents

HITL-D: Human In The Loop Diffusion Assisted Shared Control

arXiv:2605.21460v1 Announce Type: new Abstract: Autonomous manipulation systems have achieved remarkable capabilities, yet the integration of human expertise with diffusion-based policies in shared co

agentsarxiv-cs-ro

Holistic Reliability Propagation: Decoupling Annotation and Prediction for Robust Noisy-Label

arXiv:2605.20725v1 Announce Type: new Abstract: Learning with noisy labels in multimedia classification often combines external annotations and model predictions into a single reliability weight, even

applicationsarxiv-cs-cv

HORST: Composing Optimizer Geometries for Sparse Transformer Training

arXiv:2605.21104v1 Announce Type: new Abstract: Sparsifying transformers remains a fundamental challenge, as standard optimizers fail to simultaneously encourage sparsity and maintain training stabili

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective

arXiv:2502.17773v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate survey responses, but synthetic data can be misaligned with the human populatio

researcharxiv-cs-lg

How Much Online RL is Enough? Informative Rollouts for Offline Preference Optimization in RLVR

model-releasesarxiv-cs-lg

arXiv:2605.21266v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for reasoning in language models, with GRPO as its primary exam

How Open Must Language Models be to Enable Reliable Scientific Inference?

arXiv:2603.26539v2 Announce Type: replace Abstract: How does the extent to which a model is open or closed impact the scientific inferences that can be drawn from research that involves it? In this pa

Agents

How Well Do Vision-Language Models Understand Sequential Driving Scenes? A Sensitivity Study

arXiv:2604.06750v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) are increasingly proposed for autonomous driving tasks, yet their performance on sequential driving scenes remains poo

agentsarxiv-cs-cv

How You Move Tells What You'll Do: Trajectory-Conditioned Egocentric Prediction

arXiv:2605.20388v1 Announce Type: new Abstract: Predicting how a person's first-person view will evolve (what action will follow, what plan completes a task, whether an in-progress shot will score) is

HRM-Text: Efficient Pretraining Beyond Scaling

arXiv:2605.20613v1 Announce Type: new Abstract: The current pretraining paradigm for large language models relies on massive compute and internet-scale raw text, creating a significant barrier to foun

Humanoid Whole-Body Manipulation via Active Spatial Brain and Generalizable Action Cerebellum

model-releasesarxiv-cs-ro

arXiv:2605.21133v1 Announce Type: new Abstract: In this paper, we explore spatial-aware humanoid whole-body manipulation task. Compared with tabletop settings, this task poses two key challenges: 1) S

Hybrid Machine Learning Model for Forest Height Estimation from TanDEM-X and Landsat Data

arXiv:2605.20997v1 Announce Type: new Abstract: Integrating machine learning (ML) with physical models (PM) has emerged as a promising way of retrieving geophysical parameters from remote sensing data

HyDAR-Pano3D: A Hybrid Disentangled Anatomical Recovery Framework for Panoramic-to-3D Reconstruction

arXiv:2605.20827v1 Announce Type: new Abstract: Panoramic radiograph (PR) is fundamentally used in routine dental care, but it inherently provides only a two-dimensional (2D) projection of complex thr

tutorialsarxiv-cs-cv

Hyper-V2X: Hypernetworks for Estimating Epistemic and Aleatoric Uncertainty in Cooperative Bird's-Eye-View Semantic Segmentation

arXiv:2605.21309v1 Announce Type: new Abstract: Cooperative perception enabled by Vehicle-to-Everything (V2X) communication enhances autonomous driving safety by creating a unified environmental repre

Hardware

HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning

arXiv:2605.20460v1 Announce Type: cross Abstract: Recent advances in garment simulation have brought high-quality results closer to real-time performance. Physics-based simulators can produce accurate

hardwarearxiv-cs-cv

'I didn't Make the Micro Decisions': Measuring, Inducing, and Exposing Goal-Level AI Contributions in Collaboration

arXiv:2605.21363v1 Announce Type: new Abstract: As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration b

applicationsarxiv-cs-cl

Improved convergence rate of kNN graph Laplacians: differentiable self-tuned affinity

arXiv:2410.23212v2 Announce Type: replace-cross Abstract: In graph-based data analysis, k-nearest neighbor (kNN) graphs are widely used due to their adaptivity to local data densities. Allowing weight

Improved Guarantees for Constrained Online Convex Optimization via Self-Contraction

arXiv:2605.21107v1 Announce Type: new Abstract: We consider Constrained Online Convex Optimization (COCO) with adversarially chosen constraints. At each round, the learner chooses an action before obs

researcharxiv-cs-lg

Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization

arXiv:2509.13482v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) is rapidly gaining popularity for its photorealistic rendering quality and real-time performance, but it generates mass

Improving Quantized Model Performance in Qualitative Analysis with Multi-Pass Prompt Verification

arXiv:2605.20193v1 Announce Type: new Abstract: Quantized Large Language Models (LLMs) are used more often in qualitative analysis because they run fast and need fewer computing resources. This study

Local Ai

IndusAgent: Reinforcing Open-Vocabulary Industrial Anomaly Detection with Agentic Tools

arXiv:2605.20682v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown remarkable capability in bridging visual perception and textual reasoning, enabling zero-shot unders

local-aiarxiv-cs-cv

Inference Time Policy Optimization for Offline RL with Differentiable World Models

arXiv:2603.22430v2 Announce Type: replace Abstract: Offline Reinforcement Learning (RL) learns optimal policies from fixed datasets, training a policy once and deploying it at inference time without f

Informationally Compressive Anonymization: Non-Degrading Sensitive Input Protection for Privacy-Preserving Supervised Machine Learning

arXiv:2603.15842v2 Announce Type: replace Abstract: Modern machine learning systems increasingly rely on sensitive data, creating significant privacy, security, and regulatory risks that existing priv

applicationsarxiv-cs-lg

Agents

Insights Generator: Systematic Corpus-Level Trace Diagnostics for LLM Agents

arXiv:2605.21347v1 Announce Type: cross Abstract: Diagnosing failures in LLM agents remains largely manual. Practitioners inspect a small subset of execution traces, form ad-hoc hypotheses, and iterat

agentsarxiv-cs-lg

Instance Discrimination for Link Prediction

arXiv:2605.20257v1 Announce Type: new Abstract: Recently, instance discrimination models have emerged as a major solution for self-supervised learning. Having already demonstrated its effectiveness in

researcharxiv-cs-lg

Hardware

Instant GPU Efficiency Visibility at Fleet Scale

arXiv:2605.20799v1 Announce Type: cross Abstract: We present Overall FLOP Utilization (OFU), a hardware-level, precision-agnostic GPU efficiency metric for AI workloads on HPC systems, derived from tw

hardwarearxiv-cs-lg

Local Ai

Intent-First Aerial V2V for Tactical Coordination and Separation: Protocol and Performance Under Density and Disturbance

arXiv:2605.20595v1 Announce Type: new Abstract: Dense low-altitude aerial operations require more than pre-flight route coordination and last-resort collision avoidance. Once aircraft are airborne, di

local-aiarxiv-cs-ro

Local Ai

Interaction Locality in Hierarchical Recursive Reasoning

arXiv:2605.20784v1 Announce Type: cross Abstract: Spatial reasoning requires both location-bound computation and location-invariant structure: agents must make local moves while preserving route, obje

local-aiarxiv-cs-lg

InternBootcamp Technical Report: Boosting LLM Reasoning with Verifiable Task Scaling

arXiv:2508.08636v2 Announce Type: replace Abstract: Large language models (LLMs) have revolutionized artificial intelligence by enabling complex reasoning capabilities. While recent advancements in re

Interpretable Discriminative Text Representations via Agreement and Label Disentanglement

arXiv:2605.20693v1 Announce Type: new Abstract: Interpretable text representations should expose coordinates that are not only predictive, but also meaningful enough for independent auditors to apply.

Introspective X Training: Feedback Conditioning Improves Scaling Across all LLM Training Stages

arXiv:2605.20285v1 Announce Type: new Abstract: We tackle the question of how to scale more efficiently across the many, ever-growing stages of current LLM training pipelines. Our guiding intuition st

tutorialsarxiv-cs-lg

iReasoner: Trajectory-Aware Intrinsic Reasoning Supervision for Self-Evolving Large Multimodal Models

arXiv:2601.05877v3 Announce Type: replace Abstract: Recent work shows that large multimodal models (LMMs) can self-improve from unlabeled data via self-play and intrinsic feedback. Yet existing self-e

Is Fixing Schema Graphs Necessary? Full-Resolution Graph Structure Learning for Relational Deep Learning

arXiv:2605.21475v1 Announce Type: new Abstract: Relational prediction tasks are fundamental in many real-world applications, where data are naturally stored in relational databases (RDBs). Relational

applicationsarxiv-cs-lg

It Takes Two: Complementary Self-Distillation for Contextual Integrity in LLMs

arXiv:2605.20258v1 Announce Type: new Abstract: Contextual Integrity (CI) defines privacy not merely as keeping information hidden, but as governing information flows according to the norms of a given

Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization

arXiv:2603.00086v2 Announce Type: replace Abstract: Automatic speech recognition for French medical conversations remains challenging, with word error rates often exceeding 30% in spontaneous clinical

iTryOn: Mastering Interactive Video Virtual Try-On with Spatial-Semantic Guidance

arXiv:2605.21431v1 Announce Type: new Abstract: Video Virtual Try-On (VVT) aims to seamlessly replace a garment on a person in a video with a new one. While existing methods have made significant stri

tutorialsarxiv-cs-cv

JFAA: Technical Report for the EPIC-KITCHENS-100 Action Anticipation Challenge at EgoVis 2026

arXiv:2605.20904v1 Announce Type: new Abstract: We propose JFAA, a JEPA-based Future Action Anticipation method for the EPIC-KITCHENS-100 (EK-100) Action Anticipation task. Inspired by the representat

JobArabi: An Arabic Corpus and Analysis of Job Announcements from Social Media

arXiv:2605.20960v1 Announce Type: new Abstract: This paper introduces JobArabi, a large-scale corpus of Arabic job announcements collected from social media between January 2024 and October 2025. The

Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition

arXiv:2605.20648v1 Announce Type: new Abstract: Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to ne

tutorialsarxiv-cs-ro

JUDO: A Juxtaposed Domain-Oriented Multimodal Reasoner for Industrial Anomaly QA

arXiv:2605.20284v1 Announce Type: new Abstract: Industrial anomaly detection has been significantly advanced by Large Multimodal Models (LMMs), enabling diverse human instructions beyond detection, pa

LAION-C: An Out-of-Distribution Benchmark for Web-Scale Vision Models

arXiv:2506.16950v2 Announce Type: replace Abstract: Out-of-distribution (OOD) robustness is a desired property of computer vision models. Improving model robustness requires high-quality signals from

LamPO: A Lambda Style Policy Optimization for Reasoning Language Models

arXiv:2605.21235v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become an effective paradigm for improving reasoning language models on tasks such as mathemat

Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction

arXiv:2603.23531v2 Announce Type: replace Abstract: Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce

Large-Step Training Dynamics of a Two-Factor Linear Transformer Model

model-releasesarxiv-cs-lg

arXiv:2605.21292v1 Announce Type: cross Abstract: Gradient-flow analyses show that simplified linear transformers can learn the in-context linear-regression algorithm, but they do not explain the fini

LASH: Adaptive Semantic Hybridization for Black-Box Jailbreaking of Large Language Models

arXiv:2605.21362v1 Announce Type: new Abstract: Jailbreak attacks expose a persistent gap between the intended safety behavior of aligned large language models and their behavior under adversarial pro

safetyarxiv-cs-cl

Latent Dynamics for Full Body Avatar Animation

arXiv:2605.21478v1 Announce Type: new Abstract: Pose-driven full-body avatars built on neural rendering produce high-quality novel views of a captured subject. Yet loose clothing and other dynamic ele

Latent Geometry as a Structural Monitor: Eigenspace Alignment for Anomaly Detection in Anonymity Networks

arXiv:2605.20391v1 Announce Type: cross Abstract: Traditional anomaly detection marks events when measured signals cross predefined thresholds. This captures the moment of transition but not the struc