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OpenCompass: A Universal Evaluation Platform for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.19276v1 Announce Type: new Abstract: In recent years, the field of artificial intelligence has undergone a paradigm shift from task-specific small-scale models to general-purpose large lang

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Research

OpenComputer: Verifiable Software Worlds for Computer-Use Agents

DGX agent

arXiv:2605.19769v1 Announce Type: new Abstract: We present OpenComputer, a verifier-grounded framework for constructing verifiable software worlds for computer-use agents. OpenComputer integrates four

researcharxiv-cs-ai

20 May 2026

Operationalising Artificial Intelligence Bills of Materials (AIBOMs) for Verifiable AI Provenance and Lifecycle Assurance

arXiv:2605.19755v1 Announce Type: cross Abstract: Artificial Intelligence (AI) systems are increasingly dependent on complex, multi-layered software supply chains that introduce challenges for reprodu

agentsarxiv-cs-ai

Operationalizing Document AI: A Microservice Architecture for OCR and LLM Pipelines in Production

arXiv:2605.18818v1 Announce Type: new Abstract: Academic research tends to focus on new models for document understanding creating a wide gap in the literature between model definition and running mod

Optimal Reconstruction from Linear Queries

model-releasesarxiv-cs-lg

arXiv:2605.19625v1 Announce Type: new Abstract: We study the problem of reconstructing an unknown point in R^d from approximate linear queries. This setting arises naturally in applications ranging fr

Optimal Representation Size: High-Dimensional Analysis of Pretraining and Linear Probing

arXiv:2605.20105v1 Announce Type: new Abstract: Learning to generalise from limited data is a fundamental challenge for both artificial and biological systems. A common strategy is to extract reusable

safetyarxiv-cs-lg

optimize_anything: A Universal API for Optimizing any Text Parameter

arXiv:2605.19633v1 Announce Type: cross Abstract: Can a single LLM-based optimization system match specialized tools across fundamentally different domains? We show that when optimization problems are

Optimizing Computational-Statistical Runtime for Wasserstein Distance Estimation

arXiv:2605.20122v1 Announce Type: cross Abstract: Squared Wasserstein distance is a frequently used tool to measure discrepancy between probability distributions. This distance is typically computed b

researcharxiv-cs-lg

Hardware

OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond

arXiv:2605.19660v1 Announce Type: cross Abstract: The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a dominant

hardwarearxiv-cs-cl

P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation

arXiv:2605.19634v1 Announce Type: cross Abstract: Vision-and-language navigation (VLN) requires an embodied agent to ground natural-language instructions into executable navigation actions in unseen e

PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models

arXiv:2605.19580v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging,

safetyarxiv-cs-ro

PASC: Pipeline-Aware Conformal Prediction with Joint Coverage Guarantees for Multi-Stage NLP and LLM Pipelines

arXiv:2605.18812v1 Announce Type: cross Abstract: Modern NLP and LLM systems are pipelines: named entity recognition (NER) -> entity disambiguation (NED) -> entity typing, retrieval-augmented generati

agentsarxiv-cs-cl

Passive Construction Site Safety Monitoring via Persona-Scaffolded Adversarial Chain-of-Thought VLM Verification

arXiv:2605.19869v1 Announce Type: cross Abstract: Construction remains the deadliest industry sector in the United States, with 1,055 fatal worker injuries recorded in 2023, and the majority preventab

PAVE: A Cognitive Architecture for Legitimate Violation in Generative Agent Societies

arXiv:2605.19351v1 Announce Type: cross Abstract: Generative agents based on large language models reproduce believable human behavior in cooperative settings, but how they should reason in situations

agentsarxiv-cs-ai

PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents

arXiv:2605.19932v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across in

PEPL: Precision-Enhanced Pseudo-Labeling for Fine-Grained Image Classification in Semi-Supervised Learning

arXiv:2409.03192v2 Announce Type: replace Abstract: Fine-grained image classification has witnessed significant advancements with the advent of deep learning and computer vision technologies. However,

Local Ai

Perceptual misalignment of texture representations in convolutional neural networks

arXiv:2604.01341v2 Announce Type: replace Abstract: Mathematical modeling of visual textures traces back to Julesz's intuition that texture perception in humans is based on local correlations between

local-aiarxiv-cs-cv

Applications

Performance Monitoring of Proton Exchange Membrane Water Electrolyzer by Transformers-Based Machine Learning Model

arXiv:2605.19107v1 Announce Type: new Abstract: Green hydrogen plays an essential role in decarbonization, with capacity projected to scale to 560 GW by 2030 (vs. 1.39 GW in 2023) in net-zero settings

applicationsarxiv-cs-lg

Personalized Face Privacy Protection From a Single Image

arXiv:2605.19032v1 Announce Type: new Abstract: Photos of faces uploaded online are vulnerable to malicious actors who can scrape facial images from online sources and intrude on personal privacy via

researcharxiv-cs-cv

Phase-Aware Mixture of Experts for Agentic Reinforcement Learning

arXiv:2602.17038v3 Announce Type: replace Abstract: Reinforcement learning (RL) has equipped LLM agents with a strong ability to solve complex tasks. However, existing RL methods normally use a single

Physics-in-the-Loop: A Hybrid Agentic Architecture for Validated CAD Engineering Design

arXiv:2605.19717v1 Announce Type: new Abstract: Large Language Models (LLMs) can generate Computer-Aided Design (CAD), yet lack physical comprehension required for reliable engineering design. Instead

Physics-Informed Graph Neural Network Surrogates for Turbulent Nanoparticle Dispersion in Dental Clinical Environments

model-releasesarxiv-cs-lg

arXiv:2605.19589v1 Announce Type: new Abstract: Dental aerosol procedures produce sub-50 micrometre nuclei that can remain airborne for long periods in enclosed clinics, creating pathways for airborne

Physics-informed simulation framework for realistic sonar image generation and statistical validation

arXiv:2605.19712v1 Announce Type: new Abstract: Synthetic sonar datasets offer a scalable alternative to costly real-world acquisition, yet their utility remains limited by the absence of rigorous qua

safetyarxiv-cs-cv

PhyWorld: Physics-Faithful World Model for Video Generation

arXiv:2605.19242v1 Announce Type: cross Abstract: World simulators can provide safe and scalable environments for training Physical AI systems before real-world deployment. Large video generation mode

PiG-Avatar: Hierarchical Neural-Field-Guided Gaussian Avatars

arXiv:2605.20185v1 Announce Type: cross Abstract: Existing Gaussian avatar methods typically parameterize geometry on a body-template surface, which entangles the avatar's representation space with th

researcharxiv-cs-cv

Hardware

PiKV: KV Cache Management System for Mixture of Experts

arXiv:2508.06526v3 Announce Type: replace-cross Abstract: As large-scale language models continue to scale up in both size and context length, the memory and communication cost of key-value (KV) cache

hardwarearxiv-cs-ai

PixVerve: Advancing Native UHR Image Generation to 100MP with a Large-Scale High-Quality Dataset

arXiv:2605.20147v1 Announce Type: new Abstract: Text-to-Image (T2I) models have recently seen notable progress around 1K and 2K resolution. With the extreme desire for better visual experience and the

Planner-Admissible Graph-PDE Value Extensions for Sparse Goal-Conditioned Planning

arXiv:2605.19185v1 Announce Type: cross Abstract: Sparse goal-conditioned planning with few cost-to-go labels can be viewed as a graph-PDE Dirichlet extension problem: extend sparse labels on a goal-d

researcharxiv-cs-ai

PlantTraitNet: An Uncertainty-Aware Multimodal Framework for Global-Scale Plant Trait Inference from Citizen Science Data

arXiv:2511.06943v3 Announce Type: replace-cross Abstract: Global plant maps of plant traits, such as leaf nitrogen or plant height, are essential for understanding ecosystem processes, including the c

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks

arXiv:2605.19145v1 Announce Type: new Abstract: In the literature, many continual learning (CL) algorithms have been proposed to address the issue of catastrophic forgetting in ML models (i.e., learni

researcharxiv-cs-lg

POLAR-Bench: A Diagnostic Benchmark for Privacy-Utility Trade-offs in LLM Agents

arXiv:2605.19127v1 Announce Type: new Abstract: LLM agents increasingly have access to private user data and act on the user's behalf when interacting with third-party systems. The user defines what m

Applications

Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence

arXiv:2605.18893v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their scalability is increasingly strained by the size of r

applicationsarxiv-cs-lg

Position: Let's Develop Data Probes to Fundamentally Understand How Data Affects LLM Performance

arXiv:2605.18801v1 Announce Type: new Abstract: Data is fundamental to large language models (LLMs). However, understanding of what makes certain data useful for different stages of an LLM workflow, i

Position: The Turing-Completeness of Real-World Autoregressive Transformers Relies Heavily on Context Management

arXiv:2605.19514v1 Announce Type: new Abstract: Many works make the eye-catching claim that Transformers are Turing-complete. However, the literature often conflates two distinct settings: (i) a fixed

Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering

arXiv:2605.19220v1 Announce Type: cross Abstract: Uncertainty Quantification (UQ) is widely regarded as the primary safeguard for deploying Large Language Models (LLMs) in high-stakes domains. However

Local Ai

Posterior Contraction of Levy Adaptive B-spline Regression in Besov Spaces

arXiv:2605.19610v1 Announce Type: cross Abstract: We investigate the asymptotic properties of the Levy Adaptive B-spline (LABS) regression model, a Bayesian nonparametric method that incorporates B-sp

local-aiarxiv-cs-lg

PrAda: Few-Shot Visual Adaptation for Text-Prompted Segmentation

arXiv:2605.19623v1 Announce Type: new Abstract: Segmenting images is critical for visual understanding but demands extensive pixel-level annotations. Foundational models have enabled new paradigms for

PragLocker: Protecting Agent Intellectual Property in Untrusted Deployments via Non-Portable Prompts

arXiv:2605.05974v2 Announce Type: replace-cross Abstract: LLM agents rely on prompts to implement task-specific capabilities based on foundation LLMs, making agent prompts valuable intellectual proper

agentsarxiv-cs-ai

Precision Physical Activity Prescription via Reinforcement Learning for Functional Actions

arXiv:2605.19208v1 Announce Type: cross Abstract: Physical activity (PA) plays an important role in maintaining and improving health. Daily steps have been a key PA measure that is easily accessible w

safetyarxiv-cs-lg

Precision Tracked Transformer via Kalman Filtering, Kriging and Process Noise

arXiv:2605.18832v1 Announce Type: cross Abstract: The Transformer is the foundational building block of modern AI, yet offers no principled handling of uncertainty, which is prevalent in real applicat

Prediction Is Not Physics: Learning and Evaluating Conserved Quantities in Neural Simulators

arXiv:2605.18883v1 Announce Type: cross Abstract: A diffusion model trained on Hamiltonian trajectories can achieve rollout MSE near 10^{-3}, but the standard deviation of its energy over time is betw

Preferences Order, Ratings Anchor: From Fused Expert Aesthetic Ground Truth to Self-Distillation

arXiv:2605.19776v1 Announce Type: new Abstract: Pairwise preferences and pointwise ratings are the two dominant annotation protocols in image aesthetic assessment (IAA), yet existing benchmarks adopt

Hardware

Prior Knowledge or Search? A Study of LLM Agents in Hardware-Aware Code Optimization

arXiv:2605.19782v1 Announce Type: new Abstract: LLM discovery and optimization systems are increasingly applied across domains, implementing a common propose-evaluate-revise loop. Such optimization or

hardwarearxiv-cs-ai

PRISM: A Benchmark for Programmatic Spatial-Temporal Reasoning

arXiv:2605.19382v1 Announce Type: new Abstract: Programmatic video generation through code offers geometric precision and temporal coherence beyond pixel-level diffusion models, yet rigorously evaluat

Local Ai

PRISM-SLAM: Probabilistic Ray-Grounded Inference for Scale-aware Metric SLAM

arXiv:2605.19257v1 Announce Type: new Abstract: Monocular SLAM historically suffers from scale ambiguity and tracking failure in dynamic environments. While recent vision foundation models (VFMs) prov

local-aiarxiv-cs-ro

Probabilistic Multivariate Time Series Forecasting with Diffusion Copulas

arXiv:2605.19685v1 Announce Type: cross Abstract: Accurately assessing financial risk requires capturing both individual asset volatility and the complex, asymmetric dependence structures that emerge

safetyarxiv-cs-lg

Probabilistic Recursively Feasible Motion Planning Under Uncertain Environments

arXiv:2605.19015v1 Announce Type: cross Abstract: Safe motion planning in uncertain, time-varying environments is challenging because the safe region can change unpredictably across planning steps, of

safetyarxiv-cs-ro

Probabilistic Tiny Recursive Model

arXiv:2605.19943v1 Announce Type: new Abstract: Tiny Recursive Models (TRM) solve complex reasoning tasks with a fraction of the parameters of modern large language models (LLMs) by iteratively refini

researcharxiv-cs-ai