All entries

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

Content type

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

DGX agent

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

20 May 2026

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

patches allow for trust boundaries > patches separate 'system wants to change state' from 'change is now accepted policy determines what hap…

Patches represent a mechanism for establishing trust boundaries in systems by decoupling the intent to change state from the acceptance and policy-driven implementation of that change. This separation

safetyyohei-nakajima--x

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

✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, an…

model-releasesclem-delangue--x

✨ Personal AI is the next computing platform. AI is shifting from something you access to something you build with, locally, at the edge, and across systems. We’re unlocking new possibilities for deve

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

Personally incredibly exciting at LangChain Labs to lead a focused, applied research effort into Continual Learning. A big prior is in treat…

Personally incredibly exciting at LangChain Labs to lead a focused, applied research effort into Continual Learning. A big prior is in treating the long horizon agent optimization problem as one that

agentsharrison-chase--x

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

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

Probability-Conserving Flow Guidance

arXiv:2605.20079v1 Announce Type: cross Abstract: Diffusion and flow-based generative models dominate visual synthesis, with guidance aligning samples to user input and improving perceptual quality. H

Probing Embodied LLMs: When Higher Observation Fidelity Hurts Problem Solving

arXiv:2605.20072v1 Announce Type: new Abstract: Large Language Models are increasingly proposed as cognitive components for robotic systems, yet their opaque decision processes make it difficult to ex

agentsarxiv-cs-ai

Industry

@profstonge No, I want to enable anyone to go to the Moon or Mars so that we secure the future of consciousness

Elon Musk states his vision to democratize space travel by enabling broader human access to the Moon and Mars, framing this accessibility as necessary to ensure the long-term survival and continuation

industryelon-musk--x

Applications

Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis

arXiv:2605.18878v1 Announce Type: cross Abstract: Hospital readmission within 30 days of discharge is a leading driver of morbidity, mortality, and avoidable healthcare expenditure in congestive heart

applicationsarxiv-cs-cv

Program Evaluation with Remotely Sensed Outcomes

model-releasesarxiv-cs-lg

arXiv:2411.10959v4 Announce Type: replace-cross Abstract: We study causal inference in experiments and quasi-experiments, where the economic outcome is imperfectly measured by a remotely sensed variab

Progressive Autonomy as Preference Learning: A Formalization of Trust Calibration for Agentic Tool Use

arXiv:2605.19151v1 Announce Type: new Abstract: We formalize trust calibration for agentic tool use (deciding when an automated agent's proposed action may execute autonomously versus require human ap

Projecting Latent RL Actions: Towards Generalizable and Scalable Graph Combinatorial Optimization

arXiv:2605.19721v1 Announce Type: new Abstract: Graph combinatorial optimization (GCO) has attracted growing interest, as many NP-hard problems naturally admit graph formulations, yet their combinator