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Overfitting has a limitation: a model-independent generalization gap bound based on Renyi entropy

arXiv:2506.00182v3 Announce Type: replace-cross Abstract: Will further scaling up of machine learning models continue to bring success? A significant challenge in answering this question lies in under

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Research

Overlap-aware segmentation for topological reconstruction of obscured objects

DGX agent

arXiv:2510.06194v2 Announce Type: replace-cross Abstract: The separation of overlapping objects presents a significant challenge in scientific imaging. While deep learning segmentation-regression algo

researcharxiv-cs-cv

18 May 2026

PACER: Acyclic Causal Discovery from Large-Scale Interventional Data

arXiv:2605.15353v1 Announce Type: cross Abstract: Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensiona

Tools

PaddleOCR 3.5: Running OCR and Document Parsing Tasks with a Transformers Backend

PaddleOCR 3.5 introduces a Transformers-based backend for optical character recognition (OCR) and document parsing tasks, enabling improved performance and integration with the Hugging Face ecosystem.

toolshugging-face

PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Control

arXiv:2605.15963v1 Announce Type: new Abstract: Large vision-language models have significantly advanced GUI agents, enabling executable interaction across web, mobile, and desktop interfaces. Yet the

Painless Activation Steering: An Automated, Lightweight Approach for Post-Training Large Language Models

arXiv:2509.22739v3 Announce Type: replace-cross Abstract: Language models (LMs) are typically post-trained for desired capabilities and behaviors via weight-based or prompt-based steering, but the for

PanoWorld: Geometry-Consistent Panoramic Video World Modeling

arXiv:2605.15391v1 Announce Type: cross Abstract: We present PanoWorld, a panoramic video world model that generates geometry-consistent 360egree video from a single image and a caption. Existing pano

Agents

paper.json: A Coordination Convention for LLM-Agent-Actionable Papers

arXiv:2605.16194v1 Announce Type: cross Abstract: LLM agents routinely serve as first (and sometimes only) readers of academic papers, skimming for sub-claims, extracting reproducibility steps, and ge

agentsarxiv-cs-ai

parallelcbf: A composable safety-filter and auditability framework for tensor-parallel reinforcement learning

arXiv:2605.15509v1 Announce Type: new Abstract: While Isaac Lab provides massive parallel UAV simulation, OmniSafe and safe-control-gym provide constrained-RL benchmarks, and CBFKit provides control-b

PBT-Bench: Benchmarking AI Agents on Property-Based Testing

arXiv:2605.15229v1 Announce Type: cross Abstract: Existing code benchmarks measure whether an agent can produce any test that reproduces a known bug, or whether it can produce a patch that fixes a des

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment

arXiv:2605.15654v1 Announce Type: new Abstract: Real-world autonomous driving, particularly in urban environments with numerous corner cases, requires rigorous testing to ensure product safety and rob

safetyarxiv-cs-ro

PDRNN: Modular Data-driven Pedestrian Dead Reckoning on Loosely Coupled Radio- and Inertial-Signalstreams

arXiv:2605.15252v1 Announce Type: cross Abstract: Modern pedestrian dead reckoning (PDR) systems rely on fusing noisy and biased estimates of position, velocity, and calibrated orientation derived fro

PerfCodeBench: Benchmarking LLMs for System-Level High-Performance Code Optimization

model-releasesarxiv-cs-cl

arXiv:2605.15222v1 Announce Type: cross Abstract: Large language models (LLMs) can often generate functionally correct code, but their ability to produce efficient implementations for performance-crit

Perforated Neural Networks for Keyword Spotting

model-releasesarxiv-cs-lg

arXiv:2605.15647v1 Announce Type: new Abstract: Edge machine learning presents a unique set of constraints not encountered in cloud-scale model deployment: strict memory budgets, limited compute, and

Pessimistic Risk-Aware Policy Learning in Contextual Bandits

arXiv:2605.15620v1 Announce Type: cross Abstract: We study risk-aware offline policy learning, aiming to learn a decision rule from logged data that is optimal under general risk criteria. This proble

Petri Net Induced Heuristic Search for Resource Constrained Scheduling

arXiv:2605.15983v1 Announce Type: new Abstract: We formulate the Resource-Constrained Project Scheduling Problem (RCPSP) as optimal search over the reachability graph of a Timed Transition Petri Net w

phi-Balancing for Mixture-of-Experts Training

arXiv:2605.15403v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models rely on balanced expert utilization to fully realize their scalability. However, existing load-balancing methods are lar

PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models

model-releasesarxiv-cs-cv

arXiv:2512.01843v2 Announce Type: replace Abstract: Driven by the growing capacity and training scale, Text-to-Video (T2V) generation models have recently achieved substantial progress in video qualit

PhysBrain 1.0 Technical Report

arXiv:2605.15298v1 Announce Type: cross Abstract: Vision-language-action models have advanced rapidly, but robot trajectories alone provide limited coverage for learning broad physical understanding.

Please help make Grok Build great! Much appreciated, Elon.

Please help make Grok Build great! Much appreciated, Elon. Phew, Grok Build is really thorough, pretty incredible. Relay feature for PasteLocal is done, pushing to Github now. And if you want to know

industryelon-musk--x

Polymarket and Kalshi continue to operate in India despite a government advisory on April 25, directed at VPN providers, labeling prediction markets 'illegal' (Bloomberg)

Bloomberg: Polymarket and Kalshi continue to operate in India despite a government advisory on April 25, directed at VPN providers, labeling prediction markets “illegal” — Polymarket and Kalshi Inc. h

industrytechmeme

Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves

arXiv:2512.00242v3 Announce Type: replace-cross Abstract: Sheaf Neural Networks equip graph structures with a cellular sheaf: a geometric structure which assigns local vector spaces (stalks) and a lin

safetyarxiv-cs-ai

Pompeii victim ID'd as a likely doctor

Archaeologists at Pompeii Archaeological Park identified one of the victims of the 79 A.D. Mount Vesuvius eruption as a doctor after advanced exams revealed surgical instruments inside a plaster cast.

industryars-technica

Applications

Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI

arXiv:2605.15567v1 Announce Type: new Abstract: This position paper argues for metacognition as a general design principle for creating more accurate, secure, and efficient AI. The metacognitive solut

applicationsarxiv-cs-ai

Position: Early-Stage Quality Assurance in Annotation Pipelines Is More Cost-Effective Than Late-Stage Validation

arXiv:2605.15714v1 Announce Type: cross Abstract: This position paper argues that the machine learning community should prioritize early-stage quality assurance in annotation pipelines over the prevai

Position: Ideas Should be the Center of Machine Learning Research

model-releasesarxiv-cs-lg

arXiv:2605.15253v1 Announce Type: new Abstract: Machine learning research increasingly bifurcates into two disconnected modes: benchmark-driven engineering that prioritizes metrics over understanding,

Position: Zeroth-Order Optimization in Deep Learning Is Underexplored, Not Underpowered

arXiv:2605.15622v1 Announce Type: new Abstract: Zeroth-order (ZO) optimization, learning from finite differences of function evaluations without backpropagation, has recently regained attention in dee

Practical Validity Conditions for Byzantine-Tolerant Federated Learning

arXiv:2605.15887v1 Announce Type: new Abstract: Robust aggregation is the core operation in Byzantine-tolerant federated learning. To ensure the quality of aggregation independently of data distributi

PRB-RUPFormer: A Recursive Unified Probabilistic Transformer for Residual PRB Forecasting

arXiv:2605.15363v1 Announce Type: new Abstract: Accurate forecasting of residual Physical Resource Blocks (PRBs) is critical for proactive network slice provisioning, energy-efficient operation, and s

Preconditioned Regularized Wasserstein Proximal Sampling

arXiv:2509.01685v2 Announce Type: replace-cross Abstract: We consider sampling from a Gibbs distribution by evolving finitely many particles. We propose a preconditioned version of a recently proposed

Applications

Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance

arXiv:2508.08431v3 Announce Type: replace-cross Abstract: Spectral variability significantly impacts the accuracy and convergence of hyperspectral unmixing algorithms. Many methods address complex spe

applicationsarxiv-cs-cv

Pretraining Objective Matters in Extreme Low-Data FGVC: A Backbone-Controlled Study

arXiv:2605.15599v1 Announce Type: cross Abstract: Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance

Agents

PRISM: Prompt Reliability via Iterative Simulation and Monitoring for Enterprise Conversational AI

arXiv:2605.15665v1 Announce Type: new Abstract: Deploying large language model (LLM)-driven conversational agents in enterprise settings requires prompts that are simultaneously correct at launch and

agentsarxiv-cs-ai

Local Ai

PrismQuant: Rate-Distortion-Optimal Vector Quantization for Gaussian-Mixture Sources

arXiv:2605.15507v1 Announce Type: cross Abstract: For a Gaussian source under mean-squared error (MSE), classical transform coding is rate--distortion (RD) optimal: the Karhunen--Loeve transform (KLT)

local-aiarxiv-cs-ai

Privacy Evaluation of Generative Models for Trajectory Generation

arXiv:2605.15246v1 Announce Type: new Abstract: Trajectory data is fundamental to modern urban intelligence, yet its sensitivity raises significant privacy concerns. Generative models such as Generati

Probabilistic Dating of Historical Manuscripts via Evidential Deep Regression on Visual Script Features

model-releasesarxiv-cs-cv

arXiv:2605.06475v1 Announce Type: cross Abstract: We introduce a probabilistic approach for dating historical manuscript pages from visual features alone. Instead of aggregating centuries into classes

Applications

Process-Informed Forecasting of Complex Thermal Dynamics in Pharmaceutical Manufacturing

arXiv:2509.20349v3 Announce Type: replace Abstract: Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models

applicationsarxiv-cs-lg

Process Rewards with Learned Reliability

arXiv:2605.15529v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward score for each step. Down

Procurement records: the FBI seeks a vendor that can provide access to automated license plate readers nationwide; Flock and Motorola are among the few options (Joseph Cox/404 Media)

Joseph Cox / 404 Media: Procurement records: the FBI seeks a vendor that can provide access to automated license plate readers nationwide; Flock and Motorola are among the few options — Only a couple

industrytechmeme

Project Glasswing: what Mythos showed us

In recent weeks, we pointed Mythos and other security-focused LLMs at live code across critical parts of our infrastructure. We share what we observed, the models’ strengths and weaknesses, and what t

industrycloudflare-ai

Prompt Stability Scoring for Text Annotation with Large Language Models

arXiv:2407.02039v3 Announce Type: replace Abstract: Researchers are increasingly using language models (LMs) for text annotation. These approaches rely only on a prompt telling the model to return a g

researcharxiv-cs-cl

Prompting Amazon Nova 2 for content moderation

model-releasesaws-ml-blog

In this post, you learn how to prompt Amazon Nova 2 Lite for content moderation using structured and free-form approaches, grounded in the MLCommons AILuminate Assessment Standard. The prompting techn

Propagating Unsafe Actions in LLM Controlled Multi-Robot Collaboration via Single Robot Compromise

arXiv:2605.15641v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as general planners in embodied intelligence, enabling high level coordination and low level task pla

safetyarxiv-cs-ro

Tutorials

Property-Guided LLM Program Synthesis for Planning

arXiv:2605.16142v1 Announce Type: new Abstract: LLMs have shown impressive success in program synthesis, discovering programs that surpass prior solutions. However, these approaches rely on simple num

tutorialsarxiv-cs-ai

Agents

Prospective multi-pathogen disease forecasting using autonomous LLM-guided tree search

arXiv:2605.16238v1 Announce Type: new Abstract: Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling tea

agentsarxiv-cs-ai

PSD: Pushing the Pareto Frontier of Diffusion LLMs via Parallel Speculative Decoding

arXiv:2605.15609v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising masked token sequences. Although dLLMs can predict all masked positions i

safetyarxiv-cs-cl

Hardware

Pwn2Own Berlin 2026: participants earned a total of $1,298,250 for 47 vulnerabilities, with successful exploits of AI products like Codex, Cursor, and LM Studio (Eduard Kovacs/SecurityWeek)

Eduard Kovacs / SecurityWeek: Pwn2Own Berlin 2026: participants earned a total of $1,298,250 for 47 vulnerabilities, with successful exploits of AI products like Codex, Cursor, and LM Studio — Partici

hardwaretechmeme

Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels

arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deploy