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Parser-Free VLM Verification for Federated Weakly Supervised Video Anomaly Detection

arXiv:2609.07455v1 Announce Type: new Abstract: How can vision-language models help video anomaly detection (VAD) when surveillance data remain distributed, weakly labeled, and resource-constrained? M

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Particle Dynamics of Flow Matching and Classifier-Free Guidance from a Stagewise Geometry Perspective

DGX agent

arXiv:2609.06947v1 Announce Type: new Abstract: Flow matching, together with classifier-free guidance (CFG), is widely used in generative modeling, yet much of the theoretical understanding remains di

researcharxiv-cs-lg

9 Sept 2026

PASTEL: Panoramic Alignment for Monocular 4D Scene Reconstruction

arXiv:2609.06099v1 Announce Type: new Abstract: Reconstructing 4D scenes from casually captured monocular video is vital for applications in virtual reality (VR) and embodied AI. Recent advances in 4D

PatchFormer: A Patch-Based Time Series Foundation Model with Hierarchical Masked Reconstruction and Cross-Domain Transfer Learning for Zero-Shot Multi-Horizon Forecasting

arXiv:2601.20845v2 Announce Type: replace Abstract: Time series forecasting is a fundamental problem with applications in climate, energy, healthcare, and finance. Many existing approaches require dom

Patient-Specific Articulated Digital Twins from a Single Full-Body CT Scan

arXiv:2607.02156v2 Announce Type: replace Abstract: Patient-specific anatomical models provide individualized context for surgical planning, image-guided intervention, and algorithm development. Howev

Paul Christiano, an AI researcher and advisor at CAISI, is joining the OpenAI Foundation board of directors and its Safety and Security Committee (Tim Fernholz/TechCrunch)

Tim Fernholz / TechCrunch: Paul Christiano, an AI researcher and advisor at CAISI, is joining the OpenAI Foundation board of directors and its Safety and Security Committee — Paul Christiano, an influ

safetytechmeme

Paul Christiano, founder of the Alignment Research Center, is joining the OpenAI Foundation Board and its Safety and Security Committee, whi…

Paul Christiano, founder of the Alignment Research Center, is joining the OpenAI Foundation Board and its Safety and Security Committee, which provides governance over the safety and security practice

safetyopenai--x

PCFlow: Physics-Conditioned Flow Matching for GPR B-Scan Image Synthesis

arXiv:2609.07300v1 Announce Type: new Abstract: Ground-penetrating radar (GPR) B-scan image synthesis is important for data augmentation, algorithm validation, and simulation acceleration, yet generat

PCSDiff: Diffusion-Based Bias Correction and Super Resolution Toward Practical Operational Medium-Term Precipitation Forecast

arXiv:2609.06942v1 Announce Type: new Abstract: Medium-range precipitation forecasts are impaired by persistent systematic biases, lead-time-dependent error accumulation, and coarse spatial resolution

local-aiarxiv-cs-lg

Perplexity Search inside Hermes. Perplexity's index currently includes 450B+ high-quality URLs. Rapidly advancing to a trillion by EOY with …

Perplexity Search inside Hermes. Perplexity's index currently includes 450B+ high-quality URLs. Rapidly advancing to a trillion by EOY with high-quality snippets. Perplexity Search API is now availabl

agentsnous-research--x

PGMT: Perceptive General Motion Tracking for Humanoid Robots

arXiv:2609.08511v1 Announce Type: new Abstract: Humanoid motion trackers can reproduce diverse whole-body motions, but their performance degrades on complex terrain where terrain-agnostic references b

safetyarxiv-cs-ro

Phase-and-First-Arrival VLM Feedback for Sparse-Reward Reinforcement Learning in Surgical Manipulation

arXiv:2609.07211v1 Announce Type: new Abstract: Sparse outcome feedback limits what robots can learn from unsuccessful attempts at complex manipulation. Failed multi-stage surgical attempts can contai

agentsarxiv-cs-ro

PhenoBench: Mapping What a Deeply Phenotyped Human Cohort Can Tell Us

arXiv:2609.06080v1 Announce Type: new Abstract: Deeply phenotyped cohorts combine clinical, imaging, molecular, and wearable observations across timescales from seconds to years. This breadth can reve

PhysFlow: Physics-Aware Optical Flow for Motion Controllable Video Generation

arXiv:2609.08215v1 Announce Type: new Abstract: Video generation models have recently attracted substantial attention for their ability to generate visually compelling videos, yet ensuring physically

local-aiarxiv-cs-cv

Physical policy gradient theorem for in situ stochastic-adjoint training

arXiv:2609.05808v1 Announce Type: cross Abstract: In situ adjoint training extracts parameter gradients directly from measurement, but has so far been limited to reciprocal or restricted systems. Here

Physico-Geospatial Grounded Scene Interpretation for Mobile Robotics

arXiv:2609.06629v1 Announce Type: new Abstract: Recent advancements in deep learning allow robotic agents to interact with dynamic and unstructured environments. Of special interest is the integration

Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU

arXiv:2609.08992v1 Announce Type: new Abstract: False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework combi

Physics-Informed Neural Networks for Depth-Averaged Granular Avalanche Dynamics on Curved Topography

arXiv:2609.05542v1 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) provide a mesh-free framework for solving governing equations, but their application to granular avalanche dy

PhysReal: Learning Real-World Deformable Object Physics via Hybrid Constitutive Modeling

arXiv:2609.07532v1 Announce Type: new Abstract: Learning physically plausible dynamics from visual observations is essential for interactive world models and embodied agents. However, modeling real-wo

local-aiarxiv-cs-ro

PhysSAE: Mechanistic Interpretability with Sparse Autoencoders

arXiv:2609.07061v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) embed PDE residuals into neural network training, but their internal representations remain opaque: it is unkno

PhysVidBench: Language-Grounded Evaluation of Physical Commonsense in Text-to-Video Models

model-releasesarxiv-cs-cv

arXiv:2507.15824v2 Announce Type: replace Abstract: Text-to-video (T2V) models now produce striking visuals, yet they routinely violate everyday physics; objects float, tools are misused, and causal s

PhysWeep: Does a Video Generator Realize the Physics You Ask For?

model-releasesarxiv-cs-cv

arXiv:2609.06207v1 Announce Type: new Abstract: Image-to-video generators are often credited with absorbing physical dynamics as implicit world models, a claim the community currently checks with plau

PIC: Revisiting INR for Image Coding with Fast Encoding and Sub-Millisecond Decoding

arXiv:2609.09020v1 Announce Type: new Abstract: Implicit neural representation (INR) has achieved remarkable progress in novel view synthesis and image/video coding in recent years.Compared to convent

PICANet: Physics-Informed Cascaded Asymmetric Network for Infrared Small Target Detection

arXiv:2609.07515v1 Announce Type: new Abstract: Infrared small target detection (ISTD) is an important research direction in image processing. However, existing methods are limited by severe backgroun

PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving

arXiv:2609.08965v1 Announce Type: cross Abstract: Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving

safetyarxiv-cs-ro

Hardware

PlayTrain: An Efficient Reinforcement Learning Framework for LLM-Generated Adaptable JavaScript Games

arXiv:2609.09059v1 Announce Type: new Abstract: While many video-game environments (VGEs) have played crucial roles in advancing reinforcement learning (RL), developing novel VGEs or modifying existin

hardwarearxiv-cs-lg

PLSR: Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion

arXiv:2609.06436v1 Announce Type: new Abstract: High-resolution 3D asset generation is vital in various 3D applications. Existing state-of-the-art diffusion-based models remain constrained by fixed re

local-aiarxiv-cs-cv

PocketVE: Stable and Property-Guided Structure-Based Drug Design with Variance-Exploding Diffusion

arXiv:2609.08101v1 Announce Type: cross Abstract: Protein-conditioned 3D molecule generation is a central challenge in structure-based drug design, requiring a balance between pocket compatibility, mo

Point4D: Long-range 4D Motion Reconstruction

arXiv:2609.09145v1 Announce Type: new Abstract: We introduce Point4D, a feed-forward model for 4D reconstruction of long-range video sequences. Point4D is able to reliably infer dense per-point 3D tra

Poisson Image Denoising Using Minimax Concave and Reweighted ell_1 Penalties: Nonblind and Blind Approaches

arXiv:2609.07916v1 Announce Type: new Abstract: Images are important tools in various sciences. Despite the development of photo-taking tools, creating clear and image without noise remains challengin

agentsarxiv-cs-cv

Tutorials

Polarity-Asymmetric Structural Calibration for Link Sign Prediction

arXiv:2609.05896v1 Announce Type: cross Abstract: Link sign prediction (LSP) aims to infer the positive or negative polarity of unobserved links in signed networks. Signed Graph Neural Networks (SGNNs

tutorialsarxiv-cs-lg

Policy Gradients for Cumulative Prospect Theory in Reinforcement Learning

arXiv:2410.02605v5 Announce Type: replace Abstract: We derive a policy gradient theorem for Cumulative Prospect Theory (CPT) objectives in finite-horizon Reinforcement Learning (RL), generalizing the

safetyarxiv-cs-lg

Tutorials

Positive Sample Propagation along the Audio-Visual Event Line

arXiv:2104.00239v3 Announce Type: replace Abstract: Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments

tutorialsarxiv-cs-cv

Power Mean Estimation in Stochastic Continuous Monte Carlo Tree Search

arXiv:2609.06489v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) has demonstrated success in online planning for deterministic environments, yet significant challenges remain in adapting

PPIM: Pennes Physics-Informed Mamba for Heat-Source-Conditioned 3D Bioheat Simulation

arXiv:2609.06869v1 Announce Type: new Abstract: Three-dimensional bioheat simulation aims to predict transient temperature distributions in biological tissue and is commonly modeled using the Pennes b

local-aiarxiv-cs-lg

Pre-Whitening and BCJR Posterior Distillation for Bi-LSTM Detection in Faster-than-Nyquist Signaling

arXiv:2609.07762v1 Announce Type: cross Abstract: Recurrent detectors such as bidirectional long short-term memory (Bi-LSTM) networks are low-complexity alternatives to the optimal Bahl-Cocke-Jelinek-

Precision at Scale: Domain-Specific Datasets On-Demand

arXiv:2407.03463v2 Announce Type: replace Abstract: In the realm of self-supervised learning (SSL), conventional wisdom has gravitated towards the utility of massive, general domain datasets for pretr

agentsarxiv-cs-cv

Predicting Wind Turbine Power Using Machine Learning and Weather Forecasts

arXiv:2609.06194v1 Announce Type: cross Abstract: Offshore wind turbines are widely used to generate renewable energy, but their maintenance can result in decreased efficiency due to forced shutdowns.

Predictive-Coding-Based Autonomous Regulation of Internally Generated and Externally Coupled Processing in Human-Robot Interaction

arXiv:2609.06888v1 Announce Type: new Abstract: Predictive coding characterizes adaptive behavior as a dynamic balance between internally generated predictions and external sensory evidence, yet how a

agentsarxiv-cs-ro

Prevalence calibration as shortcut mitigation

arXiv:2609.07922v1 Announce Type: cross Abstract: Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing miti

PRG-Fusion: Orchestrating Generative Priors with Reconstruction Evidence for Driving View Synthesis

arXiv:2609.06948v1 Announce Type: new Abstract: Synthesizing photorealistic driving videos along specified trajectories is essential for scalable closed-loop simulation. Reconstruction-based methods l

Prior-free relative 6D pose estimation of multiple object instances

model-releasesarxiv-cs-cv

arXiv:2609.08949v1 Announce Type: new Abstract: Object 6D pose estimation formulations have progressively reduced reliance on object-specific priors, evolving from explicit 3D models to multi-view obj

Applications

Privacy Leakage from a Thousand Words: Millipixel Location Recovery from Dot Maps

arXiv:2609.07623v1 Announce Type: cross Abstract: Dot maps, which visualize individual data points as dots over a geographic region, are widely used across diverse domains to represent spatial pattern

applicationsarxiv-cs-cv

Proactive Context-Forecasted Safety Constraints for Nonstationary Reinforcement Learning

arXiv:2609.08080v1 Announce Type: new Abstract: Ensuring safety in reinforcement learning under nonstationarity requires anticipating changes in risk before they lead to unsafe behavior. Existing appr

safetyarxiv-cs-lg

Programmable Cellular Automata

arXiv:2609.06102v1 Announce Type: cross Abstract: Cellular automata is a local computation paradigm where complex behavior can arise from local interactions between simple functions. This paradigm has

Projected Neural Differential Equations for Learning Constrained Dynamics

arXiv:2410.23667v2 Announce Type: replace Abstract: Neural differential equations offer a powerful approach for learning dynamical systems from data. However, they do not inherently respect known cons

Hardware

Proper Dataset Valuation by Pointwise Mutual Information

arXiv:2405.18253v4 Announce Type: replace Abstract: Data plays a central role in advancements in modern artificial intelligence, with high-quality data emerging as a key driver of model performance. T

hardwarearxiv-cs-lg

Protocol effects on feature-based hardware-Trojan detection across Trust-Hub families

arXiv:2609.07199v1 Announce Type: new Abstract: Trust-Hub reuses host circuits: several files differ mainly in the inserted Trojan. When gates from sibling variants enter both training and test folds,