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Physics-Aware Deepfake Detection via Distance-Speech Consistency

arXiv:2610.18485v1 Announce Type: new Abstract: Recent progress in generative models has made talking-head deepfakes highly realistic and easy to create, jeopardizing public trust. However, most audio

Applications

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Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping

DGX agent

arXiv:2610.17843v1 Announce Type: new Abstract: Proactive dynamic grasping requires robots to predict the future state at which physical contact will occur, rather than reacting to the currently obser

applicationsarxiv-cs-ro

14 Oct 2026

Physics Foundation Models for Efficient Control of Fluid Flows

arXiv:2610.15173v1 Announce Type: cross Abstract: We investigate whether a physics foundation model (PFM) pretrained for PDE forecasting can be repurposed for closed-loop fluid-flow control. We constr

Physics-guided machine learning for saturation control across variable hydraulic conditions in heap leaching

arXiv:2610.15947v1 Announce Type: cross Abstract: Heap-leaching hydraulics vary between regions of an ore bed, motivating fast prediction models that accommodate different material properties. This st

Local Ai

Physics-Informed Graph Neural Networks to Reconstruct Local Fields Considering Finite Strain Hyperelasticity

arXiv:2507.05291v2 Announce Type: replace Abstract: We propose a physics-informed machine learning framework called P-DivGNN to reconstruct local stress fields at the micro-scale, in the context of mu

local-aiarxiv-cs-lg

Physics-informed random neural architectures on compact boundaryless multiply connected manifolds for random-operator models in machine learning and uncertainty quantification

arXiv:2610.14844v1 Announce Type: cross Abstract: We introduce stochastic physics-informed neural networks (SPINNs) for stochastic boundary-value problems (SBVPs) with random operators. The method enf

Physics-Informed State Space Models for Day-Ahead Photovoltaic Power Forecasting

arXiv:2610.14014v1 Announce Type: new Abstract: Accurate day-ahead photovoltaic (PV) power forecasting requires both strong temporal modeling and adherence to physical PV system constraints. Purely da

PhysicsSliders: Learning Physical Controls for Audio Foley Generation

arXiv:2610.18597v1 Announce Type: cross Abstract: Physical properties provide intuitive controls for sounds made by interacting objects. The challenge is to make a generative model respond predictably

PhySimCode: A Benchmark and Evaluation Method for Physics Video to Code Generation

arXiv:2610.15722v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are good at video understanding and code generation, but it remains unclear whether they can recover the physic

Applications

pi-Net: Empirical Latent Representations for Differentiable Physics Heads in Data-Scarce Systems

arXiv:2610.16504v1 Announce Type: new Abstract: Developing reliable predictive models for multi-physics systems often forces a compromise between computationally demanding physical simulators and data

applicationsarxiv-cs-lg

Pick the Mixture, Not the Samples: Combinatorial Data Selection for LLMs

arXiv:2610.14028v1 Announce Type: new Abstract: Data selection for LLM fine-tuning faces two facts about real training: data pools draw from many distinct domains, and evaluation spans diverse tasks.

Pickalo: Leveraging 6D Pose Estimation for Low-Cost Industrial Bin Picking

arXiv:2604.04690v3 Announce Type: replace Abstract: Bin picking in real industrial environments remains challenging due to severe clutter, occlusions, and the high cost of traditional 3D sensing setup

researcharxiv-cs-ro

PID-Gated Continual Prompt Adaptation Under Noisy Feedback

arXiv:2610.16129v1 Announce Type: new Abstract: Deploying LLM agents in non-stationary environments requires continually revising their instructions as the task drifts, yet rewriting after every repor

PIGEONS: Incremental Long-Video Captioning with Persistent Identity Gallery and Evolving Overarching Narrative Summary

arXiv:2610.16066v1 Announce Type: new Abstract: Detailed video captioning has become an essential semantic interface for multi-modal understanding and generation. While existing methods mainly target

Local Ai

PILOT: Guiding Long-Horizon World-Model Planning for Robot Manipulation with Visual Subgoals and Policy Priors

arXiv:2610.14335v1 Announce Type: new Abstract: Latent world-model planning has shown promising results in goal-conditioned robotic manipulation, but remains challenging for long-horizon planning due

local-aiarxiv-cs-ro

PING: physics informed graph neural network for advection-diffusion processes with applications to solar power forecasting

arXiv:2610.18280v1 Announce Type: new Abstract: Advection-diffusion plays an essential role in transport phenomena such as atmospheric and cloud dynamics. Improved forecasting of cloud motion, in turn

tutorialsarxiv-cs-lg

Applications

PINT: A Unified Model with Shared Predictive Intent for Robot Manipulation

arXiv:2610.16355v1 Announce Type: new Abstract: Generalist robot manipulation can benefit from pretrained vision-language models (VLMs) for semantic reasoning and video generation models (VGMs) for wo

applicationsarxiv-cs-ro

PixARG: Pixel-Asynchronous Retrieval-Augmented Diffusion for Fast and Realistic Image Generation

arXiv:2610.14713v1 Announce Type: new Abstract: While diffusion models achieve impressive image generation capabilities, they suffer from high computational costs, implausible artifacts, and limited a

safetyarxiv-cs-cv

PixelMoE: Scaling Pixel-Space Diffusion with LLM-Style Mixture-of-Experts

arXiv:2610.18857v1 Announce Type: new Abstract: Pixel-space diffusion models provide an appealing alternative to latent-space diffusion models by directly modeling raw images without relying on a lear

Pixels or Code? Benchmarking Instruction-Guided Visual Artifact Operation

arXiv:2610.16444v1 Announce Type: new Abstract: Visual artifacts such as charts, technical diagrams, webpages, and document pages underpin scientific, commercial, and engineering workflows. Editing th

agentsarxiv-cs-cv

PixSearch: Region-Grounded Retrieval for Knowledgeable Large Multimodal Models

arXiv:2601.19060v2 Announce Type: replace Abstract: Multimodal Retrieval-Augmented Generation (MM-RAG) augments visual question answering (VQA) with external knowledge, but many systems rely on modula

tutorialsarxiv-cs-cv

Plan-Bench: Can Existing VLMs Go Well Beyond Perception to Reason and Plan in Embodied Worlds?

arXiv:2610.18836v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have achieved strong performance in visual perception and spatial reasoning, leading to growing interest in using them as

Planner-Guided Recursive Exploration for Diffusion Language Model Decoding

arXiv:2607.15655v2 Announce Type: replace-cross Abstract: Masked diffusion language models (DLMs) enable parallel text generation by iteratively refining masked tokens, offering a promising alternativ

PlanPath: Exploration-Guided Orchestration for Patient-Level Pathology Reasoning

arXiv:2610.18664v1 Announce Type: new Abstract: A patient's pathology case spans tens of slides from distinct specimens and anatomical sites, each carrying non-redundant, site-specific evidence. Formi

agentsarxiv-cs-cv

PLAYGROUND: Progressive Layout Generation with Render-Grounded Planning

arXiv:2610.18865v1 Announce Type: new Abstract: Generating editable graphic designs requires arranging visual and textual elements into a coherent composition while preserving their content and editab

PLDiv: Measuring Dataset Diversity and Semantic Collapse via Persistence Landscapes

arXiv:2610.14031v1 Announce Type: cross Abstract: Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation. As datasets

PLEDGE: Progressive Lossy Error-Bounded Compression of 3D Gaussian Splatting

arXiv:2610.17605v1 Announce Type: new Abstract: Existing 3D Gaussian Splatting (3DGS) codecs report reconstruction quality after compression, and give users little control over the distortion of indiv

Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing

arXiv:2605.24273v2 Announce Type: replace Abstract: Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantifica

Industry

Pluto Security nabs $42M to help enterprises secure their AI agents

Pluto Security Inc., a provider of artificial intelligence security software, today announced that it has raised 42 million in funding. The company raised about a fifth of the sum through a seed round

industrysiliconangle

PointWalker: Mapless Quadruped Locomotion Directly From Raw LiDAR Point Clouds

arXiv:2610.15548v1 Announce Type: new Abstract: Quadruped robots deployed for inspection and field missions must cross heterogeneous terrain, from stairs, slopes, and gaps to high platforms, stepping

safetyarxiv-cs-ro

Poisoned into Action: Context Attacks on Typed Decision Workflows

arXiv:2610.15537v1 Announce Type: cross Abstract: Typed decision interfaces restrict models to predefined actions, yet schema-valid choices can still trigger policy-violating state changes. Evaluating

PolUR: Polarization Aware Multimodal Degradation Perception for Underwater Image Restoration

arXiv:2610.17991v1 Announce Type: new Abstract: Recovering high-quality images from degraded underwater observations is crucial for underwater exploration. Polarization observations show promise as an

Position: Beyond Training Time, Test-Time Coordination is Essential for Cooperative MARL

arXiv:2610.17409v1 Announce Type: new Abstract: We argue that cooperative multi-agent reinforcement learning (MARL) should treat test-time coordination as part of the problem definition rather than a

agentsarxiv-cs-lg

Posterior-Averaged Bellman Flow Matching for Distributional Critics

arXiv:2610.17788v1 Announce Type: new Abstract: Distributional critics model the full return distribution, providing richer signals for exploration and risk-sensitive control. Recent work has shown th

Posterior Flow Matching

arXiv:2610.13994v1 Announce Type: cross Abstract: Flow matching learns how to transport noise toward data, but its mean prediction does not directly report the endpoint uncertainty remaining at an int

tutorialsarxiv-cs-cv

PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation

arXiv:2610.14409v1 Announce Type: new Abstract: Generating an image with a diffusion or flow-matching model typically involves integrating a velocity field for hundreds of sequential steps. That compu

PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection

arXiv:2509.19774v3 Announce Type: replace Abstract: Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the

PRAISE: A Reproducible Workflow for Non-Programmer Scientists to Implement Published AI Models

arXiv:2610.16980v1 Announce Type: cross Abstract: Artificial Intelligence (AI) models are increasingly developed, published and used across scientific fields. Researchers, students and industry profes

tutorialsarxiv-cs-lg

Predict Before You Modify: Learning Improvement Dynamics for Self-Evolving Agents

arXiv:2610.13991v1 Announce Type: new Abstract: Self-evolving agents improve through repeated cycles of modification and evaluation. However, existing methods receive feedback on modifications only af

agentsarxiv-cs-lg

Predict in Chunks, Revise in Spans: History-conditioned Action Regeneration for Robotic Manipulation

arXiv:2610.14864v1 Announce Type: new Abstract: Action chunking improves temporal coherence in visuomotor imitation learning by jointly predicting sequences of future actions. However, many existing p

safetyarxiv-cs-ro

Predictable AI spend in BigQuery: Introducing cost management for generative AI functions

model-releasesgoogle-cloud-ai

The generative AI functions in BigQuery (AI.GENERATE, AI.IF, AI.CLASSIFY, AI.SCORE, and more) bring large language models (LLMs) directly into your SQL, so a single query can classify millions of supp

Predicting Audio Popularity: A Comparative Study of Metadata and Acoustic Features Across Music and Sound Effects

arXiv:2610.16809v1 Announce Type: cross Abstract: This study addresses the prediction of audio popularity on an online sharing platform by leveraging both metadata and acoustic feature representations

Predicting Mental Health Help-Seeking Behaviour on Reddit: A Survival Analysis of Linguistic and Behavioural Signals

arXiv:2610.16983v1 Announce Type: cross Abstract: Most prior work on predicting mental health help-seeking treats it as post-hoc classification of users already active in clinical communities, which c

Local Ai

Prediction Is All MoE Needs: Expert Load Distribution Goes from Fluctuating to Stabilizing

arXiv:2404.16914v2 Announce Type: replace Abstract: MoE facilitates the development of large models by making the computational complexity of the model no longer scale linearly with increasing paramet

local-aiarxiv-cs-lg

Prediction with Expert Advice under Heavy-Tailed Losses

arXiv:2610.14904v1 Announce Type: new Abstract: We study prediction with expert advice over T rounds with K experts under heavy-tailed losses whose (1+epsilon)-moment is bounded by heta>0 for some eps

Preserving Updates in Low-Bit Recurrent State Quantization

arXiv:2610.15709v1 Announce Type: new Abstract: Linear attention and recurrent models compress past tokens into a fixed-size state that is updated at every step. However, storing these states in high

PreSO: Privileged Representation Isolation for On-Policy Self-Distillation

arXiv:2610.14001v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) uses a self-teacher conditioned on privileged information to turn the sparse outcome signal of reinforcement learning

PRIME: Frequency-Aware Unified Pruning and Merging for MoE Compression

arXiv:2610.13982v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models have emerged as an effective paradigm for scaling large language models, yet their massive parameter footprint poses sev