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PhaseMatcher: Autoregressive Phase-Set Identification with Spectral Decomposition

arXiv:2610.05844v1 Announce Type: cross Abstract: Recovering complete phase sets from powder X-ray diffraction (PXRD) is challenging when weak-phase peaks overlap stronger signals. A natural strategy

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Physics-Augmented Graph Transformers for Patch-Antenna Forward and Inverse Design

DGX agent

arXiv:2610.05004v1 Announce Type: new Abstract: Full-wave electromagnetic (EM) simulation enables accurate patch-antenna analysis but is computationally expensive for large-scale forward prediction an

model-releasesarxiv-cs-lg

6 Oct 2026

Applications

Physics-aware Masked Diffusion-based Flood Simulation for Urban Fisheye Disaster Detection

arXiv:2607.15527v2 Announce Type: replace Abstract: Physical simulations that predict the behavior of urban disasters, such as climate-related flooding, play a crucial role in disaster prevention and

applicationsarxiv-cs-cv

Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow

arXiv:2610.05959v1 Announce Type: new Abstract: Recent neural power-flow solvers, including emerging foundation models, achieve accurate voltage predictions, yet such accuracy does not necessarily imp

Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator

arXiv:2509.22458v3 Announce Type: replace Abstract: Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers,

Physics-informed neural operator for parametric phase-field modelling of interfacial degradation and microstructural evolution

arXiv:2603.09693v2 Announce Type: replace Abstract: Predicting interfacial degradation and microstructural evolution through phase-field modelling is computationally intensive, particularly for parame

Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics

arXiv:2610.04108v1 Announce Type: new Abstract: Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing. However, existing methods rely on numerical sol

Physics Residual Dynamics and Reduced Order Whole-Body Planning for Obstacle Aware Human Robot Cloth CoTransportation

arXiv:2610.06641v1 Announce Type: new Abstract: Human--robot co-transportation of deformable objects requires predicting object deformation during motion, since obstacle clearance depends on both the

researcharxiv-cs-ro

Hardware

PhysMamba: Selective State Space Models as Learned Articulated Body Simulators

arXiv:2610.04014v1 Announce Type: new Abstract: We introduce the first learned articulated body simulator based on a selective state space model (SSM), called PhysMamba. PhysMamba predicts next-frame

hardwarearxiv-cs-cv

pi^2: Structure-Originated Reasoning Data Improves Long-Context Reasoning Ability of Large Language Models

arXiv:2604.05114v2 Announce Type: replace-cross Abstract: We study a QA curation pipeline for improving long-context complex reasoning in large language models (LLMs). Our approach, pi^2, constructs h

PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning

arXiv:2610.04850v1 Announce Type: new Abstract: Protein binding prediction is central to target identification, therapeutic binder design, and large scale screening, yet remains challenging because bi

PixReenact: Pixel-Conditioned Causal Video Diffusion for Streaming Head-Avatar Reenactment

arXiv:2610.05233v1 Announce Type: new Abstract: Streaming head-avatar reenactment aims to animate a reference image according to a live driving video, requiring robust motion transfer, long-term ident

Planetary Geospatial Foundation Models: A New Paradigm for Global Public Health

arXiv:2610.05699v1 Announce Type: new Abstract: The efficacy of traditional disease prediction is limited by spatial gaps and temporal lags, which impact the timing and targets of resource deployments

Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift

arXiv:2604.01142v2 Announce Type: replace-cross Abstract: Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test condi

PlatoLTL: Scaling LTL-Guided Multi-Task RL

arXiv:2601.22891v3 Announce Type: replace Abstract: Linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks in multi-task reinforcement lea

PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data

arXiv:2610.06825v1 Announce Type: cross Abstract: Scientific figures often encode quantitative results that are not readily available in machine-readable form, making accurate plot digitization import

Poisson-GENERIC Neural Operators: Exact Metriplectic Structure in Function Space via Casimir Entropies

arXiv:2610.05570v1 Announce Type: new Abstract: Existing thermodynamically consistent neural operators impose the GENERIC degeneracy conditions by projecting the reversible operator onto the complemen

Policy as Data: Replay-Based Policy Dual Averaging via Advantage Regression

arXiv:2610.04638v1 Announce Type: new Abstract: Actor-critic methods reuse past experience to improve sample efficiency. However, historical data are typically regarded as off-policy samples for the c

Polynomial neural surrogates for designing photonic quantum experiments

arXiv:2610.06032v1 Announce Type: cross Abstract: Physics simulators can support the discovery of quantum experiments by predicting the states generated by experimental configurations. When these simu

Local Ai

Population Scaling or Data Dilution? Dynamics of Local Topology Evolution in Decentralized Learning

arXiv:2610.05476v1 Announce Type: new Abstract: Scaling decentralized learning changes not only the number of clients N, but also the dynamics of information propagation and consensus. We argue that t

local-aiarxiv-cs-lg

PortraitAes: Intent-Conditioned Structured Portrait Aesthetics Assessment

arXiv:2610.05010v1 Announce Type: new Abstract: Portrait aesthetic assessment assigns comparable scores according to how effectively human-centered images fulfill their photographic intent. These scor

Local Ai

PreAct-Nav: Agentic Reasoning Before Action for Urban Navigation

arXiv:2610.04916v1 Announce Type: new Abstract: Urban navigation requires embodied agents to pursue long-horizon goals through local decisions based on egocentric observations. However, existing agent

local-aiarxiv-cs-ro

Priced Guidance: Can Language Models Generate Future Research Ideas?

model-releasesarxiv-cs-lg

arXiv:2610.04976v1 Announce Type: new Abstract: We evaluate language models' capability to generate novel research ideas through the lens of compression. We aim to lower-bound the potentially tiny pro

Principled Top-k Selection for Language Models with Hybrid Gradients

arXiv:2610.04162v1 Announce Type: new Abstract: Selecting the best k items out of m candidates is a critical component of modern large language model systems, such as document selection in Retrieval-A

Prism: Dynamic Sparse Attention for Native 2K Joint Video-Audio Generation Model Training

arXiv:2610.05416v1 Announce Type: new Abstract: Natively training joint video-audio generation models at higher resolutions empowers them to learn richer visual details and sharper motion dynamics. Ho

tutorialsarxiv-cs-cv

Private Component-by-Component Learning

arXiv:2610.05102v1 Announce Type: new Abstract: We study differentially private learning problems in the realizable setting, where a hypothesis is specified by k components. A direct iteration of priv

Private online learning and prediction for Littlestone classes

arXiv:2610.06822v1 Announce Type: new Abstract: We study mistake bounds for differentially private online learning and online prediction under oblivious realisable adversaries. Online learning require

Probabilistic Algorithms for Ising Machines from Optimization to Generative AI

arXiv:2610.03972v1 Announce Type: new Abstract: Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasing

Probabilistic Pedestrian Forecasts from a Handheld Phone: World-Frame Heat Maps, Visual-Inertial Height Drift, and Evaluation without Ground Truth

arXiv:2610.04736v1 Announce Type: new Abstract: A pedestrian with a phone could be shown where nearby people will be in the next few seconds, if the forecast stays on the ground while the phone moves,

Probability flow ODEs in score-based and reflected diffusion models

arXiv:2610.03846v1 Announce Type: cross Abstract: Probability-flow ordinary differential equations (PF-ODEs) are widely used as deterministic samplers for score-based diffusion models. Their usual jus

ProbeFlow: Training-Free Adaptive Flow Matching for Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2603.17850v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models equipped with Flow Matching (FM) action heads achieve state-of-the-art performance in complex robot manip

ProDER: A Continual Learning Approach for Fault Classification and Localization in Evolving Smart Grids

arXiv:2511.05420v3 Announce Type: replace Abstract: Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and moni

Applications

Programming Manufacturing Robots with Imperfect AI: LLMs as Tuning Experts for FDM Print Configuration Selection

arXiv:2603.22118v2 Announce Type: replace Abstract: We use fused deposition modeling (FDM) 3D printing as a case study of how manufacturing robots can use imperfect AI. In FDM, print configuration str

applicationsarxiv-cs-ro

Progressive Multi-Ancestor Bit-Depth Distillation

arXiv:2610.04100v1 Announce Type: new Abstract: Model compression strategies are widely employed to reduce memory footprint and network complexity, particularly for devices with constrained computatio

tutorialsarxiv-cs-lg

Local Ai

Prompt and Refinement: Asymmetric Mutual Learning for Infrared Small Target Detection with Noisy Labels

arXiv:2610.05918v1 Announce Type: new Abstract: Existing data-driven infrared small target detection (ISTD) methods typically require large-scale datasets with accurate pixel-level annotations for mod

local-aiarxiv-cs-cv

Prompt Dominance and Asymmetric Verifier Costs: Empirical Ablations of GRPO at 1B Scale on GSM8K

arXiv:2610.04928v1 Announce Type: new Abstract: This paper studies GRPO at 1B scale from both directions: what estimator choices do to the learning signal, and what a degraded reward signal does to wh

Propagating Elevation-Map Uncertainty Through the Contact Maximum in Closed Form

model-releasesarxiv-cs-ro

arXiv:2610.06103v1 Announce Type: new Abstract: Risk-aware planners score paths on uncertain elevation maps using the path cost's mean and standard deviation. Modeling rigid contact, however, requires

Protecting Sensitive Data in Image Synthesis via PAC-Private Adaptation for Diffusion Models

arXiv:2610.04038v1 Announce Type: new Abstract: Synthetic data are increasingly used as an alternative to sharing sensitive records. However, synthetic data generation does not guarantee privacy, as d

tutorialsarxiv-cs-lg

Protocol-Sensitive Evaluation of Log Anomaly Detection: Component Costs and Target-Access Sensitivity on HDFS and BGL

arXiv:2610.05807v1 Announce Type: cross Abstract: Protocol choices can change the conclusions drawn from log anomaly detection benchmarks even when detector settings are fixed. We present a joint empi

Proximal Causal Learning under Unmeasured Confounding

arXiv:2610.04519v1 Announce Type: new Abstract: Estimating treatment effects from observational data typically relies on the No Unmeasured Confounding Assumption (NUCA), which rarely holds in practice

Agents

Proxy Confidence: Auditing Black-Box LLM Agents with a Surrogate's Log-Probabilities

arXiv:2610.03894v1 Announce Type: cross Abstract: A deployed LLM agent emits tool calls, queries, and code that can be silently wrong -- by the time the error surfaces, the action has run. Frontier ch

agentsarxiv-cs-lg

PWM: Personalized World Models with Online Reinforcement Learning

arXiv:2610.04920v1 Announce Type: new Abstract: Pretrained world models can generate diverse environments, yet users often want to explore a particular scene specified by their own video. This require

safetyarxiv-cs-cv

PyINE: A Framework for Scalable Elicitation and Oversight via Code Execution

arXiv:2610.04737v1 Announce Type: cross Abstract: Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight pro

Pythia: Toward Foundation World Models for Multimodal Time Series

arXiv:2610.05240v1 Announce Type: new Abstract: Time-series foundation models offer a unified approach to forecasting across heterogeneous domains. Textual context and auxiliary observations provide c

tutorialsarxiv-cs-lg

Quantifying the Stability of Multi-Step Reasoning via Error Amplification

arXiv:2610.06404v1 Announce Type: new Abstract: We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algori

Quantum data loading from the learned shared structure of real signals

arXiv:2610.06076v1 Announce Type: cross Abstract: Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show

Quantum Information Ordering and Differential Privacy

model-releasesarxiv-cs-lg

arXiv:2511.01467v4 Announce Type: replace-cross Abstract: We study quantum differential privacy (QDP) by defining a notion of the order of informativeness between two pairs of quantum states. In parti

Quantum Machine Learning Protection of Military Quantum Key Distribution Against Cryptographically Camouflaged Attacks

arXiv:2610.04543v1 Announce Type: cross Abstract: Quantum key distribution proves its protocol secure and says nothing about the hardware beneath it, so military and government operators fielding it f