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PhyDiCT: Plug-and-Play CT Reconstruction from Sparse X-Rays via Differentiable Rendering and Strong Priors

arXiv:2610.09253v1 Announce Type: new Abstract: Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric infor

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PhysEvo: Astra Can Act, Let It

DGX agent

arXiv:2610.08995v1 Announce Type: new Abstract: Astra can act, yet reliable manipulation depends on the system through which it observes and controls the world. We introduce PhysEvo, a framework for p

safetyarxiv-cs-ro

8 Oct 2026

Physics-Aligned Electronic Ground-State Learning Improves Generalization

arXiv:2610.10298v1 Announce Type: new Abstract: Machine-learned interatomic potentials (MLIPs) excel at in-distribution tasks, accelerating drug and material development, yet they struggle to generali

Tutorials

Physics-Informed Neural Plasticity: PDE Solvers That Reshape Themselves

arXiv:2610.09510v1 Announce Type: new Abstract: Physics-informed neural PDE solvers adapt their parameters to satisfy governing equations, yet their representational structure typically remains fixed

tutorialsarxiv-cs-lg

Playing with Kruskal: algorithms for flat and hierarchical watershed cuts

arXiv:2610.10012v1 Announce Type: new Abstract: In the framework of edge-weighted graphs, watersheds have proven to be linked to well-known optimization problems, as Minimum Spanning Tree, which allow

Point It, Strike It: Direction-Conditioned Dynamic Manipulation of Deformable Linear Objects

arXiv:2610.09573v1 Announce Type: new Abstract: Goal-conditioned dynamic manipulation of deformable linear objects has mainly specified goals as positions for a rope tip to reach. Many tasks, however,

safetyarxiv-cs-ro

Policy Learning with Weak Signals

arXiv:2610.10167v1 Announce Type: cross Abstract: Policy learning in digital experimentation faces three challenges: weak signal-to-noise ratios, rich covariate spaces, and massive data volumes. We fo

safetyarxiv-cs-lg

Pooling Representation Autoencoders for Efficient Diffusion

arXiv:2610.09242v1 Announce Type: new Abstract: Representation Autoencoders (RAEs) generate images from pre-trained visual fea- tures, but their dense token grids make generative modeling expensive. M

Hardware

PoreML: A Data-Driven Framework for Learning Multiphase Flow in Porous Media

arXiv:2610.10314v1 Announce Type: new Abstract: Multiphase flow in porous microstructures is central to CO_2 storage, fuel-cell operation, and flip-chip packaging. Predicting these flows remains chall

hardwarearxiv-cs-lg

Position Forcing: Self-Conditioning 3D Generation

arXiv:2610.10342v1 Announce Type: new Abstract: Recent single-stage 3D generative models commonly adopt VecSet representations, encoding 3D shapes as unordered sets of latent tokens. However, compared

Possibilistic Radial Transport for Approximate IM Inference

arXiv:2610.09956v1 Announce Type: cross Abstract: Probing the hypothesis space after seeing the data remains valid under possibilistic inferential models (IMs), provided the significance level stays f

Local Ai

PPCAR-Net: Projection-Refined Parametric 3D Coronary Artery Reconstruction from Sparse X-ray Angiographic Views

arXiv:2610.09383v1 Announce Type: new Abstract: Sparse-view 3D coronary reconstruction commonly relies on cross-view correspondence and triangulation, which are vulnerable to vessel overlap and foresh

local-aiarxiv-cs-cv

Pre-training of Bayesian Optimization Algorithm through Bayesian Optimization

arXiv:2610.10186v1 Announce Type: new Abstract: Bayesian optimization (BO) is widely used as a standard approach for expensive black-box optimization. However, BO algorithms often involve parameters t

Pre-training, Reasoning, Benchmarking: X-ray Report Generation on CheXpert Plus Dataset

arXiv:2610.08813v1 Announce Type: new Abstract: X-ray image-based Radiology Report Generation (RRG) constitutes a critical research direction within medical artificial intelligence, with great potenti

Applications

Precise SE(3) End-Effector Tracking in Whole-Body Humanoid Control

arXiv:2610.09479v1 Announce Type: new Abstract: Precise end-effector tracking during humanoid whole-body motion is challenging due to floating-base oscillations, gravity, dynamic coupling, and locomot

applicationsarxiv-cs-ro

Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation

arXiv:2610.09309v1 Announce Type: new Abstract: Generative world models provide rich predictions of how manipulation scenes may evolve toward task objectives, yet those futures do not directly expose

safetyarxiv-cs-ro

Pretraining Shapes Spectral Structure: Architecture- and Strategy-Conditional Prediction of OOD Robustness in Foundation Models

arXiv:2610.09709v1 Announce Type: new Abstract: Can we determine whether a foundation model will generalize out-of-distribution (OOD) before any target data is available? Existing diagnostics require

PrismSSL: One Interface, Many Modalities; A Single-Interface Library for Multimodal Self-Supervised Learning

arXiv:2511.17776v2 Announce Type: replace Abstract: We present PrismSSL, a Python library that unifies state-of-the-art self-supervised learning (SSL) methods across audio, vision, graphs, and cross-m

Agents

Progress and Prospect of AI in ARPES Workflow

arXiv:2610.10140v1 Announce Type: cross Abstract: Artificial intelligence (AI) is becoming an increasingly useful tool across the experimental sciences, including angle-resolved photoemission spectros

agentsarxiv-cs-lg

Local Ai

ProtocolMatch: Protocol-Dependent Model Selection for Scientific Dynamics Forecasting

arXiv:2610.10239v1 Announce Type: new Abstract: Scientific dynamics forecasting is often framed as an architecture choice, although deployment is also determined by observed history, rollout feedback,

local-aiarxiv-cs-lg

Prototype-Based Knowledge Guidance for Fine-Grained Structured Radiology Reporting

arXiv:2603.11938v2 Announce Type: replace-cross Abstract: Structured radiology reporting promises faster, more consistent communication than free text, but automation remains difficult as models must

Purifying Backdoored Large Vision-Language Models by Removing Hijacked Directions

arXiv:2610.09941v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are increasingly deployed in safety-critical applications, yet they remain vulnerable to backdoor attacks. Defendin

safetyarxiv-cs-cv

PVSync: A Unified Lip-Sync Expert for Timing and Articulation

arXiv:2610.09223v1 Announce Type: new Abstract: Lip movements can match the timing of speech without matching the spoken sounds. We introduce PVSync, a unified model for audio-visual offset estimation

Q-Learning with Scalar Adjoint Matching

arXiv:2610.10437v1 Announce Type: new Abstract: Flow policies capture rich and diverse action distributions, and fine-tuning them with off-policy RL to improve beyond the demonstrations has drawn grow

safetyarxiv-cs-lg

Q-PACE: Dynamic Precision Allocation for Quantization-Aware Training

arXiv:2610.09183v1 Announce Type: new Abstract: Quantization-aware training (QAT) leverages lower-precision arithmetic to reduce the cost of LLM deployment, but aggressive quantization degrades final

Applications

Q-PhotoMarket: A Design Space Exploration Framework for Photonic Hybrid Quantum Neural Networks in Financial Market Prediction

arXiv:2610.09641v1 Announce Type: cross Abstract: Photonic quantum computing has recently emerged as a promising platform for hybrid quantum machine learning due to its native realization of linear-op

applicationsarxiv-cs-lg

Quad-State Safety Evaluation of Open-Weight Large Language Models on Non-Canonical Inputs

model-releasesarxiv-cs-cl

arXiv:2610.09033v1 Announce Type: new Abstract: Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while models in real-world deployment rout

Quadratic Weak-to-Strong Generalization in Random Feature Networks via Random Matrix Theory

arXiv:2610.09044v1 Announce Type: cross Abstract: Weak-to-strong generalization is the phenomenon where a strong student model trained with labels produced by a weak teacher model is able to generaliz

QuadTok: Quadtree Visual Tokenizer for Autoregressive Image Generation

arXiv:2610.10497v1 Announce Type: new Abstract: We introduce QuadTok, a novel framework for visual tokenization and autoregressive image generation. Compared to traditional approaches using 2D grids o

QuanLing: Cross-Branch Validation of Language Distance Quantification on Western Romance

arXiv:2610.08851v1 Announce Type: new Abstract: Quantifying language distance among closely related languages remains a core challenge in quantitative linguistics. Our previous work [1] introduced Qua

researcharxiv-cs-cl

Quantifying Volumetric Risk: Class-Aware Asymmetric Weighted Conformal Prediction for 3D Medical Image Segmentation

arXiv:2610.09392v1 Announce Type: new Abstract: Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated unc

Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders

arXiv:2610.09227v1 Announce Type: cross Abstract: Mixed-precision post-training quantization needs a per-module sensitivity signal; for a text embedder the obvious one -- the retrieval quality a modul

researcharxiv-cs-cl

Applications

Quantum anomaly detection in real scarce data

arXiv:2610.09635v1 Announce Type: cross Abstract: Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains,

applicationsarxiv-cs-lg

Quasi-Binarized Autoencoders: An Architecture-Independent Information Bottleneck for Medical Image Anomaly Detection

arXiv:2610.09670v1 Announce Type: new Abstract: Unsupervised anomaly detection, which learns only from normal images, is a central task in medical image analysis and remains an open problem. Reconstru

R-CNN-Based Chess Position Recognition

arXiv:2610.09191v1 Announce Type: new Abstract: Performing chess game position recognition solely from a single image of a three-dimensional board requires predicting the position and orientation of t

Local Ai

RACER: Reflective Agent Coupling Query Interpretation and Tool-Based Retrieval for Frame Selection in Long Video Understanding

arXiv:2610.08954v1 Announce Type: new Abstract: Video large language models (Vid-LLMs) excel at diverse video-language tasks by reasoning over selected frames. However, frame selection for long videos

local-aiarxiv-cs-cv

RDGSplat: Render-Dedicated Geometry for Novel View Synthesis

arXiv:2610.09173v1 Announce Type: new Abstract: 3D foundation models enable efficient novel view synthesis by carrying a Gaussian head on the representation they already use for reconstruction. Howeve

Real-Time Joint Audio-Video Generation by Parallel Adapter Composition

arXiv:2610.10343v1 Announce Type: new Abstract: Deploying a joint audio-video diffusion transformer for real-time, interactive generation normally requires two essential modifications: block-autoregre

Hardware

RealtimeWAM: How Fast Can I Run My World Action Model?

arXiv:2610.10079v1 Announce Type: new Abstract: World Action Models (WAMs) combine visual dynamics modeling with action generation, but their high inference latency limits responsive robot control. Re

hardwarearxiv-cs-ro

Reasoning-Token Spikes Under Prompted Untruthful Responding in Large Language Models

arXiv:2610.10405v1 Announce Type: cross Abstract: Monitoring the chain-of-thought of reasoning artificial intelligence (AI) models remains a key approach to detecting deception and other forms of misb

researcharxiv-cs-cl

REFIT: Recognize, Fix, and Test Wearable Sensor Placement Shifts without Labels

arXiv:2610.08991v1 Announce Type: new Abstract: We present REFIT, an input calibration for frozen activity-recognition models whose inertial sensors are worn differently at deployment than in training

Reflected Anchored Langevin Algorithms

arXiv:2610.09522v1 Announce Type: cross Abstract: First order Langevin algorithms for constrained sampling in machine learning, such as projected Langevin Monte Carlo which are based on discretization

RELATE: An Evaluation Framework for measuring Relational Orientation of Large Language Models

arXiv:2610.09569v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used for emotional support, raising concern that sustained use may draw users away from their real-world r

safetyarxiv-cs-cl

Relational Abstractions for Spatial Reasoning with Diffusion Models

arXiv:2610.09780v1 Announce Type: new Abstract: Diffusion models excel at image synthesis, but they remain limited in their ability to reliably satisfy structured spatial reasoning constraints. In con

Removing Information Content Does Not Certify Tamper Resistance in Open-Weight Models

arXiv:2610.09004v1 Announce Type: new Abstract: Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot univ

Rephrase Before You Act: Characterizing and Mitigating Language Sensitivity in Vision-Language-Action Models

arXiv:2610.10526v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) are strikingly sensitive to instruction phrasing and do not inherit the language robustness of the vision-languag

safetyarxiv-cs-cl

Reproducible LLM Inference Benchmarking: A Sequential Isolation Protocol for Regression Testing

arXiv:2610.09778v1 Announce Type: cross Abstract: Reproducible benchmarking of Large Language Model (LLM) inference is challenging because repeated measurements can vary with execution and system stat

Requirement-Based Testing: Enhancing Reinforcement Learning with Game Theory

arXiv:2407.18994v2 Announce Type: replace-cross Abstract: We consider the automatic online synthesis of black-box test cases from functional requirements specified as automata for reactive implementat