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ORACLE: Optimizer-Relative Alignment for Constrained LEarning

arXiv:2610.09040v1 Announce Type: new Abstract: Constraint handling methods typically intervene before the optimizer acts, by modifying the objective or the gradient. Yet momentum, adaptive scaling, a

Local Ai

OrBIT: Structure-Guided Embedding Compression

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DGX agent

arXiv:2610.10385v1 Announce Type: new Abstract: Embedding tables are among the largest components of modern language models. Most compression methods fix a coding geometry such as coordinate blocks, l

local-aiarxiv-cs-lg

8 Oct 2026

ORCA: Hunting Compositional Failures in Text-to-Image Diffusion

arXiv:2610.09841v1 Announce Type: new Abstract: Text-to-image diffusion models fail predictably on compositional prompts: attributes bind to the wrong objects, spatial relations invert, and multi-obje

safetyarxiv-cs-cv

Order-Optimal Sample Complexity of Rectified Flows

arXiv:2601.20250v2 Announce Type: replace Abstract: Recently, flow-based generative models have shown superior efficiency compared to diffusion models. In this paper, we study rectified flow models, w

ORDERS: An Empirical Study of Norm-Rank Aggregation for Personalized Federated Learning

arXiv:2610.10361v1 Announce Type: new Abstract: Personalized federated learning combines shared representations with client-specific predictors, but the contribution of a server weighting rule can be

Origins of Universal Machine Learning Force-Field Errors in Multicomponent Materials

arXiv:2610.09837v1 Announce Type: cross Abstract: Universal machine learning force-field generalization to multicomponent environments generated by compositional design remains insufficiently assessed

Tutorials

OrthoGen: A Generative Orthogonal Learner for Time-Varying Treatments

arXiv:2610.10210v1 Announce Type: new Abstract: Estimating conditional distributional potential outcomes (CDPOs) over time is important in medicine (e.g., to estimate patient-specific risks under diff

tutorialsarxiv-cs-lg

Oscillatory Neural Dynamics over Sheaves

arXiv:2610.10018v1 Announce Type: new Abstract: Effective long-range propagation remains a central challenge in graph neural networks, as increasing a model's propagation depth does not guarantee that

Out-of-Air Computation: Enabling Structured Function Extraction from Wireless Superposition

arXiv:2604.04312v2 Announce Type: replace-cross Abstract: Over-the-air computation (AirComp) broadly exploits the superposition property of wireless multiple-access channels (MACs) to compute function

OverLay++: Dense-Overlap Layout-to-Image Generation Dataset

arXiv:2610.09071v1 Announce Type: new Abstract: Layout-to-Image generation has made substantial progress in spatial and object-level control. However, existing methods still struggle with complex scen

PairAudit: Guiding Human Review with Graph Tokens under Distribution Shift

arXiv:2610.10260v1 Announce Type: new Abstract: Intrusion detectors can confidently misclassify attacks that were not seen during training. Human review can correct these errors, but only a limited nu

PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modeling

arXiv:2610.10370v1 Announce Type: cross Abstract: As smart glasses and lightweight MR devices become increasingly practical, input remains a key challenge. The bare palm is an always-available, tactil

PanoPed: Beyond Bounding Boxes for Sim-to-Real Panoramic Pedestrian Tracking

arXiv:2610.08826v1 Announce Type: new Abstract: Full-sphere panoramic cameras let fixed monitoring systems and mobile robots track people in every direction, but a planar bounding box does not fully d

Tutorials

PARC-Loc: Text-to-Point-Cloud Localization with Partial Assignment and Relational Consistency

arXiv:2610.09761v1 Announce Type: cross Abstract: Text-to-point-cloud localization estimates a position in a city-scale 3D map from descriptions of surrounding objects. Existing coarse-to-fine methods

tutorialsarxiv-cs-cl

Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data

arXiv:2610.09717v1 Announce Type: cross Abstract: Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantit

PatchBench: Measuring Collateral Damage in Activation Patching

model-releasesarxiv-cs-cl

arXiv:2610.10276v1 Announce Type: cross Abstract: An LLM safety patch can pass a benchmark while still being a poor repair. This risk is especially acute for jailbreak repairs, where the goal is to co

Pathwise Information Certificates for Decentralized Adaptive Sensing

arXiv:2610.10362v1 Announce Type: cross Abstract: We study decentralized adaptive sensing, where multiple agents choose measurements from evolving local beliefs while exchanging information over a com

safetyarxiv-cs-lg

Patient, Place, Prior (P^3): What Counts as Personalization in Medical World Models?

arXiv:2610.09194v1 Announce Type: new Abstract: Longitudinal models forecast how a patient's imaging state evolves, but accuracy does not show whether the patient's observed trajectory drives the pred

Industry

Pay-per-inference for AI agents: How BlockRun and Incarna use Amazon Bedrock AgentCore payments

Amazon Bedrock AgentCore payments gives AI agents a managed way to pay for services on demand, with spending limits enforced by the infrastructure. See how Incarna's agents pay BlockRun for model infe

industryaws-ml-blog

PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians

arXiv:2610.09576v1 Announce Type: cross Abstract: Nonadiabatic molecular dynamics provides mechanistic insight into light-driven processes and informs the design of molecules and materials for solar e

Perceptually Aligned Evaluation of Style Transfer

arXiv:2610.10003v1 Announce Type: new Abstract: Style transfer lacks a reliable evaluation standard: ground truth is inherently ill-defined, and existing automatic metrics often fail to reflect human

Performance at What Cost? A Sustainability-Aware Performance Index for Cell and Nucleus Instance Segmentation

arXiv:2610.10324v1 Announce Type: new Abstract: Pretrained models for cell and nuclear instance segmentation differ substantially in architecture, pretraining data and objectives, parameter count, inf

Persistence Paradox in Dynamic Science: Evidence from the Deep Learning Revolution

arXiv:2506.22729v3 Announce Type: replace-cross Abstract: Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its contextu

safetyarxiv-cs-lg

Personalize at Test Time: Learning User Preferences for Image Generation

arXiv:2610.09015v1 Announce Type: new Abstract: Diffusion models can generate high-quality images, yet aligning their outputs with individual user preferences remains challenging. A key bottleneck is

safetyarxiv-cs-cv

Phoneme-Guided Initialization for LLM-based Speech Recognition

arXiv:2610.08994v1 Announce Type: cross Abstract: Speech large language models (speech LLMs) perform well on automatic speech recognition (ASR) when sufficient paired speech-text data is available, bu

researcharxiv-cs-cl

PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs

model-releasesarxiv-cs-cl

arXiv:2610.10455v1 Announce Type: new Abstract: Hallucinated information can propagate through multi-stage LLM systems and become part of the context for subsequent reasoning. Existing studies of post

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

PhysEvo: Astra Can Act, Let It

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

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