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One Intervention per Component is Enough: Towards Identifiability in Linear Stochastic Dynamics from Steady State

arXiv:2609.19955v1 Announce Type: new Abstract: We study the problem of recovering the parameters of a multivariate Ornstein-Uhlenbeck (OU) process from steady-state observational and interventional d

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Online Adaptation of Visual Odometry Frontends with Image-Conditioned Reinforcement Learning

DGX agent

arXiv:2603.21785v2 Announce Type: replace Abstract: Visual odometry (VO) frontends are typically tuned offline by domain experts on pre-recorded datasets and then deployed with fixed hyperparameters.

model-releasesarxiv-cs-cv

18 Sept 2026

Online Adaptive Kernel Mixing for Gaussian Process Decision Making

arXiv:2609.19891v1 Announce Type: new Abstract: Gaussian Processes (GPs) are widely used as surrogates for black-box functions in sequential decision-making problems such as Bayesian optimization (BO)

Online Supervised Dimension Reduction with Random Features: Diagnostics and Computational Trade-offs

arXiv:2609.20454v1 Announce Type: cross Abstract: Accurate optimization of a supervised spectral objective need not produce an accurate population subspace or a better predictive representation. We in

Open ultrasound foundation model for robust segmentation and clinical measurement across heterogeneous settings

arXiv:2609.19230v1 Announce Type: new Abstract: Ultrasound is the most widely deployed imaging modality worldwide, yet clinical AI remains fragmented into narrow single-task models that fail when devi

Agents

Open-vocabulary 3D object detection with promptable segmentation

arXiv:2609.19358v1 Announce Type: new Abstract: Three-dimensional object detection for autonomous driving is dominated by detectors trained on large corpora of human-annotated 3D boxes. Such a detecto

agentsarxiv-cs-cv

Opinion Dynamics-based Coalition Formation for Federated Learning in Heterogeneous IoT Systems

arXiv:2609.19695v1 Announce Type: new Abstract: Federated learning (FL) enables privacy-preserving, on-device training across heterogeneous Internet-of-Things (IoT) deployments such as smart-city wate

local-aiarxiv-cs-lg

OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher

arXiv:2609.20756v1 Announce Type: cross Abstract: As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as aut

safetyarxiv-cs-cv

Optimal Transport Metric Learning for Feature Alignment in Partially Supervised Segmentation

arXiv:2609.19176v1 Announce Type: cross Abstract: Multi-organ segmentation is often challenged by partially annotated datasets and domain shifts across different imaging sources. To address these limi

safetyarxiv-cs-ai

Tutorials

Out-of-Sample Embedding with Proximity Data: Projection versus Restricted Reconstruction

arXiv:2505.06756v2 Announce Type: replace-cross Abstract: The problem of using proximity (similarity or dissimilarity) data for the purpose of 'adding a point to a vector diagram' was first studied by

tutorialsarxiv-cs-lg

Ownership in AI-Assisted Everyday Tasks

arXiv:2609.20658v1 Announce Type: new Abstract: When does work done with AI still feel like ours? As AI becomes woven into everyday tasks, we must examine what happens to our sense of ownership and co

PAA: The Probabilistic Allen Algebra: A Generative and Complete Probabilistic Extension of Allen's Interval Relations

arXiv:2609.20634v1 Announce Type: new Abstract: Allen's interval algebra is a qualitative calculus for temporal relations, but its thirteen base relations are crisp predicates over exact interval boun

PACE: Precise AI Cinematic Expression: A Typed Specification for Script-Grounded Previsualization and Geometric Conformance

arXiv:2609.19853v1 Announce Type: cross Abstract: Between a screenplay and a film sits a planning problem that is spatial first: who stands where, and what a camera sees from where it stands. An image

PaGNet: A Panel-Aware GBDT--Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting

arXiv:2609.20177v1 Announce Type: new Abstract: Forecasting corporate tax avoidance proxies from firm--year panel data is challenging because predictive signals are distributed across short firm histo

Paint-Anything: Unified Any-Color Control for Image Generation and Editing

arXiv:2609.20816v1 Announce Type: cross Abstract: Professional design requires any-color control: the ability to specify an object's target color with any 24-bit hex value for image generation and edi

PAPC: Platform Mediation for Privacy-Propagation Externalities in AI-Mediated Workflows

arXiv:2609.19226v1 Announce Type: cross Abstract: AI-mediated platforms coordinate work through LLM agents acting for different principals. In these workflows, privacy loss can be created before a fin

safetyarxiv-cs-ai

Tutorials

Parallelism, critical windows, and separations among diffusion language models

arXiv:2609.20539v1 Announce Type: new Abstract: A popular selling point of diffusion large language models (dLLMs) is their capacity for parallelism: the ability to generate sequences of text far more

tutorialsarxiv-cs-lg

PART: Learning 3D Part Assembly and Retrieval with Transformers

arXiv:2609.19872v1 Announce Type: new Abstract: 3D assembly is fundamental to modern manufacturing and digital content creation. In this paper, we present PART, a unified transformer-based framework f

applicationsarxiv-cs-cv

ParticleSplat: Self-supervised Object-centric Latent Particle Splatting

arXiv:2609.19463v1 Announce Type: new Abstract: We present ParticleSplat, a self-supervised object-centric learning method that decomposes scenes into a set of latent ''particles'' representing semant

applicationsarxiv-cs-cv

Past, Future, All at Once: Mitigating Stability-Plasticity Dilemma via Post-hoc JANUS Rectification

arXiv:2609.19985v1 Announce Type: cross Abstract: Fine-tuning foundation models on new tasks inevitably suffer from catastrophic forgetting. While existing works attempt to mitigate this on the basis

Perception, Layout, and Validation: Calibrated Confidence for Reliable Straight-Through Processing of Financial Documents

arXiv:2609.20110v1 Announce Type: new Abstract: Straight-through processing (STP) on extracted key-value fields from financial documents without human review requires a calibrated probability together

Perceptual Refinement of an End-to-End Video Streaming Pipeline via Generative AI Layers

arXiv:2609.19215v1 Announce Type: cross Abstract: Traditional codecs treat every region of a frame alike; a generative layer can instead degrade the regions a viewer attends to least and reconstruct t

Performance of Machine Learning Classification in Sonomammogram Images using BI-RADS

arXiv:2311.08493v2 Announce Type: replace-cross Abstract: This research aims to investigate the classification accuracy of various state-of-the-art image classification models across different categor

PerSeM: Persistent Semantic Memory for Long-Horizon Open-Vocabulary UAV Mapping

arXiv:2609.19542v1 Announce Type: new Abstract: Open-vocabulary segmentation enables rich semantic perception for UAVs, but frame-wise predictions can remain temporally inconsistent across repeated ob

PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents

model-releasesarxiv-cs-cl

arXiv:2605.13481v2 Announce Type: replace Abstract: We introduce PersonalAI 2.0 (PAI-2), a novel framework designed to enhance LLM-based systems through integration of external knowledge graphs (KGs).

Personalising a Cross-User Surface Electromyography Encoder Under a Small Calibration Budget

arXiv:2609.20296v1 Announce Type: new Abstract: A myoelectric interface needs calibration from the user before it will function. Earlier work has treated calibration as a quantity, but has not asked t

Personalized Federated Hierarchical Gaussian Processes for Privacy-Preserving Modeling of Heterogeneous Distributed Systems

arXiv:2609.19337v1 Announce Type: new Abstract: We present Personalized Federated Hierarchical Gaussian Processes (pFedHGP) for probabilistic regression and classification when data are distributed ac

local-aiarxiv-cs-lg

Tutorials

Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education

arXiv:2411.19304v2 Announce Type: replace-cross Abstract: Context: Machine Learning (ML) significantly impacts Software Engineering (SE), but studies mainly focus on practitioners, neglecting research

tutorialsarxiv-cs-lg

Perturbing the Phase: Analyzing Adversarial Robustness of Complex-Valued Neural Networks

arXiv:2602.06577v2 Announce Type: replace-cross Abstract: Complex-valued neural networks (CVNNs) are rising in popularity for all kinds of applications. To safely use CVNNs in practice, analyzing thei

PetriBench: Benchmarking LLM Reasoning over Dynamic State Spaces

arXiv:2609.19883v1 Announce Type: cross Abstract: Characterizing LLM reasoning remains an open challenge, as many existing benchmarks isolate specific reasoning skills, rely on external knowledge, or

PhGS: Post-Hoc Pruning and Refinement of Single-View Feed-Forward 3D Gaussian Reconstructions

arXiv:2609.20623v1 Announce Type: new Abstract: Recent single-view feed-forward 3D Gaussian Splatting (3DGS) generation predicts a fixed number of Gaussians per camera ray, introducing severe spatial

PhyRestore: Physics-Structured Latent-Factor Restoration

arXiv:2609.19776v1 Announce Type: new Abstract: Estimating temporal soil-loss change is challenging when physically meaningful input factors are noisy or corrupted, particularly because substantial ch

Physical knowledge on historical data matters more than enforcing physical constraints on the forecast

arXiv:2609.19871v1 Announce Type: new Abstract: Time series forecasting has seen signicant advancements with the emergence of new deep learning models. However, forecasting time series in applications

applicationsarxiv-cs-ai

Physics-Informed Hemodynamic Modeling for Data-Free Prediction and Sparse-Data Assimilation

arXiv:2609.19290v1 Announce Type: cross Abstract: Clinical decision-making for coronary intervention relies mainly on angiography and fractional flow reserve (FFR). However, angiography is two-dimensi

Physics-Informed Support Vector Kernels via Green-Function Analogies and Jackson-Chebyshev Spectral Design

arXiv:2502.11153v4 Announce Type: replace-cross Abstract: Kernel selection for regression of physical observables is often heuristic. We investigate a physics-informed strategy in which functional for

local-aiarxiv-cs-ai

PIVOT: Perception-aware Independent Viewpoint Online Optimization

arXiv:2609.19510v1 Announce Type: new Abstract: A fundamental assumption in robotic perception is that the sensor's field of view (FoV) is fixed relative to the robot body. Motion-decoupled sensors, s

applicationsarxiv-cs-ro

Placement Is Free, Composition Is Not: The Latin Square as a Provably-Balanced Construction for Heterogeneous Sequence-Mixer Stacks

model-releasesarxiv-cs-lg

arXiv:2609.20269v1 Announce Type: new Abstract: Since GPT, most Transformers have repeated the same attention mechanism at every layer. Yet this design is largely a convention rather than a tested con

PointEvent: Rethinking Event-based Tiny Object Detection via Serialized Motion Evidence Accumulation

arXiv:2609.20066v1 Announce Type: cross Abstract: Event cameras offer high temporal resolution and motion sensitivity for tiny UAV detection, yet distant targets generate sparse and fragmented events

local-aiarxiv-cs-ai

Pose-aware Legged Robot Semantic Exploration with Omnidirectional Perception in Confined Unknown Environments

arXiv:2609.19460v1 Announce Type: new Abstract: Semantic exploration in confined environments requires both environment mapping and detailed observation of target objects. For ground robots, limited s

applicationsarxiv-cs-ro

Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer

arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as p

safetyarxiv-cs-ai

PosteriorBench: From Point Estimates to Posterior Matching in Evaluating Generative Inverse Solvers

model-releasesarxiv-cs-lg

arXiv:2609.20794v1 Announce Type: new Abstract: Generative models are increasingly used to solve scientific inverse problems, but existing evaluations still focus primarily on whether a method can pro

Pre-train to Gain: Robust Learning Without Clean Labels

arXiv:2511.20844v2 Announce Type: replace-cross Abstract: Training deep networks with noisy labels leads to poor generalization and degraded accuracy due to overfitting to label noise. Existing approa

applicationsarxiv-cs-ai

Precision autotuning for linear solvers via contextual bandit-based RL

arXiv:2601.00728v5 Announce Type: replace Abstract: We propose a reinforcement learning (RL) framework for xy{responsive} precision tuning for linear solvers, which can be extended to general algorith

safetyarxiv-cs-lg

Predict Before You Deploy: Offline Prediction of Quantization-Induced Task Degradation for World Action Models

arXiv:2609.19441v1 Announce Type: cross Abstract: World action models (WAMs) rely on video-generation backbones, requiring substantial memory and compute for deployment. Post-training quantization red

Prediction-Powered Smoothing and Validation for Disaggregated AI Evaluation

arXiv:2609.20758v1 Announce Type: cross Abstract: Evaluating an AI system requires disaggregated assessment, as performance varies across domains such as benchmark task types or conversation types in

PrefixBench-H100: Characterizing Prefix Reuse and Time-to-First-Token in H100 LLM Serving

model-releasesarxiv-cs-lg

arXiv:2609.19657v1 Announce Type: cross Abstract: Repeated prompt prefixes are increasingly common in LLM serving workloads, appearing in system prompts, templated retrieval-augmented generation pipel

Pretrained Medical Representations for the Practical Screening of Drug Repositioning Candidates

arXiv:2609.19865v1 Announce Type: new Abstract: Representation learning from medical code sequences in electronic health records and medical claims data has been successful in various clinical applica

applicationsarxiv-cs-lg

Printing the Underdetermined: Materializing Multi-solutionness in Figurative Paintings

arXiv:2609.19782v1 Announce Type: cross Abstract: Figurative paintings are often approached as if they depict a single recoverable 3D scene: viewers infer depth and occlusion, and reconstruction pipel