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Potential-Field Action Representation for Reinforcement Learning in Contact-Rich Manipulation

arXiv:2609.21609v1 Announce Type: cross Abstract: Model-free reinforcement learning can acquire contact-rich robotic manipulation skills through trial-and-error interaction, but it often requires the

safetyarxiv-cs-ai

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Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confidence Scoring and Abstention

DGX agent

arXiv:2609.22056v1 Announce Type: cross Abstract: Multi-hop retrieval failures are not uniformly distributed across queries: they cluster in structurally predictable subpopulations. We prove two resul

safetyarxiv-cs-cl

21 Sept 2026

Prediction Dynamics in Depth-Recurrent Language Models

arXiv:2609.21383v1 Announce Type: new Abstract: Depth-recurrent language models refine predictions through repeated latent updates. Why can intermediate answers agree with the endpoint while their sco

Predictive Suppression Layers for Communication-Efficient Spiking Neural Networks

arXiv:2609.21583v1 Announce Type: cross Abstract: Feedforward Spiking Neural Networks (SNNs) typically propagate every generated spike indiscriminately, disregarding whether the information is redunda

PRIME: Perception Feedback with Situational Memory Embeddings in VLA Models

arXiv:2609.22040v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models for autonomous driving operate primarily through feedforward inference across the perception--reasoning--pla

PRISM-BN: A Controlled Corpus and Benchmark for Text-to-Parameterized Bayesian Network Extraction

arXiv:2609.21673v1 Announce Type: new Abstract: Probabilistic Graphical Models (PGMs), especially Bayesian Networks (BNs), expose directed structure and probabilistic parameters, making them natural s

PrismAlign: Prior-Steered Multi-View VLM Alignment for Hallucination-Robust Table OCR

arXiv:2609.21351v1 Announce Type: new Abstract: Table extraction suffers from frequent structural errors and semantic hallucinations. We propose PrismAlign, a multi-VLM framework aligning diverse visu

safetyarxiv-cs-cv

Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses

arXiv:2402.04013v3 Announce Type: replace Abstract: Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional performance across numerous applications. However, Model Inve

researcharxiv-cs-cv

PRIVET: PRoximIty leakage detection Via Extreme value Theory

arXiv:2510.24233v2 Announce Type: replace Abstract: Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or pr

Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes

arXiv:2609.21382v1 Announce Type: new Abstract: Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability

Programming AMD XDNA NPUs with Open-source Compiler Tools: A FlashAttention Case Study

model-releasesarxiv-cs-lg

arXiv:2609.21264v1 Announce Type: cross Abstract: Spatial NPUs such as AMD XDNA place compute tiles beside small local memories and leave data movement between them to software. Mapping a multi-stage

Applications

Project SCOUT: Interceptor Drone for Perimeter Defense

arXiv:2609.21005v1 Announce Type: new Abstract: The rapid proliferation of unauthorized unmanned aerial vehicles (UAVs) has created a growing need for robust, jamming-resistant counter-UAV systems for

applicationsarxiv-cs-ro

ProTracer: Proprioception-Guided Failure Diagnosis in Robot Manipulation

arXiv:2609.21369v1 Announce Type: cross Abstract: This paper presents a comprehensive framework for robot manipulation failure analysis that includes binary failure detection, failure categorization,

Provably Optimal Reinforcement Learning under Safety Filtering

arXiv:2510.18082v3 Announce Type: replace Abstract: Recent advances in reinforcement learning (RL) enable its use on increasingly complex tasks, but the lack of formal safety guarantees still limits i

safetyarxiv-cs-lg

PSEE: Progressive Sensor Event Expansion for Point-Supervised Temporal Action Localization

arXiv:2609.21462v1 Announce Type: new Abstract: Temporal action localization (TAL) in wearable sensor streams identifies action classes and temporal boundaries, enabling finer-grained activity underst

local-aiarxiv-cs-cv

PSR: Predictive Sensorimotor Representation Learning for Contact-Rich Manipulation

arXiv:2609.21753v1 Announce Type: new Abstract: Contact-rich manipulation requires policies to generate precise actions by reasoning over contact forces, robot configurations, and interaction historie

Purification and Regulation: Comorbidity-Aware Multi-Label Few-Shot Learning for Medical Image Classification

arXiv:2609.21541v1 Announce Type: new Abstract: Multi-label few-shot learning (MLFSL) remains a significant challenge in medical image analysis (MIA). Current metric-based meta-learning methods face t

Quantization-Aware Kalman Estimation for Diffusion Sampling

arXiv:2609.21407v1 Announce Type: new Abstract: Quantization offers a practical path to deploying diffusion models with reduced memory and computation, but aggressive compression can cause quantized o

local-aiarxiv-cs-cv

QuranicMMLU: A Cognitively-Aware Benchmark for Evaluating Generative AI Solutions on Quranic Linguistic Knowledge

arXiv:2609.22038v1 Announce Type: new Abstract: We introduce QuranicMMLU, a benchmark for evaluating generative AI on Quranic Arabic across multiple dimensions of linguistic complexity. Existing Quran

Tutorials

RACER: Role-Aligned Competence Estimation for Human-AI Routing

arXiv:2609.21953v1 Announce Type: new Abstract: Learning to defer asks a predictive system when to act autonomously and when to defer to a human expert. Population-adaptive deferral extends this probl

tutorialsarxiv-cs-lg

RAYA: Learning Where and When to Intervene for Robot Recovery

arXiv:2609.21690v1 Announce Type: new Abstract: A robot can predict failure and still be unable to prevent it. By the time a safety mechanism reacts, the nominal plan may already have spent the contro

Hardware

RBS-Attention: Radius-Bounded Sparse Prefill for Long-Context Large Language Models

arXiv:2609.20971v1 Announce Type: new Abstract: Long-context large language model inference is increasingly limited by prefill, where dense self-attention processes the entire prompt before generation

hardwarearxiv-cs-ai

Reading Anxiety or Reading the Label? Comparing Fine-Tuned and Frontier Models for Anxiety Detection on Social Media

arXiv:2609.20847v1 Announce Type: new Abstract: Anxiety is among the most common mental health conditions, and people often write about it online well before seeking clinical help. Practitioners build

Reading Less While Writing: A Closed-Form Bandwidth Dial for Streaming Multimodal Decoders

arXiv:2609.20845v1 Announce Type: new Abstract: A decoder that turns video or audio into text conventionally consumes the entire input before emitting a word. Offline this is merely more than the task

Reconstruction of 4D Mitral Regurgitation Hemodynamics from Sparse Planar Data using Deep Operator Networks with Test-Time Adaptation

arXiv:2609.20857v1 Announce Type: cross Abstract: Quantifying mitral regurgitation severity remains limited by the assumptions of clinical flow convergence methods, while high-fidelity simulation and

RecreationWorld: Scalable and Verifiable Environments for Hybrid Computer-Use Agents

arXiv:2609.22000v1 Announce Type: new Abstract: Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Rea

Rectify, Don't Regret: On-Policy Closed-Loop Training for Multimodal Trajectory Prediction

arXiv:2603.23393v2 Announce Type: replace Abstract: Current trajectory prediction models are primarily trained in an open-loop manner, which often leads to covariate shift and compounding errors when

Recursive Language Models Generalize Out of Domain

arXiv:2609.20831v1 Announce Type: new Abstract: We study when limiting what a language model can see improves learning. We compare standard CoT, the more general learner that reads the full trace, wit

safetyarxiv-cs-cl

Tutorials

Reducing Barriers to Academic Support: Evaluating a Course-Specific RAG System for Addressing Help-Seeking Disparities in Higher Education

arXiv:2609.21600v1 Announce Type: new Abstract: Access to academic support is a key determinant of student success, yet students experience it unequally: some readily seek help from lecturers or tutor

tutorialsarxiv-cs-ai

Refine Then Fusion: Training-Free 3D Point Cloud Adaptation with Priority Refinement and Multi-Modal Knowledge Fusion

arXiv:2609.21522v1 Announce Type: new Abstract: Recent pre-trained foundation models provide rich multi-modal priors for downstream 3D vision tasks. However, the effectiveness of these representations

REFINEPPO: Learning Continuous Control Policies by Iterative Action Refinement

model-releasesarxiv-cs-lg

arXiv:2609.21108v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) has achieved strong performance across a wide range of continuous-control problems. These continuous-control policies,

Refining Ground Truth Poses in Autonomous Driving Datasets via Neural Rendering

arXiv:2504.15776v2 Announce Type: replace Abstract: Public autonomous driving datasets underpin the training and benchmarking of perception, mapping, and localization algorithms, yet residual inaccura

local-aiarxiv-cs-cv

Applications

RegKT: Interpretable and Robust Deep Knowledge Tracing With IRT-Regularizer

arXiv:2609.21791v1 Announce Type: new Abstract: As deep learning models continue to advance, knowledge tracing models have achieved higher accuracy. However, these gains come at the cost of reduced in

applicationsarxiv-cs-lg

Agents

Reinforcement learning for post-coronagraphic wavefront control

arXiv:2609.20880v1 Announce Type: cross Abstract: Direct imaging of exoplanets is limited by the extreme contrast between the star and the planets, which is mitigated using a coronagraph. However, opt

agentsarxiv-cs-ai

Reinforcement Learning under External Influence: Guarantees, Algorithms, and Sample Complexity

arXiv:2305.16056v5 Announce Type: replace-cross Abstract: In this paper, we study the problem of reinforcement learning under the influence of external events. For this, we consider Markov decision pr

safetyarxiv-cs-ai

Reliability-Centered Evaluation of Sparse Longitudinal CT Lesion-Size Forecasting with Conformal Interval Calibration and Gompertz-Inspired Regularization

model-releasesarxiv-cs-lg

arXiv:2609.21197v1 Announce Type: new Abstract: Sparse longitudinal CT follow-up limits lesion-size forecasting when only a few prior observations are available. We constructed a five-visit DLT-derive

Rethinking Human-Aligned Evaluation: An Analysis of Semantic Metrics Beyond WER

arXiv:2609.21663v1 Announce Type: new Abstract: Word Error Rate (WER), the most commonly used metric for Automatic Speech Recognition (ASR), treats every lexical deviation from the reference as equall

Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts

arXiv:2609.21844v1 Announce Type: new Abstract: Long transcripts are costly inputs for downstream NLP systems and often contain irrelevant context. We study query-conditioned topic localization: predi

Reviser: Revision-Capable Text Generation via Autoregressive Cursor Actions

arXiv:2609.20830v1 Announce Type: new Abstract: Revision-capable generation is appealing because it can insert or revise earlier content, but many non-autoregressive and edit-based approaches obtain t

Rewarding Efficient Reasoning Improves Abstention on Underspecified Tasks in Reasoning Models

arXiv:2609.20846v1 Announce Type: new Abstract: While modern large reasoning models (LRMs) excel at providing correct answers in many tasks, we provide additional evidence for the observation that the

RheoSampling: Resolving the One-Hot Dilemma in Stochastic Dynamic-Tree Speculative Decoding

arXiv:2609.21827v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting multiple tokens in parallel, with tree-based methods further improving efficiency through hie

Riemannian Neural Hamiltonian Flows: Geodesic Symplectic Transport and Interpretability

arXiv:2609.21647v1 Announce Type: new Abstract: Hamiltonian normalizing flows are attractive generative models because their phase-space maps are invertible and volume preserving, but most neural cons

local-aiarxiv-cs-lg

Riemannian Simultaneous Inference for Tangent Vector Field Regression

arXiv:2609.21910v1 Announce Type: cross Abstract: We consider nonparametric tangent vector field regression on a Riemannian manifold without boundary. Because responses at different points lie in diff

local-aiarxiv-cs-lg

Risk-Aware Occupancy for Safety-Oriented End-to-End Autonomous Driving

arXiv:2609.21470v1 Announce Type: new Abstract: Sparse representation formulates the environment perception for the end-to-end driving system as a set of discrete elements like objects and lane lines.

safetyarxiv-cs-ai

RobotEQ-Video: A Video-Centric Benchmark for Social Proactive Intelligence with World-State Taxonomy

arXiv:2609.21371v1 Announce Type: new Abstract: Social Proactive Intelligence (SPI) extends proactive assistance beyond task completeness to consider social appropriateness in diverse embodied scenari

Robotic Multiphase Interaction: Manipulating Coupled Liquid and Solid Dynamics with a World Model

arXiv:2609.21448v1 Announce Type: new Abstract: This work presents extit{Robotic Multiphase Interaction (RMI)}, a setting in which liquid enters a porous material and interacts mechanically with its d

Robotic Tele-Operation for Upper Aerodigestive Tract Microsurgery: System Design and Validation

arXiv:2601.06617v4 Announce Type: replace Abstract: Upper aerodigestive tract (UADT) treatments frequently employ transoral laser microsurgery (TLM) for procedures such as the removal of tumors or pol

researcharxiv-cs-ro

Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity

arXiv:2509.17411v2 Announce Type: replace-cross Abstract: Machine learning models optimized for average performance can perform poorly on vulnerable subpopulations. Existing approaches often rely on g

safetyarxiv-cs-lg