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Real-World Reinforcement Learning with MPC Scaffolding for Dexterous Manipulation

arXiv:2609.14878v1 Announce Type: new Abstract: Real-world reinforcement learning (RL) offers a promising route to dexterous manipulation policies that can adapt directly from physical interaction, bu

safetyarxiv-cs-ro

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Realistic Continual Learning Approach using Pre-trained Models

DGX agent

arXiv:2404.07729v2 Announce Type: replace-cross Abstract: Continual learning (CL) evaluates adaptability in learning solutions to retain knowledge. Our research addresses the challenge of catastrophic

researcharxiv-cs-cv

15 Sept 2026

Realtime-Venus: A full-duplex interaction system with asynchronous delegation

arXiv:2609.13814v1 Announce Type: new Abstract: Natural interaction in digital and physical environments requires continuous perception and timely responses. Spoken dialogue relies on acoustic and lin

Reason What Matters: Retrieval-Grounded Reasoning for Universal Multimodal Embeddings

arXiv:2609.15296v1 Announce Type: new Abstract: Universal multimodal embedding (UME) learns unified representations across modalities, enabling a single model to support diverse retrieval tasks. Recen

ReCAST: Reward Credit Assignment across Timesteps for Online Diffusion Reinforcement

arXiv:2609.13425v1 Announce Type: cross Abstract: Training diffusion models with multiple rewards requires distinguishing user preference from reward informativeness. User preference determines how mu

Reconstructing Is Not Acting: Action-Centric Latent Dynamics Modeling

arXiv:2609.15189v1 Announce Type: new Abstract: Latent action models (LAMs) learn action representations from unlabeled videos by inferring latent actions from visual transitions and reconstructing fu

Recoverability as a System Primitive for Long-Horizon AI Agents

arXiv:2609.13672v1 Announce Type: new Abstract: AI agents can be interrupted while editing files, calling tools, or carrying out multi-step tasks. Restarting repeats completed work, but continuing fro

Recurrent GraphNeural NetworkswithSet-BasedAggregation

arXiv:2609.15932v1 Announce Type: new Abstract: Recurrent GNNs iterate message passing to convergence, and their logical characterizations to date rely on multi-set aggregation, graded (counting) logi

Reducing the Output-Mode Gap in Speech Language Models via Joint-Output On-Policy Distillation

arXiv:2609.15313v1 Announce Type: cross Abstract: Autoregressive generation of interleaved text and acoustic tokens is a common approach to spoken-response generation in speech large language models.

safetyarxiv-cs-cl

Refinement-based Flow Policy Optimization

arXiv:2609.15123v1 Announce Type: cross Abstract: Flow-based policies offer an expressive representation for online reinforcement learning, but conventional flow matching requires samples drawn from t

Refusal Reads Only a Slice of What the Model Knows: Harm-Keyed Routing and Its Exceptions Across Model Families

arXiv:2609.14759v1 Announce Type: cross Abstract: Alignment applied after pretraining is shallow in a measurable way: a single direction in a model's residual stream can be edited out, and the model s

ReH-FUSE: Reliability-Aware Hierarchical Fusion of Experts for Multimodal Emotion Recognition in Conversation

arXiv:2609.13857v1 Announce Type: new Abstract: Multimodal emotion recognition in conversation (ERC) requires adapting to the instance-dependent reliability of different evidence sources. Lexical cont

Reliable Egocentric Action Anticipation via Temporal Reliability Suppression and Compositional Graph Decoding

arXiv:2609.13293v1 Announce Type: new Abstract: Wearable action anticipation systems must remain reliable despite missing frames, masking, and sensor noise, yet existing egocentric anticipation method

researcharxiv-cs-cv

ReLU Neural Network Approximation to Smooth Functional Operator: Dimensional Decay and Error Analysis

arXiv:2609.15355v1 Announce Type: cross Abstract: We study the uniform approximation of smooth scalar-valued functionals on an infinite-dimensional separable Hilbert space by deep ReLU neural networks

Tutorials

ReMemNav: Memory-Based Decision Correction and Target Verification for Zero-Shot Object Navigation

arXiv:2603.26788v3 Announce Type: replace-cross Abstract: Zero-shot object navigation requires agents to locate unseen targets in unfamiliar environments without prior maps or task-specific training.

tutorialsarxiv-cs-cv

Tutorials

Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling

arXiv:2609.15379v1 Announce Type: new Abstract: Machine learning can be crucial to help scale complex signal processing applications in scenarios such as healthcare. However, these machine learning mo

tutorialsarxiv-cs-lg

RESKILL: Explicit Failure Attribution and Structured Repair for Interactive Language Agents

model-releasesarxiv-cs-cl

arXiv:2609.15684v1 Announce Type: new Abstract: Language agents increasingly rely on reusable skills, but post-failure repair is often handled by opaque one-shot reflection: a model generates a skill

Resolution-Independent Analysis of Encoder--Decoder Operator Learning via Limiting Kernels

model-releasesarxiv-cs-lg

arXiv:2609.13798v1 Announce Type: cross Abstract: Operator learning is formulated on function spaces, but training data are typically available only through finite-dimensional representations. In enco

ResSafe: Learning Safety Filtering with Residual Reinforcement Learning for Humanoids

arXiv:2609.15988v1 Announce Type: new Abstract: Safe control of humanoid robots remains challenging due to their high-dimensional dynamics, contact-rich interactions, and sensitivity to disturbances.

safetyarxiv-cs-ro

Restore What Matters: Lessons from Joint Restoration and Recognition

arXiv:2609.13791v1 Announce Type: new Abstract: Recognition pipelines typically adopt a restore-then-recognize workflow, yet decades of experience show that generating visually pleasing images seldom

safetyarxiv-cs-cv

Rethinking Camouflage Image Generation towards a Training-Free Paradigm

arXiv:2609.14377v1 Announce Type: new Abstract: Camouflage image generation (CIG) aims to synthesize realistic camouflaged images by blending foreground objects into concealment-compatible background

Rethinking Correctness for Uncertainty Estimation in Clinical Prediction with Vision-Language Models

arXiv:2609.15180v1 Announce Type: new Abstract: Vision-language models are increasingly explored for clinical prediction from electronic health records and medical images, where identifying unreliable

Rethinking Procedural Audio Pre-training: Source Scaling and Objective Adaptation

arXiv:2609.15067v1 Announce Type: cross Abstract: Procedural audio has emerged as a viable source for transferable audio representation learning, but its design principles remain unclear.We revisit tw

researcharxiv-cs-ai

Tutorials

Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration

arXiv:2609.13982v1 Announce Type: cross Abstract: In Human-Robot Interaction, the standard approach to learn a reward model that represents human preferences for robot behavior consists of three steps

tutorialsarxiv-cs-ai

Retrieval-Guided Fine-Tuning as Noisy Estimation: Risk bounds and Architectural Analysis

arXiv:2609.14485v1 Announce Type: new Abstract: Retrieval-Guided Fine-Tuning (RAG-FT) incorporates retrieved data directly into the training objective, but the statistical consequences of noisy retrie

Reverse Spatio-Temporal Disease Progression Modelling

arXiv:2609.14590v1 Announce Type: new Abstract: Deep learning-based spatio-temporal disease progression models commonly overlook the incubation period of progressive diseases, limiting the use of thos

REVOLVE: An Automated Closed-Loop Framework for Evolving Robot Manipulation with Minimal Human Intervention

arXiv:2609.14633v1 Announce Type: new Abstract: Recent advances in data-driven robot manipulation policies have substantially improved task execution and generalization. However, real-world deployment

safetyarxiv-cs-ro

ReWeight: Leveraging Human Data for VLA Post-Training via Demonstration Retrieval and Sample Weighting

arXiv:2609.13851v1 Announce Type: cross Abstract: Post-training vision-language-action (VLA) models for specific robots and tasks requires in-domain demonstrations, yet collecting diverse robot data i

RFCLLM: Evaluating LLMs' Reasoning Ability of Network Protocol State Machines

arXiv:2609.13389v1 Announce Type: new Abstract: Mapping textual specifications into formal representations is essential for ensuring the correctness of protocol designs and implementations. LLM-genera

researcharxiv-cs-cl

Riemannian ascent--descent for nonconvex nonconcave minimax landscapes: convergence to basin saddle points and applications to distributionally robust optimization

arXiv:2609.14141v1 Announce Type: cross Abstract: We study a class of distributionally robust optimization (DRO) problems for the statistical risk problem, formulated as minimax problems over the prod

RIGOR: Rig-Informed Geometry for Omnidirectional Reconstruction

arXiv:2609.13504v1 Announce Type: new Abstract: Recent developments in feed-forward 3D reconstruction resulted in models which can recover dense scene representations and camera motion solely from an

researcharxiv-cs-cv

Applications

Robotic Servo Tracking of Moving Targets with Dynamic Imitation Constraints

arXiv:2609.14589v1 Announce Type: new Abstract: Imposing explicit trajectory constraints in robot visual servoing remains challenging. Existing tracking methods achieve fast responses by mapping visua

applicationsarxiv-cs-ro

Agents

Robust and Efficient Communication for Multi-Agent Learning

arXiv:2609.15361v1 Announce Type: cross Abstract: Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated message

agentsarxiv-cs-ai

Robust low-rank tensor completion via factorized weighted tensor schatten-p norm minimization

arXiv:2609.14307v1 Announce Type: new Abstract: Low-rank tensor factorization provides a flexible framework for completing multidimensional data from incomplete and corrupted observations. However, un

researcharxiv-cs-cv

Local Ai

Robust Multi-Model Fitting through Learning Neighbor Regions

arXiv:2609.15348v1 Announce Type: new Abstract: Multi-model fitting involves fitting multiple models accurately in a noisy environment. It is the basis for computer vision tasks such as scene reconstr

local-aiarxiv-cs-cv

Rolling Day-Wise Mortality Prediction in Critically Ill Patients With AKI on CRRT Utilizing Machine Pressure Waveforms

arXiv:2609.13524v1 Announce Type: cross Abstract: Critically ill patients with acute kidney injury (AKI) on continuous renal replacement therapy (CRRT) face high mortality, yet current risk assessment

Root-Cause Attribution Is a Search Problem: Continual Search for Long-Horizon Agent Failures

arXiv:2609.13463v1 Announce Type: new Abstract: The increasing deployment of AI agents in long-horizon tasks yields massive execution logs. Diagnosing failures within these records is crucial for reli

Rotation-Based Subspace Tracking for Robust Kernel PCA on Streaming Data

arXiv:2609.15488v1 Announce Type: new Abstract: Machine learning models process large amounts of data, and Principal Component Analysis (PCA) is a widely used technique to reduce the dimensionality of

Route, Don't Fix: Regime-Dependent Decoding Correction and a Trajectory-Gated Router for Reliable Clinical LLM Answer Selection

arXiv:2609.14825v1 Announce Type: cross Abstract: Large language models (LLMs) are often deemed unsafe for clinical question answering because of their tendency to hallucinate. Retrieval augmentation,

researcharxiv-cs-ai

ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments

arXiv:2508.13488v2 Announce Type: replace-cross Abstract: Loop closure detection is important for simultaneous localization and mapping (SLAM), which associates current observations with historical ke

Agents

RSIAgent: Autonomous Exploration for Recursive Self-improvement in New Environments

arXiv:2609.15364v1 Announce Type: new Abstract: Digital agents must often adapt to new environments whose interfaces, tools, and failure modes are not fully captured by pretrained models. We introduce

agentsarxiv-cs-ai

Rubrics as an Attack Surface: Stealthy Preference Drift in LLM Judges

arXiv:2602.13576v2 Announce Type: replace-cross Abstract: Evaluation and alignment pipelines for large language models increasingly rely on LLM-based judges, whose behavior is guided by natural-langua

Runtime-Incremental Transformer for Reinforcement-Learning-Based Adaptive Control

arXiv:2609.13545v1 Announce Type: cross Abstract: Learning-based adaptive control of robotic manipulators with non-observable friction memory has been addressed by attention- based meta-controllers wh

RxScribe Bench: A Multi-Axis Benchmark for Evaluating Vision-Language Models on Indian Outpatient Prescriptions

arXiv:2609.13280v1 Announce Type: new Abstract: Prescription transcription errors are not interchangeable. A model that fabricates a drug and a model that misreads a legible dose pose very different c

Agents

S3-Tracker: Self-Supervised Surgical Tissue Tracking With Contrastive Random Walks

arXiv:2609.14313v1 Announce Type: new Abstract: Robust point tracking in endoscopic videos is essential for computer-assisted intervention and autonomous robotic surgery, enabling continuous registrat

agentsarxiv-cs-cv

Safe Meta-Reinforcement Learning via Information Space Reachability

arXiv:2609.15915v1 Announce Type: new Abstract: Meta-reinforcement learning (meta-RL) enables agents to adapt to unseen tasks with limited experience. Despite its promise, the application of meta-RL i

Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself

arXiv:2609.13657v1 Announce Type: new Abstract: Traditional recommender systems are typically trained to predict what item users will interact with next, but not why. However, offering personalized ev

Safety Signals to Verify NetOps Agents with Action-Level Granularity

arXiv:2609.14422v1 Announce Type: new Abstract: Agentic Network Operations (NetOps) are an emerging paradigm promising to enable workload-aware, self-adjustable, and reliable autonomous networks. Whil