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RADC: Risk-Aware Dual Caching for Vision-Language Test-Time Adaptation

arXiv:2610.06932v1 Announce Type: new Abstract: Cache-based test-time adaptation (TTA) for vision-language models is often hindered by background bias in global representations and unreliable entropy-

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RadOnc-Agent: An LLM-Orchestrated Framework for AI Workflows Across the Radiotherapy Care Pathway

DGX agent

arXiv:2610.06923v1 Announce Type: new Abstract: Artificial intelligence has advanced individual radiotherapy tasks, yet these capabilities remain separated across clinical stages, software environment

agentsarxiv-cs-ai

7 Oct 2026

RAG-PIBench: A Leakage-Aware Benchmark for Prompt-Injection Detection in Trustworthy RAG Systems

arXiv:2610.08571v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are vulnerable to prompt-injection attacks embedded in retrieved content. We introduce RAG-PIBench, a ben

RAM-Net: Linear-Time Sequence Modeling with Sparsely Addressable State

arXiv:2602.11958v2 Announce Type: replace-cross Abstract: Linear attention offers an efficient alternative to full attention with a fixed-size recurrent state. However, this state is shared by all tok

researcharxiv-cs-cl

Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty

arXiv:2610.08578v1 Announce Type: new Abstract: Transformers provide a state-of-the-art modeling framework, yet poor calibration limits their reliability in safety-critical applications. A promising d

Rapid Fredholm stabilization of the Kuramoto--Sivashinsky equation with unrestricted, spatially-varying anti-diffusion

arXiv:2610.08764v1 Announce Type: cross Abstract: We develop the first feedback design for rapid stabilization of the Kuramoto--Sivashinsky equation with a spatially varying anti-diffusion coefficient

researcharxiv-cs-lg

Rationale-Guided Policy Optimization: Learning to Reason with Adaptive Rationale Scaffolding

arXiv:2610.07342v1 Announce Type: new Abstract: On-policy reinforcement learning has become a central paradigm for improving the reasoning abilities of large language models. However, its effectivenes

RBMatch: Dual-Level Class Rebalancing for Semi-Supervised Building Footprint Extraction

arXiv:2610.07698v1 Announce Type: new Abstract: Accurate building footprint extraction from high-resolution remote sensing imagery is essential for urban planning, disaster response, and environmental

Reactive Exploration of Unknown Environments for Redundant Robots using Virtual Model Control

arXiv:2610.08110v1 Announce Type: new Abstract: The exploration of confined, occluded, and partially known spaces poses significant challenges in robotic manipulation. The overall pose of the robotic

researcharxiv-cs-ro

Reactive Task-Oriented Robot-Human Handovers via Generative Hypothesis Selection

arXiv:2610.08003v1 Announce Type: new Abstract: When humans hand each other objects, they incorporate both geometric and semantic information into this process. For example, passing a knife with the h

researcharxiv-cs-ro

Reading, Not Manipulating: Leveraging Router Logits for Multimodal Safety in MoE Vision-Language Models

model-releasesarxiv-cs-cl

arXiv:2610.07774v1 Announce Type: new Abstract: Vision-language models (VLMs) face compositional safety risks where harmful intent emerges from the interaction between visual and textual inputs. As mi

Readout Stability in Prefill-Only Decision Models:Zero-Label Prediction and Inference-Time Compute Allocation

arXiv:2610.07716v1 Announce Type: new Abstract: Prefill-only decision models inspired by the Jev model score every candidate in a menu during a single forward pass and never decode, which makes one ca

researcharxiv-cs-cl

Real-Time Generation of Game Video Commentary with Multimodal LLMs: Pause-Aware Decoding Approaches

arXiv:2603.02655v2 Announce Type: replace-cross Abstract: Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in d

Recommender system in X inadvertently profiles ideological positions of users

arXiv:2602.02624v2 Announce Type: replace-cross Abstract: Several data protection laws restrict processing that reveals political opinions, irrespective of the controller's intent. Whether recommender

Recurrent Looped Transformer

arXiv:2610.07591v1 Announce Type: cross Abstract: State tracking requires an update at every input, but the depth a Transformer applies to each token is fixed regardless of sequence length. We introdu

Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness

arXiv:2610.08621v1 Announce Type: new Abstract: Recent game design agents have made substantial progress in generating playable games. However, program correctness does not ensure an enjoyable experie

ReDex: Repairing Sim-to-Real Dexterous Policies by Finger-Level Compliant Interaction

arXiv:2610.07525v1 Announce Type: new Abstract: Dexterous manipulation policies trained in simulation often fail to transfer to the real world because of errors in contact timing and force regulation.

safetyarxiv-cs-ro

ReFold: Training-Free Reversible Inter-Turn Context Folding for Long-Horizon Agents

arXiv:2610.07863v1 Announce Type: cross Abstract: Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with st

RefRoute: Decoupling Conditioning Cost from References via Compact Residual Conditioning and Spatial Routing

arXiv:2610.07720v1 Announce Type: new Abstract: Multi-reference image generation requires preserving the appearance of multiple subjects while composing them into a coherent scene. However, existing d

ReGraph: A Computational Account of Emergent Generalization in the 'what' and 'where' Dual Visual Streams

arXiv:2610.07962v1 Announce Type: cross Abstract: Where generalization capacity--the ability to extract context-invariant relational structures--first emerges remains a central question in AI and neur

Reinforcement Learning for Hierarchical Reasoning Rewards: Minimax-Optimal Rates with Transformers

arXiv:2610.08561v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a standard tool for post-training language models on reasoning tasks, where the policy is updated by reward feedb

Reinforcement Learning with Conformal Action Sets: An Application to Sequential Recommendation

arXiv:2610.08743v1 Announce Type: cross Abstract: Sequential recommenders typically use a fixed slate size even though the number of useful alternatives changes within a session. We propose Reinforcem

Agents

Reinforcement Learning with Segment Reward Feedback under Linear Function Approximation

arXiv:2610.08271v1 Announce Type: new Abstract: Classical reinforcement learning (RL) assumes that a reward is observed for every visited state-action pair. However, in real-world applications such as

agentsarxiv-cs-lg

Local Ai

RELACE: retrospective likelihood-based action credit estimation for long-horizon language agents

arXiv:2610.07349v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) avoids a separate critic by estimating advantages from rollout groups. For multi-turn agents, however, traje

local-aiarxiv-cs-ai

RenderBench: Benchmarking Render-to-Real Video Transfer with Reconstructed Digital Twins

arXiv:2610.08684v1 Announce Type: new Abstract: Modern video models can generate realistic videos from real appearance references and proxy renders that specify scene structure, viewpoint changes, and

REPA-G: Test-Time Conditioning with Representation-Aligned Visual Features

arXiv:2602.03753v2 Announce Type: replace Abstract: While representation alignment with self-supervised models has been shown to improve diffusion model training, its potential for enhancing inference

Applications

Repair Lot Skyline: A Weighted Constraint Satisfaction Approach to Pavement Repair Optimization from Geospatial Hazard Density

arXiv:2610.06989v1 Announce Type: new Abstract: Pavement agencies must translate a spatially distributed distress inventory into a bounded, actionable repair-lot plan: accident-critical defects (potho

applicationsarxiv-cs-lg

Representation Bias, Correction Transfer, and Resolution Sensitivity in Three-Dimensional Mitochondrial Morphometry

arXiv:2610.07582v1 Announce Type: new Abstract: Quantitative imaging pipelines can produce precise but systematically different measurements of the same object. We present an empirical reliability ass

Responsible Institutional Analytics: Interpreting Bias with AI Support

arXiv:2610.07205v1 Announce Type: cross Abstract: Institutional Analytics (IA) dashboards inform decision-making in higher education, yet data limitations, constraints in analytical techniques, and mi

Rethinking Adapter Placement: A Dominant Adaptation Module Perspective

arXiv:2605.06183v2 Announce Type: replace Abstract: Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method that places trainable low-rank adapters into frozen pre-trained m

Rethinking Cross-Tokenizer On-Policy Distillation: From Alignment Coverage to Supervision Reliability

arXiv:2610.08448v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student on its own generations using teacher feedback. With different tokenizers, comparing teacher and student

Rethinking Faithfulness in LLMs: A Pairwise Context-Sensitive Perspective

model-releasesarxiv-cs-cl

arXiv:2610.07894v1 Announce Type: new Abstract: Large language models (LLMs) are expected to answer questions faithfully based on the provided context, abstaining when the context information is insuf

Rethinking Fine-Tuning: Unlocking Hidden Capabilities in Vision-Language Models

arXiv:2512.23073v2 Announce Type: replace-cross Abstract: Fine-tuning has become the dominant paradigm for adapting Vision-Language Models (VLMs), yet most approaches rely on explicit weight updates t

Rethinking Visual Provenance: Detection and Watermarking Across Direct Visual Generation and LLM-Driven Code Rendering

arXiv:2610.08137v1 Announce Type: cross Abstract: AI systems create images and videos with image/video generation models or by writing code and graphics descriptions that are then rendered. These rout

Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters

arXiv:2610.07834v1 Announce Type: new Abstract: Retrieval-augmented time-series forecasting uses the continuations of historical segments similar to the current context as references for a forecaster.

Revar3r: gauge-aware perturbation uncertainty for feed-forward 3d reconstruction

arXiv:2610.07883v1 Announce Type: new Abstract: A correctly reconstructed distant point appears uncertain even when a frozen 3D model processes equivalent inputs because its output frame rotates fract

Revisiting Numerical Forecasting Models for Language-Based Trajectory Prediction

arXiv:2610.07954v1 Announce Type: new Abstract: Language-based trajectory predictors represent coordinates as discrete tokens and learn auxiliary tasks such as destination and group reasoning. This fo

Revisiting Temporal Regularization for Smooth Control in Deep Reinforcement Learning

arXiv:2610.07910v1 Announce Type: new Abstract: Deep Reinforcement Learning policies can produce nonsmooth action oscillations that hinder deployment on physical robots. Existing architectural and pen

REViT-v2: Hierarchical Windowed Roto-reflection Equivariant ViT for Equivariant Feature Extraction

arXiv:2610.07585v1 Announce Type: new Abstract: We propose a scalable roto-reflection-group-equivariant vision transformer based on windowed group-convolutional self-attention and a hierarchical featu

researcharxiv-cs-cv

Reward-Driven Learning under Prompt-Level Differential Privacy

model-releasesarxiv-cs-lg

arXiv:2610.07212v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains a language model on problems that may themselves be confidential, and the trained model can

Risk-Sensitive Crowd Navigation with Adaptive Ellipsoidal Conformal Prediction

arXiv:2610.07474v1 Announce Type: new Abstract: Safe crowd navigation under distribution shift requires uncertainty representations that capture structured human-motion prediction errors and safety ob

safetyarxiv-cs-ro

Local Ai

RIWANav: Recursive World-Action Models with Self-Improvement for Urban Navigation

arXiv:2610.08640v1 Announce Type: new Abstract: Long-horizon urban navigation requires sequential local decisions whose errors can compound over time. Imitation learning (IL) rarely learns from failur

local-aiarxiv-cs-ro

Applications

RMRRT: Riemannian Barrier Metric RRT for Inequality-Aware Steering on Equality Manifolds

arXiv:2610.06863v1 Announce Type: new Abstract: This paper presents a motion planning framework that unifies equality and inequality constraints within a single geometric formulation for sampling-base

applicationsarxiv-cs-ro

RoboCap: A New Platform for Egocentric Robot Learning

arXiv:2610.07217v1 Announce Type: cross Abstract: Despite its promise for scaling robot learning, egocentric manipulation data is still scarce today. Collection at scale requires vertically integratin

Robust Importance Sampling for Rare Events via Constrained Gaussian Mixtures

arXiv:2610.07485v1 Announce Type: new Abstract: We study estimating rare-event probabilities I = P(g(mathbf{X}) > gamma) with mathbf{X} sim N(oldsymbol{mu}, oldsymbol{Sigma}) and general g : R^d o R.

researcharxiv-cs-lg

Applications

Robust Nonprehensile Object Transport with Quadruped Robots

arXiv:2610.07245v1 Announce Type: new Abstract: In this paper, we present a robust nonprehensile object transportation framework for quadruped robots. An uncertainty-aware trajectory optimization meth

applicationsarxiv-cs-ro

Tutorials

ROMA: LLM System for Real-World Object-Centric Multi-Sensory Active Perception

arXiv:2610.06955v1 Announce Type: cross Abstract: Humans inherently understand the physical world through an active process. When sensory evidence is insufficient to infer physical properties, we natu

tutorialsarxiv-cs-cv

RSJEV: Discriminative Remote Sensing Scene Classification with Multimodal Large Language Models

arXiv:2610.08539v1 Announce Type: new Abstract: Remote sensing scene classification is a fundamental task in Earth observation and geospatial analysis. Existing approaches mainly follow three paradigm