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RefCon: Iterative Refinement and Contrastive Memory Extraction for Context-Evolving Agent

arXiv:2609.39143v1 Announce Type: new Abstract: Long-horizon agent interactions generate useful but noisy experience, and retraining models to absorb it is expensive. Context-evolving agents therefore

agentsarxiv-cs-ai

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Referential Uncertainty in Human--AI Collaboration

DGX agent

arXiv:2609.39518v1 Announce Type: new Abstract: Effective human-AI collaboration requires partners to establish references through interaction, which becomes fragile when descriptions are ambiguous, s

model-releasesarxiv-cs-ai

1 Oct 2026

Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners

arXiv:2609.38971v1 Announce Type: cross Abstract: Embodied vision-language models (VLMs) are increasingly deployed as high-level planners for robots because they generalize across diverse environments

ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images

arXiv:2609.38680v1 Announce Type: cross Abstract: Text-to-image diffusion models are personalized to a subject by DreamBooth fine-tuning on a handful of its images. Increasingly, these images come fro

Reinforcement Learning-Guided Graph Transformations for SpTRSV Optimization

arXiv:2609.40159v1 Announce Type: cross Abstract: Sparse triangular solve (SpTRSV) is a fundamental kernel in numerous scientific and engineering applications. However, the data dependencies inherent

agentsarxiv-cs-lg

Reinforcement Learning with Complex (valued) Memories

arXiv:2609.38598v1 Announce Type: new Abstract: Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from ob

safetyarxiv-cs-lg

Reinforcing Multimodal Reasoning via Token-Level Perception-Grounded Advantage Estimation

arXiv:2609.39168v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing

researcharxiv-cs-ai

Applications

ReLaG: A Scalable Framework Generalizing Random Splits to Data with Latent Relations

arXiv:2609.38538v1 Announce Type: cross Abstract: Random splitting can yield non-independent train--test subsets when a dataset contains related samples, as is common in certain applications such as b

applicationsarxiv-cs-ai

Reliability-Aware Checkpoint Selection for Domain Generalization

arXiv:2609.39934v1 Announce Type: cross Abstract: Checkpoint selection in domain generalization often relies on source-validation accuracy, yet the selected checkpoint need not provide reliable probab

researcharxiv-cs-cv

Removing Timing Shortcuts Improves Non-Invasive Brain-to-Text

arXiv:2609.40359v1 Announce Type: new Abstract: We find that major reported improvements in decoding words from non-invasive brain recordings are largely reproducible without any brain data. In the in

Rep2Skill: Representation-Guided Skill Self-Evolution for LLM Agents

arXiv:2609.39149v1 Announce Type: new Abstract: Textual skills enable large language model (LLM) based agents to accumulate reusable procedural knowledge without updating model parameters. Yet existin

local-aiarxiv-cs-ai

Replay on Demand: An Emergent Curriculum for Balancing Adaptation and Forgetting in Continued Pretraining

arXiv:2609.40089v1 Announce Type: new Abstract: Continued pretraining enables language models to adapt to new domains and knowledge, but often at the cost of forgetting previously acquired capabilitie

Representation Risk in Pretrained Image Encoders

arXiv:2609.35470v1 Announce Type: cross Abstract: Applied researchers increasingly convert images into features with pretrained encoders, then use those features in a downstream prediction model. The

ReSAIL: Mitigating Collapse in Iterative Agent Self-Distillation

arXiv:2609.39306v1 Announce Type: cross Abstract: Iterative self-distillation enables LLM agents to learn from successive deployments, offering a path toward recursive self-improvement (RSI). Yet our

agentsarxiv-cs-ai

ResARC: Residual-Aware AutoRegressive Coding for Ultra-Low Bitrate Image Compression

arXiv:2609.39451v1 Announce Type: new Abstract: Progressive autoregressive image codecs provide an appealing paradigm for generative compression by quantizing continuous latents into discrete tokens,

ReSCENE: Server-Side Replay for Structural Mitigation of Catastrophic Forgetting in Federated Continual Learning

arXiv:2609.38833v1 Announce Type: new Abstract: Federated continual learning must integrate new tasks over time without losing earlier-task knowledge. Most existing methods attach an anti-forgetting m

local-aiarxiv-cs-lg

Reserve-Aware Contrast Certificates for Conservative Bandits with Uncertain Baselines

arXiv:2609.39106v1 Announce Type: new Abstract: Conservative bandits must improve an incumbent policy without exhausting a prescribed performance budget. When the incumbent is uncertain, separately bo

Residual Wrench Certification and Margin-Aware Control Synthesis for Aerial Physical Interaction

arXiv:2609.38707v1 Announce Type: new Abstract: We develop a task-relative framework for certifying residual wrench authority after hover and contact loading in multirotors with bounded actuators. Usi

researcharxiv-cs-ro

ResidualKV: Residual-Based KV Cache Compression for Efficient Long-Context Inference

model-releasesarxiv-cs-ai

arXiv:2602.08005v2 Announce Type: replace-cross Abstract: Efficient long-context inference faces two coupled bottlenecks: KV-cache memory grows linearly with context length, while attention computatio

Tutorials

Residuals Are Not Enough: Limits of Physics-Informed Pre-Training for Scientific Foundation Models

arXiv:2503.19081v2 Announce Type: replace Abstract: Scientific foundation models (SciFMs) aim to learn generalizable representations of physical systems governed by partial differential equations (PDE

tutorialsarxiv-cs-lg

Restoring without Forgetting: Filter-Level Continual Image Restoration via Parameter-Space Integrated Gradients

arXiv:2609.38591v1 Announce Type: new Abstract: Adapting image restoration models to a stream of new tasks without revisiting past data remains challenging due to catastrophic forgetting. In this work

RESUME: Recurrent State Updates from Motion and Residual Signals for Efficient Video Language Modeling

arXiv:2609.39563v1 Announce Type: new Abstract: Existing video language models encode sampled RGB frames independently, so a long video must either exhaust the token budget or drop the changes between

researcharxiv-cs-cv

ReTaCo: Residual-Target Control for On-Policy Distillation

arXiv:2609.39275v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own generated prefixes with token-level teacher feedback, but transmitting or storing the teacher's

safetyarxiv-cs-lg

Retargeting Motions to Diverse Skeletons via Learnable Flattening

arXiv:2609.38578v1 Announce Type: cross Abstract: Cross-structural motion retargeting aims to transfer motion between different skeletal topologies. Despite recent progress, existing state-of-the-art

local-aiarxiv-cs-ai

Rethinking Generative Image Compression at Extremely Low Bitrates

arXiv:2609.39315v1 Announce Type: new Abstract: Generative image compression produces visually plausible reconstructions at low bitrates, yet their behavior as the rate approaches zero remains largely

Rethinking Legibility in Social Robot Hallway Navigation: Impact of Intent Representation and Human Distraction

arXiv:2609.40158v1 Announce Type: new Abstract: We focus on legible robot motion generation in social navigation settings. Legibility in human-robot interaction (HRI) is often described as the propert

Rethinking Multi-Image Re-Representation in Multi-Image Understanding

model-releasesarxiv-cs-ai

arXiv:2609.39363v1 Announce Type: cross Abstract: Multi-image understanding requires MLLMs not only to recognise the content of individual images, but also to organise visual evidence distributed acro

RetroGEF: Dynamic Graph Edit Flow for Single-Step Retrosynthesis

arXiv:2609.38484v1 Announce Type: new Abstract: Retrosynthesis enables the discovery of viable synthetic routes to target molecules. It plays a central role in modern drug discovery and materials desi

Revisiting On-policy Adversarial Black-Box Distillation: Calibrating Groupwise Reward Geometry for Effective Advantage Construction

arXiv:2609.39757v1 Announce Type: cross Abstract: Black-box distillation is a practical route for transferring capabilities from API-accessible large language models that expose only text outputs into

safetyarxiv-cs-cv

Revisiting scaling laws for reward optimization

arXiv:2609.38526v1 Announce Type: cross Abstract: Scaling laws for optimization against reward models in AI alignment have pinned down how performance depends on optimization effort---measured by a KL

ReWAM: Reciprocal World Action Models for Interactive Autonomous Driving

arXiv:2609.39245v1 Announce Type: new Abstract: In interactive scenarios, an autonomous driving system is required to generate ego actions under the influence of other agents' behaviors. Existing Worl

RiboUnmix: Learning Shared Translational Dynamics from Biased and Noisy Ribo-seq Measurements

arXiv:2609.39644v1 Announce Type: new Abstract: Ribosome profiling (Ribo-seq) measures ribosome distributions along mRNAs, but observed occupancy profiles also contain experiment-specific distortions

Richard: Voice-First Mobile Interaction for Persistent Tasks

arXiv:2609.39976v1 Announce Type: cross Abstract: Mobile terminals need to provide application and network services while supporting users' control over their attention. We explore voice-first interac

agentsarxiv-cs-ai

Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Prediction

arXiv:2609.39773v1 Announce Type: new Abstract: Crystal structure governs material properties, making crystal structure prediction (CSP) a fundamental problem in materials science. Generative models a

Right Answer, Wrong Mechanism: Detecting Pernicious Divergence in Causal Interventions

arXiv:2609.39243v1 Announce Type: new Abstract: Causal interventions such as activation patching and distributed alignment search (DAS) are the main tool for making mechanistic claims about neural net

Right Answers, Costly Models: The Efficiency Gap in LLM-based Optimization Modeling

model-releasesarxiv-cs-ai

arXiv:2609.38884v1 Announce Type: cross Abstract: Optimization modeling formulates real-world decision problems as mathematical programs that solvers can use to find optimal decisions. Large language

Right-Wing Rock or Just Rock? A Computational Linguistic Analysis of Frei.Wild

arXiv:2609.39460v1 Announce Type: new Abstract: Rechtsrock is a subgenre of rock music that spreads right-wing ideology, often instrumentalized to recruit adolescents into the radical scene. Monitorin

researcharxiv-cs-cl

Applications

Rising Multi-Armed Bandits with Known Horizons

arXiv:2602.10727v3 Announce Type: replace Abstract: Rising Multi-Armed Bandits (RMABs) model sequential decision problems where each arm's expected reward improves with repeated pulls. In such problem

applicationsarxiv-cs-lg

Risk-Aware Adaptive Evaluation: Finding High-Impact Failures Under Limited Budgets

arXiv:2609.38914v1 Announce Type: new Abstract: Evaluating interactive agents is expensive. Agent behavior is stochastic, so reliability must be measured over repeated trials, but failures are rare an

RL-Guided PAC-NMPC for Probabilistically-Safe Perception-Based Navigation in Unknown Environments

arXiv:2609.39854v1 Announce Type: new Abstract: In this paper, we present an approach for combining stochastic nonlinear model predictive control (SNMPC) and reinforcement learning (RL) to enable prob

RoboAssist: Interactive Human-Humanoid Planning for Long-Horizon Surgical Assistance

arXiv:2609.39384v1 Announce Type: new Abstract: Long-horizon surgical assistance requires humanoid robots to coordinate with evolving human activities while maintaining safety across planning and exec

RoboCoach: World Models as Active Coaches for Compositional Robot Skills

arXiv:2609.39685v1 Announce Type: cross Abstract: Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstration

Robust and Learned Online Matching in Growing Trees

arXiv:2609.40077v1 Announce Type: cross Abstract: We study irrevocable maximum-cardinality matching in trees revealed by successive leaf attachments, with a known horizon and an exogenous growth law t

Tutorials

Robust Budget Pacing with a Single Sample

arXiv:2302.02006v2 Announce Type: replace Abstract: Major Internet advertising platforms offer budget pacing tools as a standard service for advertisers to manage their ad campaigns. Given the inheren

tutorialsarxiv-cs-lg

Robust LassoNet: Enhancing Feature Selection in Neural Networks via Robust Loss Functions

arXiv:2609.38263v1 Announce Type: cross Abstract: Feature selection in neural networks remains a challenging problem, particularly in the presence of noisy or contaminated data. LassoNet is a recent a

Robust Risk-Sensitive Reinforcement Learning from Corrupted Human Feedback

arXiv:2609.38938v1 Announce Type: new Abstract: Reinforcement learning with human feedback (RLHF) learns from human comparisons, which can be corrupted or deliberately manipulated. This paper studies

Applications

Robust Transfer Learning for Paper ECG Recognition

arXiv:2609.39581v1 Announce Type: cross Abstract: Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts, and label availability. We introduce RobECG-CL,

applicationsarxiv-cs-ai

Robust Wake-Up Word Detection by Two-stage Multi-resolution Ensembles

arXiv:2310.11379v2 Announce Type: replace-cross Abstract: Voice-based interfaces rely on a wake-up word mechanism to initiate communication with devices. However, achieving a robust, energy-efficient,

local-aiarxiv-cs-cl