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Reliability Testing of Medical Model Performance under Distributed Deployment

arXiv:2609.36525v1 Announce Type: cross Abstract: Distributed inference has become an indispensable part of deploying medical models under practical latency, memory, and throughput constraints. Althou

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Research

Reliable but Design-Sensitive: Instrument Uncertainty in LLM Annotation

DGX agent

arXiv:2609.35824v1 Announce Type: new Abstract: Large language models (LLMs) can give reliable labels under one setup yet change those labels when researchers make other reasonable design choices. We

researcharxiv-cs-cl

30 Sept 2026

Reliable Parallel Decoding in Masked Diffusion Language Models

arXiv:2609.36452v1 Announce Type: cross Abstract: Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same

Relic: From Multi-Agent Collaboration to Persistent Organizational Capability

arXiv:2609.32965v2 Announce Type: replace Abstract: Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to

ReLMem: Learning Recurrent Memory for Longitudinal EHR Modeling

arXiv:2609.37587v1 Announce Type: cross Abstract: Longitudinal electronic health record (EHR) modeling requires integrating new visits with an expanding patient history. Yet the continual accumulation

ReLOBGen: Replayable Limit Order Book Message Generation

arXiv:2609.35867v1 Announce Type: new Abstract: We propose ReLOBGen, a method for generating limit order book (LOB) messages that are replayable by construction. Replayability is required for closed-l

researcharxiv-cs-lg

Agents

ReMem: Rethinking Perception and Memory in Long-Context Recommendation Agents

arXiv:2609.37311v1 Announce Type: new Abstract: Recent Recommendation Agents (RecAgents) offer a promising alternative by shifting recommendation to an active, user-side paradigm, where generative age

agentsarxiv-cs-ai

Remember What You Did: Action-History Memory with Dual-Expert Denoising for Long-Horizon Vision-Language-Action Policies

arXiv:2609.37307v1 Announce Type: new Abstract: Vision-language-action (VLA) models have driven rapid progress in robotic manipulation, demonstrating strong fine-grained control and promising performa

safetyarxiv-cs-ro

Render Before Reading: Visual Rendering as a Prompt Injection Defense

arXiv:2609.36121v1 Announce Type: cross Abstract: Large language models are vulnerable to prompt injection attacks, where third-party adversarial content can hijack the model's behavior. In this paper

Repetition, Not Length: Isolating the Counting Failure in Neural Text-to-Speech

arXiv:2609.36974v1 Announce Type: new Abstract: Text-to-speech models loop, truncate and lose count on text that repeats a phrase many times. We show that repetition itself is what breaks them, not th

researcharxiv-cs-cl

Replay the Curvature: Accurate and Scalable NVFP4 Quantization for Large Language Model Inference

model-releasesarxiv-cs-cl

arXiv:2609.36654v1 Announce Type: cross Abstract: Large language models make weight storage and memory traffic major inference costs, motivating low-precision formats that represent each weight with o

Replication Failure and Trivial Baselines in Road-Level Crash Prediction

model-releasesarxiv-cs-lg

arXiv:2609.35917v1 Announce Type: new Abstract: Graph neural networks are increasingly applied to road-level crash prediction, but the stability of their reported gains has received little scrutiny. W

RepoMAS: Solving Progressively Specified Tasks with Issue-Driven Multi-Agent Systems

arXiv:2609.32490v2 Announce Type: replace Abstract: LLM-based multi-agent systems (MASs) have shown strong potential for solving complex tasks, but most assume that task requirements are sufficiently

Representable but Unlearned: Encoding Rank and the Interaction-Prediction Floor

arXiv:2609.36208v1 Announce Type: cross Abstract: Input encodings can restrict which measured contrasts a predictor can jointly reproduce, even when no single contrast is forced to vanish. We compute

Representation by Design in Generation: Cross-View Class-Token Alignment in Diffusion Transformers

arXiv:2609.36348v1 Announce Type: cross Abstract: Generative and representation learning remain asymmetrically connected: semantic representations are used to improve diffusion generation, whereas the

Representation Dynamics Reveal Semantic Saliency and Similarity for Visual Token Pruning in MLLMs

arXiv:2609.36916v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) incur high inference latency from long visual token sequences. Existing pruning methods commonly use attention

researcharxiv-cs-cv

Representational and Functional Robustness to Electrode Montages in EEG Foundation Models

arXiv:2609.36288v1 Announce Type: new Abstract: EEG foundation models (EEG-FMs) are intended to generalize across different datasets by learning representations that, ideally, are invariant to dataset

researcharxiv-cs-lg

Representational Simplicity and Circuit Size Dissociate in a Threshold-Dependent Way: A Controlled Test via Adversarial Training

arXiv:2609.35890v1 Announce Type: new Abstract: Sparse-autoencoder decomposability and concentrated feature attribution are increasingly treated as evidence that a model's computation is easier to rev

Reprogramming Vision-Language Models via Structured Prompt Reparameterization

arXiv:2609.36680v1 Announce Type: new Abstract: Visual reprogramming adapts pretrained models to downstream tasks by modifying their input and output interfaces while keeping the backbone fixed. In vi

researcharxiv-cs-cv

ResComEmb: Effective and Efficient Multimodal Embedding via Residual Homogeneity Compression

arXiv:2609.37225v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have shown strong potential for universal multimodal representation learning. However, existing methods eithe

Local Ai

RESCUE: Repairing Language Model Errors to Sparse Circuits via Reinforcement Learning

arXiv:2609.36813v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong general capabilities that mechanistic interpretability has attributed to sparse computational circuits. Howe

local-aiarxiv-cs-lg

Resolving the Missing Financial Data Crisis: A Generative AI Pipeline for SEC 10-K Extraction

model-releasesarxiv-cs-cl

arXiv:2609.35864v1 Announce Type: new Abstract: SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem aff

Applications

Rethinking Multimodal Fake News Detection in the Generative AI Era

arXiv:2609.36850v1 Announce Type: new Abstract: Generative content is increasingly entering the production and dissemination of news, transforming fake news from manually fabricated or simply manipula

applicationsarxiv-cs-cl

Rethinking Reasoning Paths as Phase-Structured Trajectories

arXiv:2609.36461v1 Announce Type: new Abstract: Large language models often improve problem-solving performance by generating multi-step reasoning paths, yet how to analyze the hidden states along the

tutorialsarxiv-cs-ai

Rethinking Representations for World-Action Modeling

arXiv:2609.38163v1 Announce Type: new Abstract: World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and predi

safetyarxiv-cs-cv

Rethinking Soft Tokens for Parallel Decoding in Diffusion Language Models

arXiv:2609.37391v1 Announce Type: new Abstract: Diffusion language models (DLMs) enable parallel generation by predicting and committing multiple tokens at each denoising step, yet they can generate i

researcharxiv-cs-lg

Agents

Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation

arXiv:2609.38024v1 Announce Type: new Abstract: An agent skill is a reusable, actionable natural-language artifact that guides an agent to perform a task effectively under a given harness. Recent stud

agentsarxiv-cs-ai

Retrieval Capacity of Self-Attention Under Competition

arXiv:2609.37879v1 Announce Type: new Abstract: How many tokens from its context does a language model actually use, and what determines that number? We study this question through self-attention. Wit

researcharxiv-cs-cl

Retrieval Sensitivity to Identity Signals in Queries

arXiv:2609.36534v1 Announce Type: new Abstract: Dense retrievers decide which documents reach users and the language models that use them, yet they are typically evaluated with neutral queries. We ask

safetyarxiv-cs-cl

Retrieve, Reproduce, Reveal: Dissecting Retrieval-Augmented Software Vulnerability Detection

arXiv:2609.37669v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is increasingly used to enhance Large Language Model (LLM)-based software vulnerability detection by grounding pr

Reverberation: Learning the Latencies Before Forecasting Trajectories

arXiv:2511.11164v3 Announce Type: replace Abstract: Bridging the past to the future, connecting agents both spatially and temporally, lies at the core of the trajectory prediction task. Despite great

tutorialsarxiv-cs-cv

REVO: Rollout-Efficient Off-Policy Distillation via Variance-Guided Reuse

arXiv:2609.37500v1 Announce Type: new Abstract: On-policy distillation (OPD) trains language models using dense token-level teacher supervision on student-generated trajectories. However, its reliance

safetyarxiv-cs-lg

Reward-rate Policy Gradient for Efficient Machine Learning Engineering Agents

arXiv:2609.36393v1 Announce Type: cross Abstract: Traditional reinforcement learning (RL) techniques focus on maximizing expected cumulative reward, where each action assumes to take a constant unit o

ReWorld-Track: A Recursive Event World Model for Language-Guided Multi-Camera Tracking

arXiv:2609.36677v1 Announce Type: new Abstract: Language-guided multi-camera tracking must preserve a target identity across unobserved gaps, where similar candidates and uncertain returns can make ea

tutorialsarxiv-cs-cv

Rho: A Foundation for Efficiently Adaptable VLA Models

arXiv:2609.38164v1 Announce Type: new Abstract: General-purpose physical AI models must combine broad visual and linguistic capabilities with precise control across robot embodiments and efficient ada

safetyarxiv-cs-ro

Riemannian Splat Regression Models for Learning Time Fields on Arbitrary Riemannian Manifolds

arXiv:2609.36561v1 Announce Type: new Abstract: Motion planning on arbitrary Riemannian manifolds is an important and difficult problem that frustrates typical planning methods for Euclidean spaces. I

tutorialsarxiv-cs-ro

Right Words, Wrong Moment: A Clinician-Grounded Analysis of Distress in 19,930 Conversations between Young People and ChatGPT

arXiv:2609.35953v1 Announce Type: new Abstract: Young people increasingly turn to General-Purpose Conversational Agents (GPCAs), such as ChatGPT, in moments of distress. We examine young adults' (ages

Risk-Averse Online POMDP Planning via CVaR of the Immediate Cost with Performance Guarantees

arXiv:2609.35874v1 Announce Type: new Abstract: Online POMDP planners optimize the expected cumulative cost, which can mask dangerous states when the belief places significant mass on high-cost states

Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning

arXiv:2609.37554v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructio

Risk-Controlled Selective LLM Answering by Pricing Label-Free Checks

arXiv:2609.37493v1 Announce Type: cross Abstract: Serving an answer from a large language model requires deciding when to abstain, yet a verifier's ranking accuracy alone does not determine the error

RL-PaO: Prediction as Action in Decision Making under Uncertainty

arXiv:2609.37065v1 Announce Type: new Abstract: Decision-making under uncertainty often relies on predicted parameters, yet accurate prediction does not necessarily lead to good operational decisions.

safetyarxiv-cs-lg

RLTL;DR: Self-improvement by Internalizing Self-generated Feedback

arXiv:2609.37633v1 Announce Type: cross Abstract: The common paradigm of reinforcement learning with verifiable rewards (RLVR) is to let agents make multiple attempts at a task, and optimize towards t

Local Ai

RLX: A Unified Multi-Backend Tensor Compiler and Distributed Runtime in Rust

arXiv:2609.37916v1 Announce Type: cross Abstract: Production machine learning (ML) stacks often split graph compilation and kernel execution across different layers and languages, making backend behav

local-aiarxiv-cs-ai

RNA Design via Conditioned Flow Matching and Finite-Policy Reinforcement Learning

arXiv:2609.36885v1 Announce Type: cross Abstract: RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or

safetyarxiv-cs-lg

Agents

Robo-Saber: Generating and Simulating Virtual Reality Players

arXiv:2602.18319v2 Announce Type: replace-cross Abstract: We present the first motion generation system for playtesting virtual reality (VR) games. Our player model generates VR headset and handheld c

agentsarxiv-cs-ai

RoboChrono: A Real Robot Benchmark for Streaming Task Understanding

model-releasesarxiv-cs-ro

arXiv:2609.36605v1 Announce Type: new Abstract: Understanding ongoing robot manipulation requires models to interpret visual observations in relation to interaction history and task progress. We intro

Applications

RoboFin3D: A Sim-to-Real Platform for Robotic Surface Finishing

arXiv:2609.37560v1 Announce Type: new Abstract: Grinding and sanding are fundamental processes in industrial robotic surface finishing. However, physical trials are expensive and consume workpieces, m

applicationsarxiv-cs-ro

RoboHarn-Evo: Evolving Hierarchical Physical Knowledge for Self-Improving Robotic Manipulation

model-releasesarxiv-cs-ro

arXiv:2609.37583v1 Announce Type: new Abstract: Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interaction