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Real vs. Semi-Simulated: Rethinking Evaluation for Treatment Effect Estimation

arXiv:2605.10430v1 Announce Type: cross Abstract: Estimating heterogeneous treatment effects with machine learning has attracted substantial attention in both academic research and industrial practice

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Human DGX agent

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ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking

DGX agent

arXiv:2505.20381v4 Announce Type: replace Abstract: Referring Multi-Object Tracking (RMOT) aims to track targets specified by language instructions. However, existing RMOT paradigms heavily rely on ex

model-releasesarxiv-cs-cv

12 May 2026

REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage

arXiv:2604.01527v3 Announce Type: replace-cross Abstract: Production deployment of AI coding agents requires fast, reproducible evaluation signals. Existing industrial practices trade off speed and fi

Agents

REAP: Reinforcement-Learning End-to-End Autonomous Parking with Gaussian Splatting Simulator for Real2Sim2Real Transfer

arXiv:2605.08713v1 Announce Type: cross Abstract: In recent years, autonomous parking has made significant advances, yet parking tasks still face challenges in extreme scenarios such as mechanical and

agentsarxiv-cs-ai

Reasoning-Aware Training for Time Series Forecasting

arXiv:2605.08625v1 Announce Type: cross Abstract: Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LL

researcharxiv-cs-ai

Reasoning Compression with Mixed-Policy Distillation

arXiv:2605.08776v1 Announce Type: new Abstract: Reasoning-centric large language models (LLMs) achieve strong performance by generating intermediate reasoning trajectories, but often incur excessive t

Reasoning emerges from constrained inference manifolds in large language models

model-releasesarxiv-cs-cl

arXiv:2605.08142v1 Announce Type: cross Abstract: Reasoning in large language models is predominantly evaluated through labeled benchmarks, conflating task performance with the quality of internal inf

Reasoning Is Not Free: Robust Adaptive Cost-Efficient Routing for LLM-as-a-Judge

arXiv:2605.10805v1 Announce Type: new Abstract: Reasoning-capable large language models (LLMs) have recently been adopted as automated judges, but their benefits and costs in LLM-as-a-Judge settings r

Tutorials

Reasoning Trajectories for Socratic Debugging of Student Code: From Misconceptions to Contradictions and Updated Beliefs

arXiv:2511.00371v2 Announce Type: replace Abstract: In Socratic debugging, instructors guide students towards identifying and fixing a bug on their own, instead of providing the bug fix directly. Most

tutorialsarxiv-cs-cl

Rebellious Student: Reversing Teacher Signals for Reasoning Exploration with Self-Distilled RLVR

arXiv:2605.10781v1 Announce Type: cross Abstract: Self-distillation has emerged as a powerful framework for post-training LLMs, where a teacher conditioned on extra information guides a student withou

researcharxiv-cs-cl

Reconciling Consistency-Based Diagnosis with Actual-Causality-Based Explanations

arXiv:2605.08688v1 Announce Type: new Abstract: We establish, from the point of view of Explainable AI (XAI), connections between Consistency-Based Diagnosis (CBD), on one side, and Actual Causality a

researcharxiv-cs-ai

Reconfigurable Computing Challenge: Real-Time Graph Neural Networks for Online Event Selection in Big Science

arXiv:2605.10612v1 Announce Type: cross Abstract: Graph neural networks are increasingly adopted in trigger systems for collider experiments, where strict latency and throughput constraints render dep

Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency

arXiv:2605.08454v1 Announce Type: cross Abstract: Recovering continuous-time dynamics from discrete observations is difficult because local supervision (e.g., pointwise regression targets, derivative

Recursive Language Models

arXiv:2512.24601v3 Announce Type: replace Abstract: We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive

Reducing Annotation Burden for Femoral Cartilage Segmentation in Knee MRI via Cross-Sequence Transfer Learning

arXiv:2605.09067v1 Announce Type: new Abstract: Purpose: To develop and evaluate cross-sequence transfer learning for automatic femoral cartilage segmentation, testing bidirectional transfer between d

researcharxiv-cs-cv

Reflection Anchors for Propagation-Aware Visual Retention in Long-Chain Multimodal Reasoning

arXiv:2605.09614v1 Announce Type: new Abstract: Long chain-of-thought (CoT) reasoning improves large vision--language models, but visual information often fades during generation, limiting long-horizo

safetyarxiv-cs-cv

Reflective Prompted Policy Optimization: Trajectory-Grounded Revision and Salience Bias

arXiv:2605.08315v1 Announce Type: new Abstract: Existing LLM-based policy optimizers see only scalar rewards: that a policy scored 0.45, but not whether the agent got stuck in a loop, fell into a hole

Agents

Regime-Calibrated Fleet Repositioning with a Spatial Queue-Regret Decomposition

arXiv:2604.03883v2 Announce Type: replace-cross Abstract: Ride-hailing and autonomous mobility-on-demand operators reposition idle supply before future demand is fully observed. We study a retrieval-c

agentsarxiv-cs-ai

Region Seeding via Pre-Activation Regularization: A Geometric View of Piecewise Affine Neural Networks

arXiv:2605.06300v2 Announce Type: replace Abstract: Deep networks with continuous piecewise affine activations induce polyhedral partitions of the input space, making the number of realized affine reg

Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs

arXiv:2605.10385v1 Announce Type: cross Abstract: Guided-diffusion black-box optimization (BO) has shown strong empirical performance on structured design problems such as molecules and crystals, but

Regret Minimization in Bilateral Trade With Perturbed Markets

arXiv:2605.10475v1 Announce Type: cross Abstract: We address the problem of maximizing Gain from Trade (GFT) in repeated buyer-seller exchanges subject to global budget balance constraints. While this

REI-Bench: Can Embodied Agents Understand Vague Human Instructions in Task Planning?

arXiv:2505.10872v4 Announce Type: replace-cross Abstract: Robot task planning decomposes human instructions into executable action sequences that enable robots to complete a series of complex tasks. A

Reinforce Adjoint Matching: Scaling RL Post-Training of Diffusion and Flow-Matching Models

arXiv:2605.10759v1 Announce Type: cross Abstract: Diffusion and flow-matching models scale because pretraining is supervised regression: a clean sample is noised analytically, and a model regresses ag

researcharxiv-cs-cv

Reinforcement learning for inverse structural design and rapid laser cutting of kirigami prototypes

arXiv:2605.08098v1 Announce Type: new Abstract: Kirigami is an increasingly useful fabrication method to produce shape-programmable metamaterial structures. However, inverse design remains difficult b

Reinforcement Learning for Scalable and Trustworthy Intelligent Systems

arXiv:2605.08378v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for improving the capability of intelligent systems, but its practical deployment faces two cent

Reinforcement Learning Measurement Model

arXiv:2605.09305v1 Announce Type: cross Abstract: Interactive assessments generate sequential process data that are not well handled by conventional item response models. Existing MDP-based measuremen

Reinforcement Learning with Action Chunking

arXiv:2507.07969v4 Announce Type: replace-cross Abstract: We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward task

Reinforcing Multimodal Reasoning Against Visual Degradation

arXiv:2605.09262v1 Announce Type: cross Abstract: Reinforcement Learning has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet the resulting policies r

safetyarxiv-cs-cl

Relational reasoning and inductive bias in transformers and large language models

arXiv:2506.04289v3 Announce Type: replace Abstract: Transformer-based models have demonstrated remarkable reasoning abilities, but the mechanisms underlying relational reasoning remain poorly understo

Relational Retrieval: Leveraging Known-Novel Interactions for Generalized Category Discovery

arXiv:2605.09420v1 Announce Type: cross Abstract: In this study, we tackle Generalized Category Discovery (GCD) via a Relational Retrieval perspective, explicitly coupling labeled and unlabeled data t

Relations Are Channels: Knowledge Graph Embedding via Kraus Decompositions

arXiv:2605.10317v1 Announce Type: cross Abstract: Knowledge graph embedding (KGE) models typically represent each relation as an operator on entity embeddings. In this work, we identify three structur

Relative Kinetic Utility for Reasoning-Aware Structural Pruning in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.09008v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting symbolized a huge improvement of reasoning capabilities of Large Language Models (LLMs). However, scaling up test-tim

Relative Score Policy Optimization for Diffusion Language Models

arXiv:2605.10218v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer a promising route to parallel and efficient text generation, but improving their reasoning ability require

safetyarxiv-cs-cl

RelBench v2: A Large-Scale Benchmark and Repository for Relational Data

arXiv:2602.12606v2 Announce Type: replace Abstract: Relational deep learning (RDL) has emerged as a powerful paradigm for learning directly on relational databases by modeling entities and their relat

RelFlexformer: Efficient Attention 3D-Transformers for Integrable Relative Positional Encodings

arXiv:2605.10706v1 Announce Type: new Abstract: We present a new class of efficient attention mechanisms applying universal 3D Relative Positional Encoding (RPE) methods given by arbitrary integrable

Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems

arXiv:2512.20012v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including

ReLibra: Routing-Replay-Guided Load Balancing for MoE Training in Reinforcement Learning

arXiv:2605.08639v1 Announce Type: new Abstract: Load imbalance is a long-standing challenge in Mixture-of-Experts (MoE) training and is exacerbated in reinforcement learning (RL) for LLMs, where hot e

Local Ai

Relightable Gaussian Splatting for Virtual Production Using Image-Based Illumination

arXiv:2605.09024v1 Announce Type: new Abstract: Virtual production (VP) use LED walls to provide both background imagery and image-based lighting. While this enables on-set compositing, it couples lig

local-aiarxiv-cs-cv

Agents

Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory

arXiv:2605.10870v1 Announce Type: new Abstract: Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive crit

agentsarxiv-cs-ai

Remember to Forget: Gated Adaptive Positional Encoding

arXiv:2605.10414v1 Announce Type: new Abstract: Rotary Positional Encoding (RoPE) is widely used in modern large language models. However, when sequences are extended beyond the range seen during trai

Remix the Timbre: Diffusion-Based Style Transfer Across Polyphonic Stems

arXiv:2605.09259v1 Announce Type: cross Abstract: Timbre transfer aims to modify the timbral identity of a musical recording while preserving the original melody and rhythm. While single-instrument ti

researcharxiv-cs-ai

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction

arXiv:2605.08871v1 Announce Type: cross Abstract: Large-scale machine learning models are trained on clusters of machines that exhibit heterogeneous performance due to hardware variability, network de

ReorgGS: Equivalent Distribution Reorganization for 3D Gaussian Splatting

model-releasesarxiv-cs-cv

arXiv:2605.08739v1 Announce Type: new Abstract: A converged 3D Gaussian Splatting (3DGS) model may approximate the target scene while remaining poorly parameterized for further optimization. We identi

Repeated-Token Counting Reveals a Dissociation Between Representations and Outputs

model-releasesarxiv-cs-cl

arXiv:2605.09239v1 Announce Type: new Abstract: Large language models fail at counting repeated tokens despite strong performance on broader reasoning benchmarks. These failures are commonly attribute

ReplaySCM: A Benchmark for Executable Causal Mechanism Induction from Interventions

arXiv:2605.08197v1 Announce Type: cross Abstract: Most causal benchmarks for language models score local answers or graph structure. We introduce ReplaySCM, a 1,300 item benchmark for executable causa

RePO-VLA: Recovery-Driven Policy Optimization for Vision-Language-Action Models

arXiv:2605.09410v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models remain brittle in long-horizon, contact-rich manipulation because success-only imitation provides little supervisi

Representative Action Selection for Large Action Space Bandit Families

arXiv:2505.18269v5 Announce Type: replace Abstract: We study the problem of selecting a subset from a large action space shared by a family of bandits. In many natural situations, while the nominal se

Research on Security Enhancement Methods for Adversarial Robust Large Language Model Intelligent Agents for Medical Decision-Making Tasks

arXiv:2605.08257v1 Announce Type: cross Abstract: Motivated by the challenge to improve the adversarial robustness, security, and trust of medical decision making intelligent agents, this study develo