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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

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DGX agent

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

model-releasesarxiv-cs-lg

12 May 2026

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

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

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

researcharxiv-cs-lg

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

researcharxiv-cs-lg

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

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

Tools

Replit is going to London ✈️ @posthog CEO, @james406, and @amasad are coming together for a fireside chat, hosted with our friends at @meetg…

Replit is going to London ✈️ @posthog CEO, @james406, and @amasad are coming together for a fireside chat, hosted with our friends at @meetgranola. May 21st. Save your spot ⠕ https://luma.com/amjad-ja

toolsreplit--x

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

researcharxiv-cs-lg

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

Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation

arXiv:2605.08238v1 Announce Type: cross Abstract: Cardiac magnetic resonance (CMR) segmentation underpins quantitative assessment of ventricular structure and function, yet reliable delineation remain

Responsible Benchmarking of Fairness for Automatic Speech Recognition

arXiv:2605.10615v1 Announce Type: new Abstract: Many studies have shown automatic speech processing (ASR) systems have unequal performance across speakergroups (SG's). However, the manner in which suc

ReST-KV: Robust KV Cache Eviction with Layer-wise Output Reconstruction and Spatial-Temporal Smoothing

arXiv:2605.08840v1 Announce Type: new Abstract: Large language models (LLMs) face growing challenges in efficient generative inference due to the increasing memory demands of Key-Value (KV) caches, es

researcharxiv-cs-cl

Restoration-Aligned Generative Flow Models for Blind Motion Deblurring

arXiv:2605.08854v1 Announce Type: new Abstract: Generative flow models offer powerful priors learned from large-scale natural images, but directly adapting them to restoration tasks such as motion deb

researcharxiv-cs-cv

Restoring Exploration after Post-Training: Latent Exploration Decoding for Large Reasoning Models

arXiv:2602.01698v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have recently achieved strong mathematical and code reasoning performance through Reinforcement Learning (RL) post-tra

researcharxiv-cs-cl

Agents

Results and Retrospective Analysis of the CODS 2025 AssetOpsBench Challenge

arXiv:2605.08518v1 Announce Type: new Abstract: Competition retrospectives are useful when they explain what a leaderboard measured, how hidden evaluation changed conclusions, and which design pattern

agentsarxiv-cs-ai

Rethinking Agentic Search with Pi-Serini: Is Lexical Retrieval Sufficient?

arXiv:2605.10848v1 Announce Type: cross Abstract: Does a lexical retriever suffice as large language models (LLMs) become more capable in an agentic loop? This question naturally arises when building

Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver

arXiv:2605.10122v1 Announce Type: new Abstract: Heavy-Encoder-Light-Decoder (HELD) neural routing solvers have emerged as a promising paradigm due to their broad applicability across multiple vehicle

Rethinking Entropy Minimization in Test-Time Adaptation for Autoregressive Models

arXiv:2605.08186v1 Announce Type: cross Abstract: Test-Time Adaptation (TTA) via entropy minimization (EM) has proven effective for classification tasks, yet its application to generative autoregressi

Applications

Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models

arXiv:2605.09666v1 Announce Type: cross Abstract: Multiple Sclerosis (MS) is a chronic autoimmune disease that can significantly reduce the quality of life of a patient. Existing treatment options can

applicationsarxiv-cs-ai

Rethinking Event-Based Object Dtection through Representation-Level Temporal Aggregation and Model-Level Hypergraph Reasoning

arXiv:2605.08825v1 Announce Type: new Abstract: Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, offering potential for perception under fast motion an

researcharxiv-cs-cv

Rethinking Expert Trajectory Utilization in LLM Post-training for Mathematical Reasoning

arXiv:2512.11470v2 Announce Type: replace-cross Abstract: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) dominate the post-training landscape for mathematical reasoning, yet differ funda

researcharxiv-cs-cl

Applications

Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate

arXiv:2505.19525v3 Announce Type: replace-cross Abstract: Effectively managing missing modalities is a fundamental challenge in real-world multimodal learning scenarios, where data incompleteness ofte

applicationsarxiv-cs-ai

Rethinking Loss Reweighting for Imbalance Learning as an Inverse Problem: A Neural Collapse Point of View

arXiv:2605.10047v1 Announce Type: cross Abstract: Loss reweighting is a widely used strategy for long-tailed classification, but existing reweighting strategies often rely on heuristics and rarely def

Rethinking Random Transformers as Adaptive Sequence Smoothers for Sleep Staging

arXiv:2605.09905v1 Announce Type: cross Abstract: Automatic sleep staging commonly adopts Transformers under the assumption that they learn complex long-range dependencies. We challenge this view by r

Rethinking Ratio-Based Trust Regions for Policy Optimization in Multi-Agent Reinforcement Learning

arXiv:2605.09212v1 Announce Type: new Abstract: Centralized training with decentralized execution (CTDE) is a standard framework for cooperative multi-agent policy-gradient reinforcement learning, all

Rethinking RL for LLM Reasoning: It's Sparse Policy Selection, Not Capability Learning

arXiv:2605.06241v2 Announce Type: replace Abstract: Reinforcement learning has become the standard for improving reasoning in large language models, yet evidence increasingly suggests that RL does not

Tutorials

Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation

arXiv:2502.06818v3 Announce Type: replace Abstract: Recent works modify CLIP to perform open-vocabulary semantic segmentation in a training-free manner (TF-OVSS). In vanilla CLIP, patch-wise image rep

tutorialsarxiv-cs-lg

Retrieval Mechanisms Surpass Long-Context Scaling in Time Series Forecasting

arXiv:2605.08217v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have borrowed the long context paradigm from natural language processing under the premise that feeding more histo

Retrieve-then-Steer: Online Success Memory for Test-Time Adaptation of Generative VLAs

arXiv:2605.10094v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models show strong potential for general-purpose robotic manipulation, yet their closed-loop reliability often degrades u

Revis: Sparse Latent Steering to Mitigate Object Hallucination in Large Vision-Language Models

arXiv:2602.11824v2 Announce Type: replace Abstract: Despite the advanced capabilities of Large Vision-Language Models (LVLMs), they frequently suffer from object hallucination. One reason is that visu

Revisiting Mixture Policies in Entropy-Regularized Actor-Critic

arXiv:2605.09157v1 Announce Type: cross Abstract: Mixture policies theoretically offer greater flexibility than unimodal policies in continuous action reinforcement learning, but the practical benefit

Revisiting Policy Gradients for Restricted Policy Classes: Escaping Myopic Local Optima with k-step Policy Gradients

arXiv:2605.10909v1 Announce Type: new Abstract: This work revisits standard policy gradient methods used on restricted policy classes, which are known to get stuck in suboptimal critical points. We id