All entries

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

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Model Releases

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

DGX agent

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

model-releasesarxiv-cs-lg

12 May 2026

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

safetyarxiv-cs-cl

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

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

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

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

safetyarxiv-cs-lg

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

safetyarxiv-cs-cl

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

model-releasesarxiv-cs-lg

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

safetyarxiv-cs-lg

Revisiting the syntax of imperatives in Yemeni Arabic: An Agree across phases approach

arXiv:2605.08447v1 Announce Type: new Abstract: This article revisits the syntax of imperatives in Yemeni Arabic proposing an Agree acros phases (AAP) approach. I argue that the AAP approach successfu

Revitalizing the Beginning: Avoiding Storage Dependency for Model Merging in Continual Learning

arXiv:2605.08311v1 Announce Type: cross Abstract: Model merging provides a compelling paradigm for integrating specialized expertise into a unified multi-task model, a goal that aligns naturally with

safetyarxiv-cs-cv

Reward Auditor: Inference on Reward Modeling Suitability in Real-World Perturbed Scenarios

arXiv:2512.00920v4 Announce Type: replace Abstract: Reliable reward models (RMs) are critical for ensuring the safe alignment of large language models (LLMs). However, current RM evaluation methods fo

safetyarxiv-cs-cl

Reward-Conditioned Reinforcement Learning

arXiv:2603.05066v2 Announce Type: replace Abstract: Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their abilit

safetyarxiv-cs-lg

RewardHarness: Self-Evolving Agentic Post-Training

arXiv:2605.08703v1 Announce Type: new Abstract: Evaluating instruction-guided image edits requires rewards that reflect subtle human preferences, yet current reward models typically depend on large-sc

RigidFormer: Learning Rigid Dynamics using Transformers

arXiv:2605.09196v1 Announce Type: cross Abstract: Learning-based simulation of multi-object rigid-body dynamics remains difficult because contact is discontinuous and errors compound over long horizon

Applications

RIR-Former: Coordinate-Guided Transformer for Continuous Reconstruction of Room Impulse Responses

arXiv:2602.01861v3 Announce Type: replace-cross Abstract: Room impulse responses (RIRs) are essential for many acoustic signal processing tasks, yet measuring them densely across space is often imprac

applicationsarxiv-cs-lg

Industry

Rivian’s AI-powered voice assistant is ready to roll

Rivian's AI-powered voice assistant is rolling out today to the company's vehicle fleet. The assistant will be available through a software update to all compatible Rivian Gen 1 and Gen 2 vehicle owne

industrythe-verge-ai

RL Fine-Tuning Heals OOD Forgetting in SFT

arXiv:2509.12235v3 Announce Type: replace-cross Abstract: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) is a standard post-training recipe for improving Large Language Models (L

Rly looking forward to this Deep Agents workshop at @LangChain Interrupt Learning everything I can about Context Management and Deep Agents …

Rly looking forward to this Deep Agents workshop at @LangChain Interrupt Learning everything I can about Context Management and Deep Agents are the best examples of OSS best practices Harness to Fleet

agentsharrison-chase--x

RoboMemArena: A Comprehensive and Challenging Robotic Memory Benchmark

model-releasesarxiv-cs-ro

arXiv:2605.10921v1 Announce Type: new Abstract: Memory is a critical component of robotic intelligence, as robots must rely on past observations and actions to accomplish long-horizon tasks in partial

Robust Building Damage Detection in Cross-Disaster Settings Using Domain Adaptation

arXiv:2603.14694v2 Announce Type: replace-cross Abstract: Rapid structural damage assessment from remote sensing imagery is essential for timely disaster response. Within human-machine systems (HMS) f

Robust Multi-Agent LLMs under Byzantine Faults

arXiv:2605.09076v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly collaborate over peer-to-peer networks to improve their reliability. However, these same interactions c

agentsarxiv-cs-ai

Robust Probabilistic Shielding for Safe Offline Reinforcement Learning

arXiv:2605.10293v1 Announce Type: cross Abstract: In offline reinforcement learning (RL), we learn policies from fixed datasets without environment interaction. The major challenges are to provide gua

Robust Remote Reinforcement Learning over Unreliable Communication Channels using Homomorphic State Encoding

arXiv:2508.07722v2 Announce Type: replace Abstract: Traditional Reinforcement Learning (RL) frameworks generally assume that the agent perceives the state of the underlying Markov process instantaneou

agentsarxiv-cs-lg

Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning

model-releasesarxiv-cs-lg

arXiv:2605.08616v1 Announce Type: new Abstract: Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and