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Projected Riemannian Gradient Descent for the Bures-Wasserstein Barycenter: Dimension-Independent Linear Convergence at Unit Step Size

arXiv:2609.03762v1 Announce Type: new Abstract: The computation of the Bures-Wasserstein (BW) barycenter of an ensemble of positive definite matrices arises throughout machine learning, optimal transp

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Prospective Coding Improves Learning in Deep Continuous-Time Recurrent Networks

DGX agent

arXiv:2609.04134v1 Announce Type: new Abstract: Temporal integration gives continuous-time recurrent networks memory, but in deep stacks it also delays bottom-up signals and attenuates top-down errors

model-releasesarxiv-cs-lg

4 Sept 2026

Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States

arXiv:2609.04196v1 Announce Type: new Abstract: We propose Puffin-World, a unified multimodal architecture that integrates physical understanding, spatial simulation, and 3D world generation and recon

Local Ai

Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning

arXiv:2609.03851v1 Announce Type: new Abstract: Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentiality in distributed machine learning. However

local-aiarxiv-cs-lg

Hardware

QLAUN: A Research-Oriented, Robust, Agile, Modular, and Affordable Torque-Controlled Quadruped Robot

arXiv:2609.03623v1 Announce Type: new Abstract: QLAUN Bot (Quad-Legged Adaptive Unmanned Navigator Robot) is a torque-controlled quadruped robot that is research-oriented, cost-effective, and aimed at

hardwarearxiv-cs-ro

Quantum Implicit Neural Representations for Novel View Synthesis

arXiv:2601.05250v2 Announce Type: replace Abstract: Quantum implicit neural representations have recently emerged as compact function approximators for continuous visual signals. In parallel, Neural R

R^{2}Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAG

arXiv:2609.02894v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has become a prevailing paradigm for enhancing Large Language Models (LLMs) with non-parametric knowledge. Vanilla

researcharxiv-cs-cl

R2S-Eval: Robot Evaluation with Real-to-Sim Calibration via Vision-Language Models

arXiv:2609.03276v1 Announce Type: new Abstract: Evaluating robot manipulation policies is becoming increasingly important as generalist models, particularly vision-language-action (VLA) models, are de

safetyarxiv-cs-ro

RACE-AIMC: Selective Inference for Heterogeneous Analog In-Memory Accelerators at the Edge

arXiv:2609.03149v1 Announce Type: cross Abstract: Analog in-memory computing (AIMC) speeds up neural-network inference by doing the arithmetic directly inside a memory array, instead of shuttling weig

Random Attention: Rethinking KV Cache Eviction for Efficient Reasoning

arXiv:2609.03430v1 Announce Type: new Abstract: Large language models achieve superior performance on tasks that require extended reasoning, but long chains of thought make the KV cache a severe memor

researcharxiv-cs-cl

RARF: Region-Aware Rectified Flows for 3D Brain MRI Inpainting

arXiv:2609.03956v1 Announce Type: cross Abstract: Medical image inpainting has the potential to improve automated brain MRI analysis by reconstructing healthy tissue within pathological regions. We in

researcharxiv-cs-ai

Local Ai

RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting

arXiv:2609.03937v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output

local-aiarxiv-cs-ai

Real-Time Shape Control of Multi-Segment Soft Robotic Arms Using Koopman Operators with Global and Local Observables

arXiv:2609.03175v1 Announce Type: new Abstract: Multi-segment soft robotic arms can continuously reconfigure their body shapes for safe interaction, but tip control alone is insufficient for constrain

researcharxiv-cs-ro

RealCADBench: Benchmarking Parametric CAD Modeling from Industrial Design Intents

model-releasesarxiv-cs-cl

arXiv:2609.03773v1 Announce Type: cross Abstract: Parametric computer-aided design (CAD) modeling is difficult to evaluate with a single metric. Existing CAD benchmarks often emphasize synthetic or CA

Applications

RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data

arXiv:2505.19030v5 Announce Type: replace Abstract: Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their expanding applications and users' growing proficienc

applicationsarxiv-cs-ai

ReCoSplat: Online Feed-Forward Gaussian Splatting via Render-and-Compare

arXiv:2603.09968v2 Announce Type: replace Abstract: Online novel view synthesis requires a model to reconstruct a scene causally from a stream of observations while keeping it renderable at every mome

RecurTrace: Adaptive Latent Reasoning with Loop-Time Memory

arXiv:2609.03379v1 Announce Type: new Abstract: Repeating a small block of middle layers increases a language model's effective inference depth without adding parameters or generating extra tokens, an

Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progression

arXiv:2609.03189v1 Announce Type: cross Abstract: This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artific

researcharxiv-cs-ai

Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL

model-releasesarxiv-cs-ai

arXiv:2609.02944v1 Announce Type: cross Abstract: Democratizing data access through natural language is a crucial goal for modern enterprises, but the practical adoption of Text-to-SQL is critically h

Relative Prime Factorization and Finite-State Presentations under Fixed Finite-Monoid Observation

arXiv:2609.03643v1 Announce Type: cross Abstract: Let LsubseteqSigma^* and fix a morphism h:Sigma^*o M into a finite monoid. We study exact factorization and canonical presentation in the relative syn

Reliable Selection of Heterogeneous Treatment Effect Estimators

arXiv:2511.18464v2 Announce Type: replace-cross Abstract: We study the problem of selecting the best heterogeneous treatment effect (HTE) estimator from a collection of candidates in settings where th

Remember and Reweight: Enhancing Multi-Agent Debate with Experience Memory and Confidence Estimation

arXiv:2609.03619v1 Announce Type: cross Abstract: Multi-agent debate (MAD) improves the reasoning capabilities of large language models by having multiple agents iteratively refine their responses thr

Rent-a-RAG: Embedding-Space Watermarks for Auditing Third-Party RAG

model-releasesarxiv-cs-cl

arXiv:2609.03749v1 Announce Type: cross Abstract: Third-party retrieval-augmented generation (RAG) marketplaces create a new auditing problem: data providers may license corpora to a RAG operator, yet

Representational alignment yields generalizable safety in language models

model-releasesarxiv-cs-ai

arXiv:2609.04022v1 Announce Type: cross Abstract: Aligning large language models (LLMs) is essential for their safe deployment. Current alignment methods mainly optimize observable responses, yet mode

Residual neural networks overcome the curse of dimensionality for semilinear heat equations

arXiv:2609.03626v1 Announce Type: cross Abstract: Rigorous results show that feedforward neural networks can overcome the curse of dimensionality in the numerical approximation of high-dimensional par

Residual Optimal Transport-Based Experts Collaboration Towards Modality-Aware Infrared-Visible Object Detection

arXiv:2609.03516v1 Announce Type: new Abstract: Infrared-visible object detection (IVOD) integrates complementary evidence from visible and infrared sensors for reliable perception in challenging scen

Resolution-Aware Experimental Design under Partial Identifiability

arXiv:2609.03686v1 Announce Type: new Abstract: Experimental design is commonly framed as choosing the experiment expected to provide the most information. Under partial identifiability however, persi

Restricted Eigenvalues Beyond Gaussian Width: Threshold Occupancy under Heavy Tails

arXiv:2609.03504v1 Announce Type: new Abstract: Restricted eigenvalue (RE) bounds govern stable recovery by norm-regularized estimators. For isotropic sub-Gaussian measurements, the benchmark sample s

Rethinking 3D Noise: Learning 3D-Aware Video Priors via Optimization-Free Morphological Perturbations

arXiv:2609.03657v1 Announce Type: new Abstract: 3D scene representations like NeRF and 3D Gaussian Splatting (3DGS) suffer severe artifacts in sparse-view settings. Recent generative 3D artifact fixer

Rethinking On-Policy Distillation of Large Language Models II: One Training Example

arXiv:2609.04172v1 Announce Type: new Abstract: On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher. Existing work has mainly studied its

Rethinking World Models for Safety-Critical Embodied Systems

arXiv:2609.03774v1 Announce Type: new Abstract: World models have progressed from compact latent dynamics to generative, controllable, and interactive simulators of embodied environments. However, hig

Revisiting Topological Graphs for Macro Action based Closed-loop Reinforcement Learning of Vision Language Navigation in Continuous Environment

arXiv:2609.03906v1 Announce Type: new Abstract: Vision-Language Navigation in Continuous Environments (VLN-CE) requires an agent to follow natural language instructions through unseen environments. Ex

safetyarxiv-cs-ro

Agents

Risk and Anomaly Identification for Distribution Network Optimal Operation Based on Reinforcement Learning and Uncertainty Quantification

arXiv:2609.03308v1 Announce Type: new Abstract: Reliable operation of modern distribution networks requires timely identification of operational risks and anomalous events under pervasive uncertainty.

agentsarxiv-cs-lg

Agents

RL-ADA: A World-Feedback Framework for Adversarially Robust Enterprise Dialogue Agents

arXiv:2609.02902v1 Announce Type: new Abstract: Deploying task-oriented dialogue agents in enterprise customer support faces a persistent annotation bottleneck: robust training requires labelled inter

agentsarxiv-cs-cl

Applications

Robot Aware Computational Design of Object Specific Passive Grippers for Additive Manufacturing

arXiv:2609.03761v1 Announce Type: new Abstract: This paper presents an end-to-end computational pipeline that converts a selected object mesh, a measured object state, and a selected six-axis robot in

applicationsarxiv-cs-ro

RoboTok: An Internet-Scale Data Engine for Human Demonstration Retrieval and Dexterous Manipulation Learning

arXiv:2609.03199v1 Announce Type: new Abstract: Robot learning increasingly depends on broad and diverse demonstrations, yet collecting robot data remains expensive and poorly suited to covering the l

Robust PAC Learning of Concurrent Stochastic Games

arXiv:2609.04189v1 Announce Type: new Abstract: We introduce the first Probably Approximately Correct (PAC) learning framework for general-sum concurrent stochastic games (CSGs) with transition uncert

Hardware

RobustSeiz: An Open-Source Framework for Benchmarking the Robustness of EEG Seizure Detection Models

arXiv:2609.04007v1 Announce Type: new Abstract: Despite strong performance on held-out electroencephalography (EEG) data, seizure detectors may fail under real-world acquisition variability, artifacts

hardwarearxiv-cs-lg

Agents

RoughSense: Lightweight Terrain-Induced Rover Vibration Prediction Using Point Clouds and IMU Feedback

arXiv:2609.03720v1 Announce Type: new Abstract: Autonomous navigation in space requires reliable terrain assessment for safe operations, especially in underground environments with limited communicati

agentsarxiv-cs-ro

Routing Is Not Enough: Diagnosing Intra-Adapter Subspace Contention in MoE+LoRA Fine-Tuning

model-releasesarxiv-cs-cl

arXiv:2609.03150v1 Announce Type: cross Abstract: Multi-domain fine-tuning often combines MoE routing with LoRA, assuming that token-level routing separates domain-specific updates. We test this assum

Tutorials

RuleMem: Active Rule Memory for Long-Term Conversational Agents

arXiv:2609.03915v1 Announce Type: new Abstract: Question answering agents in long-term conversations must reason over massive, temporally dispersed dialogue histories. However, existing memory mechani

tutorialsarxiv-cs-cl

SafeRestore: Detector-Relative Risk Certificates for Selective Industrial Image Restoration

arXiv:2609.03475v1 Announce Type: new Abstract: Industrial inspection pipelines often restore a measured image before a detector acts on it, yet restoration can suppress detector-supported defect stru

SafeRI: Recognition and Intervention for Token-Level Safety Intervention in Large Vision Language Models

arXiv:2609.03544v1 Announce Type: new Abstract: Existing safety alignment methods for vision-language models usually modify the model behavior globally: once the safety parameters are trained or loade

Hardware

Scal3R: Learning Efficient Multi-Relative Pose Query for Scalable Online 3D Reconstruction

arXiv:2609.04201v1 Announce Type: new Abstract: Online 3D reconstruction models perform poorly on long videos. This happens because regressing poses relative to a fixed first-frame anchor forces extra

hardwarearxiv-cs-cv

Scaling Bimanual Household Manipulation from 1,500 hours of Demonstrations to On-Policy Corrections

model-releasesarxiv-cs-ro

arXiv:2609.03591v1 Announce Type: new Abstract: Learning generalist policies for robust bimanual manipulation is bottlenecked by the scarcity of high quality large scale human demonstration data. In t

Scaling Laws, Tabular Data and Actuarial Ratemaking Models

arXiv:2609.03106v1 Announce Type: new Abstract: Scaling laws in modern deep learning describe how held-out loss improves as model capacity, training data, and compute increase, often following power-l

ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition

arXiv:2506.10226v3 Announce Type: replace-cross Abstract: Synthetic data generation is increasingly used in machine learning for training and data augmentation. Yet, current strategies often rely on e

Local Ai

Seeing Before Synthesizing: VLM-Guided Transition Event Discovery for Weakly-Supervised Dense Video Captioning

arXiv:2609.04183v1 Announce Type: cross Abstract: Weakly-Supervised Dense Video Captioning aims to localize and describe multiple events in untrimmed videos given only an ordered set of event-level ca

local-aiarxiv-cs-ai