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Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models

arXiv:2610.16851v1 Announce Type: new Abstract: Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data. Existing approaches ofte

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RAGE: A Tightly Coupled Radar-Aided Grip Estimator For Autonomous Race Cars

DGX agent

arXiv:2604.02892v2 Announce Type: replace Abstract: Real-time estimation of vehicle-tire-road friction is critical for allowing autonomous race cars to safely and effectively operate at their physical

agentsarxiv-cs-ro

14 Oct 2026

RAIN: Robotic Task Generalization with Region-Aware Interaction Networks

arXiv:2610.14039v1 Announce Type: cross Abstract: Existing vision-language-action (VLA) models benefit from large-scale vision-language pretraining, but tend to memorize training trajectories, limitin

Random Feature Pruning for Multi-Class Classification

arXiv:2610.15101v1 Announce Type: new Abstract: Weighted sums of random kitchen sinks (W-RKS) is a commonly used feature extraction technique for supervised learning tasks. A major drawback of this me

Random Fourier Features in Near-Linear Time

arXiv:2610.14751v1 Announce Type: new Abstract: Random Fourier features approximate a shift-invariant kernel in R^d, such as the Gaussian kernel exp(-frac12|x-y|_2^2), using independent samples from i

Random Probing for Structural Self-Interactions in Graph Neural Networks

arXiv:2610.18164v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) fundamentally lack the ability to capture structural properties defined by node self-interactions, such as cycle counts, su

RAR: Rate-Adaptive Rollout for Time-Consistent Interactive World Models

arXiv:2610.16302v1 Announce Type: new Abstract: Video-based interactive world models aim to enable real-time, user-controllable environments. Yet most existing systems are trained and rolled out at a

Rarity Determines Minimax Rates of Semantic Entropy From Weak Pairwise Judgments With a Known Channel

arXiv:2610.17971v1 Announce Type: cross Abstract: The semantic entropy of a language model is the entropy of the meanings of its sampled answers. The meanings are never observed, and the entropy is es

RCStat: A Statistical Framework for using Relative Contextualization in Transformers

arXiv:2506.19549v2 Announce Type: replace-cross Abstract: Identifying which tokens and activations truly influence model predictions is critical for both the efficiency and interpretability of large a

RDM: Recurrent Diffusion Model for Human Motion Generation

arXiv:2406.07169v3 Announce Type: replace Abstract: Human motion generation is a challenging task due to its high dimensionality and the difficulty of generating fine-grained motions. Diffusion method

[Re] Boosting the Visual Interpretability of CLIP via Adversarial Fine-Tuning

arXiv:2610.14832v1 Announce Type: new Abstract: This paper presents a reproducibility study of ``Boosting the Visual Interpretability of CLIP via Adversarial Fine-Tuning'', published at ICLR 2025, whi

Reach-Avoid Certificates for Contact-based Stochastic Hybrid Systems

arXiv:2610.17308v1 Announce Type: cross Abstract: This work studies certified reach-avoid control of stochastic hybrid systems in which a stochastic differential equation governs continuous flow and a

Local Ai

Reachy Remembers: Giving a Robot a Local Semantic Memory with Qdrant Edge

Reachy Mini is an open-source robot from Pollen Robotics and Hugging Face, run by a Raspberry Pi with 4 GB of RAM. We wanted it to do what a good host does: greet you by name the second time you walk

local-aiqdrant

Readout Scaling Shapes Sparse Feature Recovery

arXiv:2610.18462v1 Announce Type: new Abstract: Readout scaling changes what a sparse autoencoder (SAE) learns, but the benefit of removing it depends on the data. We use readout-canonicalized decisio

Real-Time Traffic Density Estimation: A Hybrid Computer Vision and Predictive Machine Learning Pipeline

arXiv:2610.14326v1 Announce Type: new Abstract: Traffic density estimation from camera feeds requires connecting visual perception with short-horizon forecasting. We present a two-stage pipeline that

RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work

arXiv:2610.14397v1 Announce Type: new Abstract: Augmenting worker capabilities in the physical world through guidance has been a decades-long goal for AI agents. One key supporting capability is to un

ReAP: From Visual Failure Recognition to Action-Conditioned Risk Prediction for Runtime Failure Monitoring

arXiv:2610.16309v1 Announce Type: new Abstract: Despite rapid progress, generalist robot policies remain brittle during execution. Reliable deployment therefore requires runtime monitoring that provid

Recalibrating Many Questions with Few Labels: Minimax Rates and Identification Limits

arXiv:2610.18488v1 Announce Type: new Abstract: Many related prediction problems, which we call questions, come with few labels each. What information can be shared across them, and what is actually n

Recasting DiT for Extreme Image Compression with Hierarchical Visual Priors

arXiv:2610.18607v1 Announce Type: new Abstract: Pretrained diffusion models provide powerful generative priors for extreme image compression, but their stochastic, iterative generation processes are n

Recovering from Pose-Selection Failures in Repeated Visual Localization

arXiv:2610.16409v1 Announce Type: new Abstract: Repetitive structures can give an incorrect pose candidate more inlier support than an accurate one, causing visual localization to select it. History-G

Recovering Low-Bit Failures in Vision-Language-Action Policies via Quantization-Conditioned Reinforcement Learning

arXiv:2610.15675v1 Announce Type: cross Abstract: Quantization lowers deployment costs of vision-language-action (VLA) policies but can compromise task completion. Across five VLA models and 4,050 rea

Recurrent In-Context Learning for Tabular Foundation Models

arXiv:2610.14998v1 Announce Type: new Abstract: Tabular foundation models adapt to new datasets through in-context learning (ICL), and, like most transformers, they tie computational depth to the numb

Recursive self-improvement is one of the most discussed ideas in AI right now. RSI-Anything measures it in code repair, GUI agents, lab plan…

Recursive self-improvement is one of the most discussed ideas in AI right now. RSI-Anything measures it in code repair, GUI agents, lab planning, and chemistry. They find that the lab-planning score m

safetydair-ai--x

Red-Teaming of Vision-Language-Action Models via Quality Diversity Environment Generation

arXiv:2610.14415v1 Announce Type: new Abstract: A long-standing goal in robotics is to develop generalist robots that can perform tasks across a wide range of settings, flexibly specified with images

Redesigning Regularization for Effective Policy Smoothing

arXiv:2606.13169v2 Announce Type: replace Abstract: This paper proposes a novel regularization design to effectively smooth policy functions in reinforcement learning. While regularization that enhanc

ReDiS: Reasoning-Diagnosis Self-Evolution for Multimodal Reasoning in Remote Sensing

arXiv:2610.18388v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential for remote sensing understanding, yet reliable reasoning remains challenging when t

Reducing Qwen training time by 6x on AI Runtime

The post explains that high GPU utilisation does not guarantee fast training of large language models. It identifies two common bottlenecks—missing GPU kernels and excessive waste in the data‑input pi

model-releasesdatabricks

REEL: Learning to Retrieve, Evaluate, and Evolve Visual Memory for Any Long-Horizon Multi-Shot Video Generation

arXiv:2610.16632v1 Announce Type: new Abstract: Memory-based long-video generation aims to combine user control with long-range visual consistency. We organize this process into three functional compo

Tutorials

ReEnvision: Future-Guided Selective Action Repair for Long-Horizon Manipulation

arXiv:2610.17977v1 Announce Type: new Abstract: Long-horizon robotic manipulation requires robust execution over sequences of interdependent actions. Existing methods typically handle execution deviat

tutorialsarxiv-cs-ro

Reference-Efficient Multimodal Bitstream-Corrupted Video Recovery via Depth-Conditioned Adaptation

arXiv:2610.15806v1 Announce Type: new Abstract: Recovering visually faithful content from damaged video bitstreams is important for reliable multimedia communication and storage. Existing methods expl

Reference Wording Shapes Retrieval of Robot Task Instructions

model-releasesarxiv-cs-ro

arXiv:2610.17549v1 Announce Type: cross Abstract: Identifying a robot task from a short instruction is sensitive to which descriptions are available for retrieval. We investigate this effect in the na

Hardware

Refine Where It Matters: Contact-Adaptive Meshing for Deformable Object Manipulation

arXiv:2610.14142v1 Announce Type: new Abstract: Deformable-object manipulation requires accurate modeling of highly localized contact while maintaining simulation speeds suitable for interactive use.

hardwarearxiv-cs-ro

Refined Grid Sampling for Efficient Implicit Neural Representations of Large-Scale Scientific Data

arXiv:2610.14205v1 Announce Type: new Abstract: Implicit neural representations (INRs) offer a powerful framework for modeling high-dimensional physical fields, but their training remains inefficient

Reflected Proximal Dynamics for Accelerated High-Accuracy Log-Concave Sampling

arXiv:2610.16248v1 Announce Type: new Abstract: We study sampling from pi(x)proptoe^{-V(x)} on R^d, where V is alpha-strongly convex and eta-smooth with condition number kappa=eta/alpha. For this prob

REFLEX: A Learned On-Board Reflex for Robust Autonomous Collision Recovery of Soft Quadrotors

arXiv:2610.15867v1 Announce Type: new Abstract: Collision-resilient airframes allow quadrotors to survive impacts, but do not ensure autonomous post-collision recovery. Innovation gating in stock onbo

ReForesight: Occlusion-Robust Re-Identification and Future Occupancy Forecasting for Dynamic Semantic Mapping

model-releasesarxiv-cs-ro

arXiv:2610.17279v1 Announce Type: new Abstract: Robots sharing walkways need to know where people will be, yet semantic occupancy maps show only where they are. We present ReForesight, an open-source

Refuses in Chat, Complies with Arms: LLM Refusal Does Not Survive Robot Control

model-releasesarxiv-cs-ro

arXiv:2610.14043v1 Announce Type: cross Abstract: Frontier language models reliably refuse to explain how to cause physical harm. We ask whether this refusal survives when the same models control a ro

Regime-Aware Router Learning for Multi-Domain Medical Image Generation with Diffusion Experts

arXiv:2610.16082v1 Announce Type: new Abstract: Heterogeneous medical image data motivate domain-specialized diffusion experts, but expert specialization alone does not determine how their predictions

Region Aware Squeeze-and-Excitation Networks for Image Segmentation

arXiv:2610.15079v1 Announce Type: new Abstract: Squeeze-and-Excitation (SE) modules improve convolutional networks by modeling channel-wise dependencies, but global average pooling collapses spatial s

Registration is a Representation Problem: Solving Whole-Body PET/CT Registration

arXiv:2610.18804v1 Announce Type: new Abstract: Raw image intensities are a poor surrogate for similarity across images in correspondence problems. Further, while auxiliary segmentation labels can pro

Reinforcement Learning Based Heterogeneous Task Allocation Under Spatio-Temporal Environmental Uncertainty for Multi-Robot Teams

arXiv:2610.15237v1 Announce Type: new Abstract: Autonomous Surface Vehicles (ASVs) have been increasingly deployed to perform different kinds of maritime tasks in recent years, which motivates the nee

Reinforcement Learning for Large-Scale Lifelong Multi-Agent Path Finding with Shared Spatial Fields

arXiv:2610.18186v1 Announce Type: new Abstract: Large-scale applications of lifelong multi-agent path finding (LMAPF) call for computationally inexpensive movement decisions under changing traffic con

safetyarxiv-cs-lg

Reinforcement Learning from Real-Robot Interaction without High-Quality State Estimates

arXiv:2610.14042v1 Announce Type: new Abstract: A longstanding goal for robot reinforcement learning (RL) is that robots learn from their own physical experience, as humans and animals do. A key obsta

Reinforcement Learning In Partially Verifiable Environments for Colon Cancer Treatment Planning

arXiv:2610.17426v1 Announce Type: new Abstract: Clinical decision-making is a sequential process of information collection, clinical inference, and forecasting under partial observability, often witho

Reinforcement Learning with Hawkes-Structured Networks for Market Making

arXiv:2610.16524v1 Announce Type: new Abstract: Market making under realistic order-flow dynamics is challenging due to the clustered, self-exciting nature of order arrivals, which are naturally model

safetyarxiv-cs-lg

RELAGRAM: Recursive Latent Graph Model for the Capacitated Vehicle Routing Problem

arXiv:2610.15106v1 Announce Type: new Abstract: Latent recursion repeatedly processes internal representations through the same neural network. Recent work has shown this approach to be effective on s

Relational Learning as World Modeling

arXiv:2610.16763v1 Announce Type: new Abstract: Relational learning typically predicts from a database state, but decision making asks a different question: how will that state change under an action?

RELAY: Selective Self-Distillation with Adaptive Hand-off for Rubric-Based Reinforcement Learning

arXiv:2610.17623v1 Announce Type: new Abstract: Rubric-based reinforcement learning grades a response against many written criteria but collapses them into one reward, which discards the detail the ru