All entries

arXiv:2610.01072v1 Announce Type: new Abstract: Robotic manipulation of labware is difficult when transparent or reflective objects must be identified and localized. Coded planar fiducials are a pract

local-aiarxiv-cs-ro

Applications

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Query-Conditioned Articulation Estimation from a Single Image

DGX agent

arXiv:2610.01726v1 Announce Type: new Abstract: Enabling robots to estimate the kinematic parameters of articulated objects unlocks a wide range of capabilities for interaction and manipulation. The e

applicationsarxiv-cs-ro

2 Oct 2026

Query-efficient winner prediction in district-based elections

arXiv:2610.00577v1 Announce Type: cross Abstract: In a district-based election, N voters are partitioned into k districts, and each voter votes for one of m candidates. Each district elects a winner u

Query Independent Variable Rate Visual Token Coding

model-releasesarxiv-cs-cv

arXiv:2610.00204v1 Announce Type: new Abstract: Visual-token compression for vision--language models is posed almost entirely as a selection problem: decide which tokens to keep and discard the rest.

Tutorials

Questionnaire-Guided Disaggregation of Energy Appliance Use for Domestic Smart Meter Data

arXiv:2610.01297v1 Announce Type: new Abstract: Ireland's smart metering programme records electricity use at 30-minute resolution, with smart meters installed in over 80% of households as of late 202

tutorialsarxiv-cs-ai

R-GroundBench: A Diagnostic Benchmark for R-Group Groundingin Markush Molecular Editing

arXiv:2610.00700v1 Announce Type: new Abstract: Recent advances in AI for scientific discovery enable molecular understandingand design, yet reasoning over incomplete chemical representations remainsu

RACE: Residual-Aware Test-Time Adaptation for Neighbor-Rich Time-Series Foundation Model Forecasting

arXiv:2610.00405v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) perform strongly across forecasting tasks, but their per-series inference is ill-suited to neighbor-rich forecasti

Random Recursive Models

arXiv:2610.00541v1 Announce Type: cross Abstract: Recursive models create computational depth through parameter reuse, offering a parameter-efficient alternative to increasing model size. However, mos

Range-GRPO: Policy Optimization via Pairwise Relations among Reward Intervals

arXiv:2610.01548v1 Announce Type: new Abstract: As the use of large language models (LLMs) expands, post-training has become increasingly important for adapting them to downstream tasks. However, obta

RASteer: Retain-Aware Activation Steering for Concept Erasure in Diffusion Models

arXiv:2610.01969v1 Announce Type: new Abstract: Concept erasure aims to remove a target concept, such as a copyrighted style, a recognizable character, or unsafe content, from a pretrained text-to-ima

researcharxiv-cs-cv

Rate-Optimal Algorithm for Adversarial Linear CMDPs

arXiv:2610.00927v1 Announce Type: new Abstract: We study episodic adversarial linear constrained Markov decision processes (CMDPs) with unknown transitions, where both the loss and constraint function

Reachability-Informed Reinforcement Learning for Multi-Impulse Interplanetary Transfers

model-releasesarxiv-cs-lg

arXiv:2610.01344v1 Announce Type: cross Abstract: Reinforcement learning offers the prospect of a reusable sequential decision-making mechanism for spacecraft trajectory design, motivating policy inte

Reachability Is Not Generalization: Understanding Verb--Noun Decomposition in Assembly Action Recognition

arXiv:2610.00064v1 Announce Type: new Abstract: Assembly actions are compositional: they combine a manipulation with a part or tool. In deployment, systems routinely encounter novel combinations of fa

researcharxiv-cs-cv

Reactive Humanoid Multi-Contact Using Learned Stability Models

arXiv:2610.00823v1 Announce Type: new Abstract: We present a planning and control approach to reactively use hand contacts to stabilize a humanoid in low stability scenarios, where only using feet con

researcharxiv-cs-ro

Real-time Appearance-based Gaze Estimation for Open Domains

model-releasesarxiv-cs-cv

arXiv:2603.26945v2 Announce Type: replace Abstract: Appearance-based gaze estimation (AGE) has achieved remarkable performance in constrained settings, yet we reveal a significant generalization gap w

Real-Time Human-Adaptive Task Allocation for Multi-Human Multi-Robot Supervision

arXiv:2610.00897v1 Announce Type: new Abstract: We propose a human-factor-aware method of allocating robot supervision tasks to multiple human operators. In scenarios where multiple operators occasion

researcharxiv-cs-ro

Real-Time Whole-Body Safe Motion Generation for Multi-Segment Tendon-Driven Continuum Robots

arXiv:2610.00220v1 Announce Type: new Abstract: Real-time motion generation for tendon-driven continuum robots requires accurate modeling of nonuniform bending and whole-body collision avoidance. This

safetyarxiv-cs-ro

RealCompanion: Benchmarking Human Understanding from Reasoning over Longitudinal Real-World Conversations

arXiv:2610.01780v1 Announce Type: new Abstract: A companion that talks with a person for months should come to understand them. It should remember what they said, infer who they are, and know when the

Tutorials

Reason in Style: Discovering and Controlling Style in Language Models

arXiv:2610.00724v1 Announce Type: cross Abstract: Language models learn content and style jointly, making stylistic variation in their outputs difficult to identify and control. We study whether recur

tutorialsarxiv-cs-lg

Reasoning as Pattern Matching: Shared Mechanisms in Human and LLM Everyday Reasoning

arXiv:2606.13607v3 Announce Type: replace Abstract: When large language models (LLMs) fail to generalize or make content-sensitive errors in reasoning, it is often taken as evidence that LLMs are not

Local Ai

ReCast: Contract-Preserving Protection for Fixed-Interface Multimodal Reasoning

arXiv:2610.01184v1 Announce Type: cross Abstract: Remote multimodal models offer strong numerical reasoning capabilities over charts and speech, but sending private inputs risks exposing sensitive con

local-aiarxiv-cs-ai

ReCo: Response-Consistent Locomotion with Policy-Aware MPC for Legged Manipulation

model-releasesarxiv-cs-ro

arXiv:2610.01612v1 Announce Type: new Abstract: Continuous legged manipulation requires accurate end-effector tracking while the base keeps walking. Combining reinforcement learning (RL) with model pr

Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents

arXiv:2610.02204v1 Announce Type: cross Abstract: Building reliable robot capabilities across diverse tasks requires substantial human effort to develop and maintain skills, design rewards, and integr

agentsarxiv-cs-ai

Recova: Agent-Guided Failure Recovery for Autonomous Robotic Manipulation

arXiv:2610.01178v1 Announce Type: new Abstract: Manipulation failures can leave scenes in states from which a task policy cannot recover. Learning corrective behaviors requires scalable failure explor

safetyarxiv-cs-ro

Reducing Cognitive Overhead in Tool Use via Multi-Small-Agent Reinforcement Learning

arXiv:2508.08882v5 Announce Type: replace Abstract: Recent advances in multi-agent systems highlight the potential of specialized small agents that collaborate via division of labor. Existing tool-int

agentsarxiv-cs-ai

Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization

arXiv:2610.00558v1 Announce Type: new Abstract: While Mixture-of-Experts (MoE) models effectively scale model capacity through sparse activation, their deployment is often bottlenecked by prohibitive

Applications

Reformulation-Contrastive Learning for Mixed Integer Programs

arXiv:2610.00730v1 Announce Type: new Abstract: Mixed-integer linear programs (MILP) model many real-world decision problems, motivating machine-learning methods that exploit recurring structure to ac

applicationsarxiv-cs-lg

Refusal Localizes, the Damage Relocates: Safety Layers Under Few-Sample Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2610.00320v1 Announce Type: cross Abstract: Fine-tuning adapts aligned large language models (LLMs) to downstream tasks, but a few dozen harmful examples can remove their refusal of harmful requ

Region Based SLAM-Aware Exploration: Efficient and Robust Autonomous Mapping Strategy That Can Scale

arXiv:2504.10416v3 Announce Type: replace Abstract: Autonomous exploration for mapping unknown large scale environments is a fundamental challenge in robotics, with efficiency in time, stability again

agentsarxiv-cs-ro

ReHoPER: Receding-Horizon Planning for Enhanced Reasoning

arXiv:2610.00940v1 Announce Type: cross Abstract: We propose ReHoPER, an inference-only, zero-shot method that improves large language models' reasoning by generating and answering intermediate questi

Reinforcement Learning to Accelerate Primal-Dual Hybrid Gradient for Linear Programming

arXiv:2610.01546v1 Announce Type: cross Abstract: Primal-dual hybrid gradient (PDHG) methods solve large-scale linear programs (LPs) using GPU-friendly matrix-vector products and projections, but thei

RelationVGGT: Visual Geometry Transformers for 3D Spatial Relation Segmentation

arXiv:2610.00970v1 Announce Type: cross Abstract: Recent advances in 3D reconstruction have progressed from per-scene optimization to feed-forward inference, and semantic scene understanding has follo

Relative Transitions, Not Absolute Destinations: A Transfer-and-Ground Framework for Target-Trajectory-Free Human Mobility Generation

arXiv:2610.02033v1 Announce Type: new Abstract: Individual mobility trajectories support urban analysis and location-based services, yet most trajectory generators require observations from their depl

Reliability-aware short-term roll prediction for unmanned surface vehicles via multi-task learning and adaptive centralization

arXiv:2610.00996v1 Announce Type: new Abstract: Reliable roll prediction of unmanned surface vehicles (USVs) is essential for ensuring navi?gational safety and enhancing autonomous decision-making. Wh

Tutorials

RelICL: Training-free Relational Learning with Tabular Foundation Models

arXiv:2610.01725v1 Announce Type: new Abstract: Tabular foundation models achieve state-of-the-art performance on single-table tasks without any training. Recent work suggests that they are also well-

tutorialsarxiv-cs-lg

ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality

arXiv:2610.00710v1 Announce Type: new Abstract: As large language model (LLM) agents become widely adopted, they are increasingly deployed for tasks that require persistent monitoring or recurring act

agentsarxiv-cs-ai

Removing spurious minima for planar features by skip connections

arXiv:2610.01728v1 Announce Type: cross Abstract: Understanding loss landscapes is central to explaining neural-network training, yet their structure remains only partially understood even in simple m

Removing the NEEDLE in the Haystack: Backdoor Removal in LLMs via Weight Orthogonalisation

arXiv:2610.00348v1 Announce Type: cross Abstract: Backdoor attacks can be implanted in Large Language Models (LLMs) during training, causing unwanted behaviour when a trigger appears in the input. Exi

Representation Transitions Reveal Emerging Safety Risks in Multi-Turn LLM Agents

arXiv:2610.00400v1 Announce Type: cross Abstract: Multi-turn attacks on agentic systems can compose individually permissible actions into harmful outcomes, challenging defenses that assess actions or

safetyarxiv-cs-ai

Repurposing Obsolete Representations for Post-Deployment Adaptation

arXiv:2610.01453v1 Announce Type: new Abstract: Deep neural networks are increasingly deployed in long-lived systems, where task requirements may change after training. In such settings, part of the o

Reputation, Strategy, and Emotion Effects on Generative AI Cooperation: A Comparison Across Reasoning and Non-Reasoning Models

arXiv:2610.01222v1 Announce Type: new Abstract: As generative AI (Gen AI) systems take on increasingly autonomous roles in economically and socially consequential interactions, understanding their pro

ReSolve: Reusing Candidate Reasoning through Selective Generative Moderation

arXiv:2610.01140v1 Announce Type: new Abstract: Sampling multiple solutions spends computation on intermediate deductions and unfinished arguments as well as final answers. We introduce ReSolve, a tra

Resolving Mixed Single-Photon LiDAR Returns for Foreground-View and Hidden Scene Reconstruction

arXiv:2610.01206v1 Announce Type: new Abstract: Partially transmissive screens and protective covers are common in robotic inspection, but they create mixed LiDAR returns from both the foreground mate

researcharxiv-cs-cv

Applications

Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective

arXiv:2601.22434v2 Announce Type: replace-cross Abstract: Training generative machine learning models to produce synthetic tabular data has become a popular approach for enhancing privacy in data shar

applicationsarxiv-cs-lg

Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting

arXiv:2610.00873v1 Announce Type: cross Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between

Rethinking Memorization Mitigation in Diffusion Models: Reinforcing Text Conditioning

arXiv:2610.01723v1 Announce Type: new Abstract: Text-to-image diffusion models have achieved remarkable progress in image synthesis, yet can exhibit memorization by closely reproducing individual trai

safetyarxiv-cs-cv

Rethinking Probability-Based Reinforcement Learning From Posterior Concentration

arXiv:2610.01458v1 Announce Type: new Abstract: Verifier-free reinforcement learning with probability-based rewards offers a promising way to train LLMs on general reasoning tasks where external verif

safetyarxiv-cs-ai

Rethinking the Global Convergence of Softmax Policy Gradient with Linear Function Approximation: The Case of Multi-Armed Bandits

arXiv:2505.03155v2 Announce Type: replace Abstract: Policy gradient (PG) methods have played an essential role in the empirical successes of reinforcement learning. In order to handle large state-acti