All entries

Receding-Horizon Pushing with Composable Object-Centric Policies

arXiv:2609.23439v1 Announce Type: new Abstract: Non-prehensile manipulation is practical for relocating large, heavy, or geometrically ungraspable objects. Yet, long-horizon pushing of arbitrarily-sha

safetyarxiv-cs-ro

Content type

All Blog X Post Paper YouTube Reddit GitHub

Recognition, Simulation, and Refusal: A Contamination-Aware Study of Classic Psychological Effects in LLM Agents

DGX agent

arXiv:2609.22090v1 Announce Type: new Abstract: An LLM producing the response pattern associated with a human psychological effect is not the same claim as the LLM possessing that bias. We present Psy

model-releasesarxiv-cs-cl

22 Sept 2026

Agents

Recommended if you work on agent memory. Retrieval is the hardest memory problem in most harnesses because agents keep pulling stale context…

Recommended if you work on agent memory. Retrieval is the hardest memory problem in most harnesses because agents keep pulling stale context. AML tests this with a coding track of 150 software tasks,

agentsdair-ai--x

Local Ai

Reconstructed holograms and explanation-aware evaluation for low-cost computational pollen analysis in veterinary cytology

arXiv:2609.23017v1 Announce Type: new Abstract: Automated pollen analysis supports veterinary cytology, but brightfield microscopy is costlier and more complex than lens-less digital in-line holograph

local-aiarxiv-cs-cv

RefAM: Attention Magnets for Zero-Shot Referral Segmentation

arXiv:2509.22650v3 Announce Type: replace Abstract: Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models,

Reflective Policy Optimization

arXiv:2406.03678v2 Announce Type: replace Abstract: On-policy reinforcement learning methods, like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), often demand extensiv

Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation

arXiv:2501.08547v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have been widely adopted for their ability to compute expressive node representations in graph datasets. However,

Hardware

RegVGGT: Sustainable Visual Geometry Grounding for Streaming via Regulated Memory

arXiv:2609.23286v1 Announce Type: new Abstract: 3D reconstruction from a lengthy video stream input poses a dilemma for feed-forward reconstruction models (FFRMs), that a whole-stream inference contex

hardwarearxiv-cs-cv

Reinforcement Learning in Operational Research: A Technical Review and Practical Roadmap

arXiv:2609.24750v1 Announce Type: cross Abstract: The growing demand for real-time, data-driven decision-making in complex and dynamic systems is placing increasing pressure on traditional Operational

Reinforcement Learning Inspired Black-box Adversarial Attacks for Computer Vision

arXiv:2609.24249v1 Announce Type: cross Abstract: Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small pertur

safetyarxiv-cs-cv

Reinforcement Learning under State and Outcome Uncertainty: A Foundational Distributional Perspective

arXiv:2609.24103v1 Announce Type: new Abstract: In many real-world planning tasks, agents must tackle uncertainty about the environment's state and variability in the outcomes of any chosen policy. We

Reinforcement learning with an expectile-based objective

arXiv:2602.09300v2 Announce Type: replace Abstract: We consider the policy evaluation and control in a finite horizon reinforcement learning (RL) setting under an expectile-based objective. First, we

Agents

Relationally Grounded Latent World Models for Autonomous Driving

arXiv:2609.24626v1 Announce Type: new Abstract: Latent world models learn predictive representations for autonomous driving, but the relational semantics these states preserve often remain implicit. W

agentsarxiv-cs-cv

Agents

ReliCAD: From Uncertain LLM Generation to Reliable Parametric CAD Modeling

arXiv:2609.22325v1 Announce Type: new Abstract: Large language models have shown considerable potential for natural-language-driven parametric CAD modeling. However, a fundamental contradiction exists

agentsarxiv-cs-ro

Relightable 3D Avatar Reconstruction with Semantic-Adaptive Motion-Illumination Responses

arXiv:2609.24158v1 Announce Type: new Abstract: Reconstructing expressive and relightable 3D head avatars from monocular videos remains challenging in computer vision, as it requires accurate modeling

Replay-Gated Neural Execution: Decoupling Persistent Behavioral Specifications from Neural Realizations in Frozen Language Models

arXiv:2609.22243v1 Announce Type: new Abstract: Input-conditioned neural interventions raise a runtime question: what persists when one behavioral specification admits multiple actions whose validity

Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents

arXiv:2601.15322v3 Announce Type: replace-cross Abstract: Tool-using agents can repeat a final decision while changing their recorded execution. We introduce the Determinism-Faithfulness Assurance Har

Replicating the Geometry of Emotion Representations in a Base Open-Weights Model

arXiv:2609.22208v1 Announce Type: new Abstract: Sofroniew et al. (2026) report that emotion concepts in Claude Sonnet 4.5 are represented as vectors whose geometry mirrors human affect psychology. We

Replication Without Persistence in Hosted LLMs: Measurement Sensitivity in Action-Time Belief Evaluation

arXiv:2609.22478v1 Announce Type: cross Abstract: Behavioural evaluations of hosted language models can vary because the evaluated service, the measurement instrument, or both differ across runs. We s

Representation-guided in-context learning for medical image interpretation with multimodal large language models

arXiv:2609.24057v1 Announce Type: cross Abstract: Medical image interpretation is central to diagnosis and care, yet adapting general-purpose multimodal large language models (MLLMs) often requires re

Residual Primitive Fitting of 3D Shapes with SuperFrusta

arXiv:2512.09201v2 Announce Type: replace-cross Abstract: We introduce a framework for converting 3D shapes into compact and editable assemblies of analytic primitives, directly addressing the persist

RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent

arXiv:2502.07977v3 Announce Type: replace Abstract: Empirical risk minimization (ERM) is a cornerstone of modern machine learning. This paper focuses on the man-in-the-middle (MITM) attack, wherein ad

Resist, Update, Reject: Preference Optimization Installs a Prior-Dependent Reliability Switch

arXiv:2609.22359v1 Announce Type: new Abstract: An aligned model asked to hold its answer against a manipulative source must still update on a reliable one and reject an unreliable one: resistance, re

Resolution-Flexible Decoding for Hybrid Neural Video Representations

model-releasesarxiv-cs-cv

arXiv:2609.23555v1 Announce Type: cross Abstract: Neural video representations (NVRs) represent videos using neural network parameters and, in hybrid formulations, frame-wise latent embeddings. Althou

ReSTI: A Source-Grounded Audit and Repair of STI-Bench

arXiv:2609.24727v1 Announce Type: new Abstract: Spatial--temporal benchmarks are valid only when their questions, source annotations, and answer options identify the same physical quantity. We audit S

safetyarxiv-cs-cv

Rethinking Class Imbalance for Single-Cell Foundation Models: A Systematic Benchmark Across Architectures and Long-Tail Loss Functions

arXiv:2609.23325v1 Announce Type: new Abstract: Single-cell foundation models (scGPT, scBERT, Geneformer) achieve cell-type classification accuracy up to 97.5% in our experiments, yet this aggregate a

Rethinking Diffusion Segmentation: When Does It Rely on Its Noisy State, and Does Diffusion Matter?

arXiv:2609.23967v1 Announce Type: new Abstract: Diffusion models are increasingly adapted from generation to conditional prediction, where a conditioning signal is combined with an evolving noisy repr

Rethinking Pivot Programming Languages in Code Language Models

arXiv:2609.22988v1 Announce Type: new Abstract: Multilingual code language models transfer skills across programming languages (PLs), but whether any PL occupies a privileged pivot position remains co

safetyarxiv-cs-cl

Rethinking Streaming Video Diffusion Model: Context, Execution, and Training

model-releasesarxiv-cs-cv

arXiv:2609.22283v1 Announce Type: new Abstract: Understanding the design space of streaming video diffusion is essential to exploring its potential for generation quality and computational efficiency.

Rethinking Vision Architectures with Gated Linear Attention and KAN

arXiv:2609.22506v1 Announce Type: new Abstract: Vision Transformers allocate most parameters to multi-layer perceptrons (MLPs) for channel mixing, while token interactions usually rely on quadratic mu

Retrieval Geometry Shapes Cache-Based Clip Adaptation

arXiv:2609.23409v1 Announce Type: new Abstract: Cache-based test-time adaptation improves CLIP predictions by storing and retrieving examples from the target stream while keeping the model frozen. How

ReVeal: A Reconstruction-Aware Real-to-Sim Framework for VLA Policy Evaluation

arXiv:2609.23910v1 Announce Type: new Abstract: Simulation-based evaluation provides a scalable and repeatable alternative to real-world evaluation of vision-language-action (VLA) policies. However, r

safetyarxiv-cs-ro

Revisiting Multi-View Stereo: A Sequence-to-Sequence Formulation

arXiv:2609.24850v1 Announce Type: new Abstract: Computing accurate geometry from multi-view images is a fundamental problem in computer vision. Recent feed-forward (FF) models jointly estimate 3D geom

RewardVerse: Rubric-Guided Policy Optimization for Video Reward Modeling

model-releasesarxiv-cs-cv

arXiv:2609.22947v1 Announce Type: new Abstract: Reinforcement learning (RL) is vital for optimizing video generation models, with a robust reward model (RM) serving as the cornerstone. However, existi

RheOFormer: A generative transformer model for simulation of complex fluids and flows

arXiv:2510.01365v2 Announce Type: replace Abstract: The ability to model mechanics of soft materials under flowing conditions is key in designing and engineering processes and materials with targeted

Riemannian Density-Driven Optimal Control: Tangent-Space LQR for Second-Order Multi-Agent Systems on Curved Manifolds

arXiv:2609.22678v1 Announce Type: cross Abstract: Density-Driven Optimal Control (D2OC) provides an effective framework for steering multi-agent systems toward prescribed spatial distributions. Howeve

safetyarxiv-cs-ro

Industry

Right-size generative AI endpoints with concurrency sweeps on Amazon SageMaker AI

Concurrency sweeps help you right-size a generative AI endpoint on Amazon SageMaker AI by systematically benchmarking it at increasing load levels. This post walks through deploying a model, running a

industryaws-ml-blog

Local Ai

Risk-Aware Motion Planning and Control under Unknown Dynamics with Hybrid Observations

arXiv:2609.23792v1 Announce Type: new Abstract: We consider robotic motion planning and control under unknown dynamics with hybrid state observations, where state measurements are available only in pa

local-aiarxiv-cs-ro

RiverVLN: Phase-Grounded Temporal Vision--Language Navigation for Unmanned Surface Vehicles

model-releasesarxiv-cs-ro

arXiv:2609.23423v1 Announce Type: new Abstract: Vision-language navigation (VLN) has largely been developed for indoor and terrestrial robots, where language can often be treated as a static goal and

RL-STaR: Theoretical Analysis of Reinforcement Learning Frameworks for Self-Taught Reasoner

arXiv:2410.23912v3 Announce Type: replace-cross Abstract: The reasoning abilities of large language models (LLMs) have improved with chain-of-thought (CoT) prompting, allowing models to solve complex

RLVR is a Kernel, Not a Function: Statistical Inference for pass@k Crossovers

arXiv:2609.22547v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) often improves pass@1 while falling behind its base model at larger sampling budgets k, a crosso

RLVR^{2}: Reinforcement Learning with Verifiable Rubric-based Ranking

arXiv:2609.23457v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is expanding from tasks with well-defined correctness signals, such as mathematics and code, towar

Tutorials

Robot Programming with Augmented Reality: The Role of Spatial Ability

arXiv:2602.03544v2 Announce Type: replace Abstract: Programming a robot arm requires users to interpret coordinate frames, joint rotations, and trajectories that are not directly visible. Augmented re

tutorialsarxiv-cs-ro

Robot World Models Are Not Invariant to How the Actions Are Written

arXiv:2609.23252v1 Announce Type: cross Abstract: A robot policy is trained with one of two action parameterizations: absolute joint targets, or deltas relative to the current state. The choice is a l

Local Ai

RoboTalk: Learning Multi-Robot Communication and Coordination from Multimodal Demonstrations

arXiv:2609.23997v1 Announce Type: new Abstract: Multi-robot collaboration could enable more efficient and scalable solutions to complex robotic tasks, but collaboration under partial observability rem

local-aiarxiv-cs-ro

Robotic Valve Turning: Axial Misalignment Correction Using Reaction Torque Feedback

arXiv:2609.24413v1 Announce Type: new Abstract: In this work, we propose a haptic update control law that uses reaction torques to correct axial misalignment during robotic valve manipulation. Unlike

researcharxiv-cs-ro

Robust Market Making with Hawkes Order Flow and Price Impact via Adversarial Reinforcement Learning

arXiv:2609.22785v1 Announce Type: new Abstract: Market-making strategies in real limit order book markets face substantial model uncertainty and regime-shift risk. Existing adversarial reinforcement l

Role-Aware Morgan Fingerprints for Reaction Yield Prediction

arXiv:2609.22167v1 Announce Type: new Abstract: Predicting reaction yield from molecular structure and reaction context can cut experimental trial-and-error and speed up condition screening in synthet