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Orchestrating Level-K Policies Against Unknown Opponents in Partially-Observable Dynamic Games

arXiv:2610.04937v1 Announce Type: cross Abstract: Level-K reasoning generates a hierarchy of policies specialized to opponents with different reasoning levels. When an opponent's level is unknown, a c

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Outcome-Guided On-Policy Self-Distillation

DGX agent

arXiv:2610.05070v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) provides denser token-level supervision and better computational efficiency than Reinforcement Learning with Verifiab

safetyarxiv-cs-lg

6 Oct 2026

PatternDex: Learning Interaction Patterns to Guide Reinforcement Learning of Bimanual Dexterous Manipulation of Articulated Objects

arXiv:2610.04765v1 Announce Type: new Abstract: In this paper, we develop a method that enables bimanual dexterous hands to manipulate articulated objects with a high success rate without suffering fr

PB-STDG: A Prediction-Based Short-Term Decentralized Greedy Guidance Algorithm for a Drone Road System

arXiv:2610.05122v1 Announce Type: new Abstract: In recent years, Unmanned Aerial Vehicles (UAVs) or drones have been increasingly adopted in urban environments for applications such as parcel delivery

PDE-SSM: A Spectral State Space Approach to Spatial Mixing in Diffusion Transformers

arXiv:2603.13663v2 Announce Type: replace Abstract: The success of vision transformers-especially for generative modeling-is limited by the quadratic cost and weak spatial inductive bias of self-atten

PermVLA: Factorization Order as a Regularizer for VLA Learning

arXiv:2610.04659v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies commonly learn action chunks through a fixed left-to-right (LTR) factorization, although the same expert traject

PerturBot: Breaking Shortcut Priors in Vision-Language-Action Models with Perturbative Training

arXiv:2610.04616v1 Announce Type: cross Abstract: A vision--language--action (VLA) policy can complete complex tasks while ignoring the evidence that should determine its actions. An object held near

Pessimistic Minimax Learning for Public-Private Information Games under Unilateral Coverage

arXiv:2610.04997v1 Announce Type: new Abstract: We study offline learning in two-player zero-sum contextual games with public and private information, motivated by strategic settings such as auctions

Physics-Informed but Not Physics-Consistent: Error Geometry and Subspace Projection for Neural AC Power Flow

arXiv:2610.05959v1 Announce Type: new Abstract: Recent neural power-flow solvers, including emerging foundation models, achieve accurate voltage predictions, yet such accuracy does not necessarily imp

PIT-GCL: Protein Interaction using Topological Graph Contrastive Learning

arXiv:2610.04850v1 Announce Type: new Abstract: Protein binding prediction is central to target identification, therapeutic binder design, and large scale screening, yet remains challenging because bi

Planning for Change: Reinforcement Learning Combined with Bounded Extremum Seeking for Robotic Control under Distribution Shift

arXiv:2604.01142v2 Announce Type: replace-cross Abstract: Reinforcement learning has shown strong performance in robotic manipulation, but learned policies often degrade in performance when test condi

PlatoLTL: Scaling LTL-Guided Multi-Task RL

arXiv:2601.22891v3 Announce Type: replace Abstract: Linear temporal logic (LTL) has emerged as a powerful formalism for specifying structured, temporally extended tasks in multi-task reinforcement lea

Policy as Data: Replay-Based Policy Dual Averaging via Advantage Regression

arXiv:2610.04638v1 Announce Type: new Abstract: Actor-critic methods reuse past experience to improve sample efficiency. However, historical data are typically regarded as off-policy samples for the c

Principled Top-k Selection for Language Models with Hybrid Gradients

arXiv:2610.04162v1 Announce Type: new Abstract: Selecting the best k items out of m candidates is a critical component of modern large language model systems, such as document selection in Retrieval-A

Prompt Dominance and Asymmetric Verifier Costs: Empirical Ablations of GRPO at 1B Scale on GSM8K

arXiv:2610.04928v1 Announce Type: new Abstract: This paper studies GRPO at 1B scale from both directions: what estimator choices do to the learning signal, and what a degraded reward signal does to wh

PWM: Personalized World Models with Online Reinforcement Learning

arXiv:2610.04920v1 Announce Type: new Abstract: Pretrained world models can generate diverse environments, yet users often want to explore a particular scene specified by their own video. This require

R^2-WAM: Repair-and-Reject Post-Training for World Action Models

arXiv:2610.04913v1 Announce Type: new Abstract: World Action Models (WAMs) emerge as a promising foundation for policy refinement by predicting the consequences of sampled actions. However, visually p

Ramp Metering Control via Hybrid State Deep Reinforcement Learning in Partially Observable Connected Vehicle Environments

arXiv:2610.06266v1 Announce Type: new Abstract: Freeway on-ramp merges are major sources of congestion, causing significant economic and environmental costs. While Deep Reinforcement Learning (DRL) of

Reachability-Aware Diffusion Policy Optimization

arXiv:2610.05969v1 Announce Type: new Abstract: Diffusion policies provide expressive action distributions for continuous-control reinforcement learning. However, safety-aware online diffusion policy

Really nice paper on harness-aware distillation for small language model agents. (bookmark it) It's a super interesting distillation framewo…

Really nice paper on harness-aware distillation for small language model agents. (bookmark it) It's a super interesting distillation framework for small language model agents that are deployed with a

safetydair-ai--x

Recon2Servo: Robotic Ultrasound Visual Servoing via Learned Image-to-Motion Inference

arXiv:2610.06010v1 Announce Type: new Abstract: Ultrasound visual servoing is essential for autonomous robotic ultrasound, yet 6-DoF probe control from 2D B-mode images remains challenging due to limi

Recurrent Latent Visual Search for GUI Grounding

arXiv:2610.05185v1 Announce Type: new Abstract: GUI grounding is a critical capability for GUI agents powered by vision-language models, helping them execute user instructions by locating the correspo

Recursive Self-Improvement of Visuomotor Policies through Local Recovery Supervision

arXiv:2610.05151v1 Announce Type: new Abstract: Visuomotor policies can execute familiar tasks yet lack the corrective behavior needed after their own mistakes. We present a framework for recursive se

Reflections and Fragments: Securing LLMs Against Sequential Mosaic Attacks

arXiv:2610.05346v1 Announce Type: new Abstract: Self-play red-teaming improves language-model safety by pitting attacker and defender roles against each other in a zero-sum game. However, real adversa

Relational Synthesis: Structure-Mediated Concatenative Synthesis for Foley and Retrieval-Augmented Audio Generation

arXiv:2610.05768v1 Announce Type: cross Abstract: We ask: given a retrieved source audio S and a separate reference audio R, can we synthesize novel audio Y out of this pair (S,R) such that Y remains

Representation Disentanglement for Fair Chest X-Ray Diagnosis

arXiv:2610.06041v1 Announce Type: new Abstract: Deep learning has advanced chest X-ray (CXR) diagnosis, yet demographic biases in learned representations may contribute to performance disparities acro

Representation-Space MMD for Diffusion Language Models

arXiv:2610.06648v1 Announce Type: cross Abstract: We introduce a post-training method for diffusion language models (DLMs) that minimizes Maximum Mean Discrepancy (MMD) between generated and reference

Request Order Matters: Cache-History Sensitivity in Selective KV-Cache Reuse for Rolling Agents

arXiv:2610.05833v1 Announce Type: cross Abstract: Long-running agents repeatedly call an LLM while retaining most of their document window, evicting old documents, and appending new ones. These rollin

ResOPD: Tail Residualization for Sparse On-Policy Distillation

arXiv:2610.04882v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student model on self-generated trajectories, but transmitting dense teacher distributions across long reasoning t

Responsible AI governance: How AWS positions customers to align with ISO/IEC 42005:2025

AWS invests in tools that help customers align with international standards for responsible AI governance. In this post, we explore the AI system impact assessment: what it is, how it improves enterpr

safetyaws-ml-blog

Rethinking Self-Distillation for Multi-Teacher Capability Merging

arXiv:2610.04272v1 Announce Type: new Abstract: Combining capabilities of multiple expert models trained starting from the same base checkpoint has become increasingly common in frontier language-mode

Return-to-Home Feasible Micro-Aerial Vehicle Exploration for 3D Gaussian Splatting Reconstruction

arXiv:2610.04013v1 Announce Type: new Abstract: Micro aerial vehicles (MAVs) enable rapid indoor 3D reconstruction for inspection and time-critical situational awareness, but must operate under strict

Revealing After Overwriting: An Exponential POMDP OPE Lower Bound under History-Dependent Logging

arXiv:2610.05063v1 Announce Type: new Abstract: Multi-step revealing can make off-policy evaluation tractable under memoryless logging. With history-dependent logging, state decodability and target-re

Reward-DAgger: Robot-Gated Interactive Imitation Learning with General-Purpose Progress-Based Reward Models

arXiv:2610.04054v1 Announce Type: new Abstract: Recent advances in robot learning have enabled generalist control policies capable of completing a wide range of tasks. However, their performance degra

RFG: Self-Improving Diffusion Large Language Models with Reward-Free Guidance

arXiv:2509.25604v2 Announce Type: replace-cross Abstract: Diffusion Large Language Models (dLLMs) have shown strong reasoning capabilities, yet further improving them typically requires costly post-tr

RoboIRS: Inference-Time Internal Representation Steering for Generalist Robot Policies

arXiv:2610.04681v1 Announce Type: new Abstract: Vision-language-action (VLA) and world-action models (WAMs) often degrade under out-of-distribution task variations despite retaining partial task capab

Robot Learning with Visual Predicted Force

arXiv:2610.04741v1 Announce Type: new Abstract: Force-aware manipulation typically relies on specialized force or tactile sensors. We show that force-aware manipulation can instead be achieved through

Robotizing Human Videos with Physically Consistent Interactions

arXiv:2610.06137v1 Announce Type: new Abstract: Human videos offer scalable manipulation data, but the embodiment gap between human hands and robot manipulators limits their direct use. Existing video

Robust 2D Traversability Mapping for Construction AMRs via Failure-Mode-Aware Fusion of LiDAR Geometry and Monocular Semantics

arXiv:2610.05505v1 Announce Type: cross Abstract: Autonomous Mobile Robots (AMRs) on active construction sites face severe navigational challenges: geometry-based traversability mapping (e.g., LiDAR)

ROOT: Discovering Rewards for User-Specified Embodied Behaviors

arXiv:2610.04250v1 Announce Type: new Abstract: Reinforcement learning for embodied control remains constrained by the difficulty of reward specification. Although recent large language model (LLM)-ba

Safe Ergodic Control for Multi-Robot Systems via Quadratic Programming

arXiv:2610.05258v1 Announce Type: new Abstract: Ergodic control drives robots to spend time in each region in proportion to a spatial distribution of interest, making it well suited for dense spatiote

Scenario-Based Compositional Statistical Model Checking for Safety Specifications

arXiv:2610.05571v1 Announce Type: cross Abstract: In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as

Score-Calibrated Flow for Sampling from Unnormalized Densities with Applications to Generative Online Reinforcement Learning

arXiv:2610.04696v1 Announce Type: new Abstract: Diffusion and flow models provide expressive policy classes for online reinforcement learning (RL), enabling multimodal behaviors and improved performan

SEER: Self-Evolving Event Reasoning and Retrieval for Time Series Forecasting

arXiv:2610.04109v1 Announce Type: new Abstract: Real-world time series are frequently driven by exogenous events and structural shifts, rendering conventional forecasting based solely on historical nu

Selecting Long-Horizon Trajectories for Reliable and Efficient Terminal-Agent Training

arXiv:2610.05831v1 Announce Type: new Abstract: Terminal agents are commonly trained by imitating long teacher trajectories, yet how much of each trajectory to supervise remains unexplored. We study t

Sharp Convergence and Sample Complexity of Policy Mirror Descent for Average-Reward MDPs

arXiv:2610.04117v1 Announce Type: new Abstract: Policy mirror descent (PMD) has a mature finite-time theory in discounted Markov decision processes (MDPs), but less is known in the average-reward sett

Sharpen Without Search: On-Policy Distillation of Sequence-Level Power Distribution

arXiv:2610.06804v1 Announce Type: new Abstract: A language model can give a correct answer more probability than any single incorrect answer and still usually sample an incorrect one, because the inco

SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models

arXiv:2610.06598v1 Announce Type: cross Abstract: Action-conditioned robot world models must respond precisely to robot trajectories while preserving realistic visual dynamics, yet learning both from