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A Step Harder, Not a Step Too Far: The Benefits and Limits of Curriculum Difficulty Progression in GRPO

arXiv:2610.13669v1 Announce Type: new Abstract: Curriculum learning typically organizes training samples from easy to hard. In GRPO, medium-difficulty samples are attractive for online selection becau

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A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)

DGX agent

arXiv:2510.22809v2 Announce Type: replace-cross Abstract: Traditional machine learning relies on explicit models and domain assumptions, often limiting flexibility and interpretability. We introduce a

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12 Oct 2026

ABLEIST: Intersectional Disability Bias in LLM-Generated Hiring Scenarios

arXiv:2510.10998v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly under scrutiny for perpetuating identity-based discrimination in high-stakes domains such as hir

Agreement Without Alignment: Label-Free Rubric Clarification Increases LLM-Judge Consensus but Not Agreement with Humans

arXiv:2610.13303v1 Announce Type: cross Abstract: LLM judges are increasingly deployed as panels, and disagreement among panel members is often read as a symptom of an unclear rubric. We test a label-

ATHLETE: Learning Reactive Humanoid Tennis via Trajectory-Guided Motion Matching

arXiv:2610.13572v1 Announce Type: new Abstract: Humanoid tennis requires reactive whole-body coordination under rapidly changing ball dynamics, yet collecting task-specific motion-capture demonstratio

AutoControl Arena: Synthesizing Executable Test Environments for Frontier AI Risk Evaluation

arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costl

Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models

arXiv:2610.13212v1 Announce Type: cross Abstract: Actions specify what a robot should execute, but do not describe what those commands will achieve. Vision-language-action (VLA) policies nevertheless

Beyond Binary Verifiable Rewards: Behavioral Self-Rewarding for SWE Agents

arXiv:2610.12598v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) provides reliable supervision for software engineering agents through executable tests, but binary

Beyond Domain-Routed Supervision: Cross-Teacher Guarding for Multi-Teacher On-Policy Distillation

arXiv:2610.13521v1 Announce Type: new Abstract: Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) by routing domain-specific data to specialized teachers, integrating th

Beyond Successful Robot Demonstrations: Learning from Human Interactions and Modeling Failure Dynamics

arXiv:2610.13522v1 Announce Type: new Abstract: Action-conditioned world models offer a promising foundation for generalist robotic intelligence by predicting the consequences of actions. However, exi

Branching On-Policy Distillation

arXiv:2610.13481v1 Announce Type: new Abstract: On-policy distillation (OPD) provides teacher supervision on student-generated trajectories, but a single trajectory realizes only one continuation at e

Bridging Human and Model Attention: Explainability Analysis of CNN, Mamba, and ViT Architectures with Gaze-Based Validation

arXiv:2610.12703v1 Announce Type: cross Abstract: High-risk applications of deep learning require precise characterization of model decision-making. We conduct a systematic explainability evaluation a

Can Language Models Learn When to Abstain? Policy Learning and Calibrated Answer Selection

arXiv:2610.13821v1 Announce Type: new Abstract: A useful language model should know when to answer and when to say 'I don't know.' Can we improve that decision by training the model itself or by train

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CASTER: Formalizing Zero-Shot Manipulation from Synthetic Videos as Constrained Optimization

arXiv:2610.12567v1 Announce Type: new Abstract: Prior research on learning from humans often requires real human demonstrations. But advances in video generation models offer a new paradigm: we can no

CeDR-Nav: Cross-Embodiment Mapless Navigation via Offline Demonstration Recasting

arXiv:2610.13007v1 Announce Type: new Abstract: Learning-based methods have demonstrated strong performance in mapless robot navigation. However, a learned policy remains restricted to the embodiment

Chronos Policy: Learning Robot Policies on Physical Time

arXiv:2610.13929v1 Announce Type: new Abstract: Robot policies often convert sensing and control into fixed-rate observation-action sequences, leaving physical time implicit in sequence position. This

CLEAT: Learning Value-Guided Clarification for Interactive Agents

arXiv:2610.12655v1 Announce Type: new Abstract: LLM agents can execute increasingly complex tasks, but effective assistance also requires aligning their actions with goals and constraints that users m

CliQ-RRG: Clinical-Knowledge Guided Disease-aware Visual-Textual Alignment for QA-Style Radiology Report Generation

arXiv:2610.13059v1 Announce Type: new Abstract: Radiology reports are essential for diagnostic reasoning and patient care, yet their manual preparation is time-consuming and cognitively demanding. Aut

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Collaborative Theory of Mind: Learning to Self-Revise by Anticipating User Responses

arXiv:2610.12872v1 Announce Type: new Abstract: Large language models (LLMs) demonstrate strong individual task performance, yet still struggle to collaborate with users over extended, multi-turn inte

CopRoute: Dependence-Aware Conformal Routing for Language Models

arXiv:2610.13276v1 Announce Type: new Abstract: Set-based language model routing aims to increase the coverage of the event where at least one model answers a query correctly while controlling the num

CV-OPD: Contrastive Vision On-policy Distillation

arXiv:2610.13264v1 Announce Type: cross Abstract: On-policy distillation (OPD) reveals versatility and convenience during VLM post-training, providing dense supervision for student from teacher. Howev

DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial Networks

arXiv:2610.12916v1 Announce Type: new Abstract: Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subje

DiffusionART: Efficient, Stable and Scalable Reinforcement of Diffusion Models

arXiv:2610.12572v1 Announce Type: new Abstract: Online reinforcement learning (RL) fine-tunes diffusion models with black-box rewards, but reverse-process methods such as Flow-GRPO are limited in effi

DistressBench: Evaluating Multi-Dimensional Crisis Support in Large Language Models

arXiv:2610.12758v1 Announce Type: new Abstract: Language models have been adopted faster than evaluation practices that reliably assess their risks, and a growing share of use is affective rather than

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Distributionally Robust Adaptive Iterative Covariance Steering for Small-Body Proximity Operations

arXiv:2610.12774v1 Announce Type: cross Abstract: Autonomous small-body proximity operations require continuous-thrust guidance under uncertain gravity, solar-radiation pressure (SRP), and stochastic

Dream to Remember: Continual Steering of Generalist Robot Policies with World Models

arXiv:2610.13816v1 Announce Type: cross Abstract: Generalist robot policies are trained with imitation learning and are bounded by the quality and coverage of their demonstrations. Reinforcement learn

Dual-Stream Memory for Vision-Language-Action Policies

arXiv:2610.13262v1 Announce Type: new Abstract: Memory-dependent manipulation requires a policy to track execution state and recover visual evidence that is no longer observable. We propose dual-strea

Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring

arXiv:2610.12538v1 Announce Type: new Abstract: Modern machine learning models are typically trained on data assumed to be representative of the predictive tasks the model will face in the future, but

EndoRSI: Physical-Evidence-Driven Recursive Improvement of Endoscopic Robot Policies

arXiv:2610.13376v1 Announce Type: new Abstract: Artificial intelligence is beginning to automate scientific discovery, yet physical robot-policy research still relies on scientists and engineers to in

Equality, Equity, and Causality in Fairness Research: A Commentary on Cheng (2026)

arXiv:2607.17679v2 Announce Type: replace-cross Abstract: This is an invited commentary on the Psychometrika focus article 'Fairness Issues and Evaluation in Psychometrics and AI/ML: What Can We Learn

Evidence-Guided Counterfactual Learning for Long-Tailed Robotic Policies

arXiv:2610.12861v1 Announce Type: new Abstract: In the practical deployment of vision--language--action policies, handling long-tailed task distributions is a common challenge due to the highly imbala

Evidential Multimodal Transformers with Dempster-Shafer Fusion for Emotion Recognition

arXiv:2610.13013v1 Announce Type: new Abstract: Softmax-based multimodal emotion recognition methods often assign high confidence even when sensor data are corrupted or contradictory, for example when

Evo-RL: Iterative Robot Reinforcement Learning from Selective Experience

arXiv:2610.13345v1 Announce Type: new Abstract: Existing real-world reinforcement learning methods improve robot policies by combining human corrections with autonomous rollouts. However, accumulated

Evo-World: A Lightweight Vision-Language-Action Model with Future Motion Prediction via Optical-Flow Supervision

arXiv:2610.12936v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a unified framework for visual understanding, language grounding, and robot control, yet anticipating how th

FedPCD: Priority-Constrained Federated Aggregation

arXiv:2610.13508v1 Announce Type: new Abstract: Federated learning (FL) seeks to optimize a shared global model from decentralized client data. While effective for average performance, this process ca

Forward Dynamics Models

arXiv:2610.13117v1 Announce Type: new Abstract: Vision-language-action (VLA) models and world action models (WAMs) have advanced robotic policy learning by building on pretrained vision-language model

Generalized Proximal Policy Optimization: Unifying LLM RL Algorithms from the Perspective of Divergence

arXiv:2610.13015v1 Announce Type: cross Abstract: Reinforcement learning has become the foundational paradigm for enhancing the reasoning capabilities of LLMs. Yet the dominant approach, Proximal Poli

Goal-Oriented Bundle Management and Flow Control for Deep-Space Communications via Reinforcement Learning

arXiv:2610.13923v1 Announce Type: cross Abstract: Deep-space communications operate under intermittent connectivity, long propagation delays, and relay storage constraints. In such networks, the Bundl

Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding

arXiv:2610.13164v1 Announce Type: new Abstract: Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive

Hidden Data Challenges in Clinical Machine Learning

arXiv:2610.13350v1 Announce Type: new Abstract: Widely reused preprocessing pipelines for electronic health records can encode assumptions about when information became available, what clinical episod

HorizonNav: Geometry-Guided Video Diffusion for Anticipatory 3D Robot Navigation

arXiv:2610.13849v1 Announce Type: new Abstract: We present HorizonNav, a video diffusion-based framework for anticipatory robot navigation that transforms a single RGB image and a text prompt into a g

Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations

arXiv:2610.13119v1 Announce Type: new Abstract: Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition. We ask whether one shared corpus

Hyper Algorithm Design Agent: Evolving Learnable Optimizer from Zero

arXiv:2609.35328v2 Announce Type: replace Abstract: Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages

i don’t know whether this is a joke or serious but the whole challenge with generative AI is that it literally cannot be trusted to follow “…

i don’t know whether this is a joke or serious but the whole challenge with generative AI is that it literally cannot be trusted to follow “simple, generalizable instructions”. the inability of most p

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Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models 🚀 Can AI learn to improve itself by predicting the consequ…

Introducing RSI-Anything: Unlocking Recursive Self-Improvement with World Models 🚀 Can AI learn to improve itself by predicting the consequences of its actions? Built on JEPA-Anything, RSI-Anything us

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JoyInside-VLM-Agent: Product-Conditioned Multimodal Policy Learning for Embodied Agents

arXiv:2610.12957v1 Announce Type: cross Abstract: Embodied agents must translate multimodal observations and user requests into executable actions while adapting to product constraints, changing goals

KITE: A Knowledge-Injected Temporal Encoder for Time-Series Event Forecasting

arXiv:2610.12738v1 Announce Type: cross Abstract: Forecasting rare, high-cost events in multivariate time-series systems is hard because their precursors are seldom observed, so a purely data-driven m

L_0 Feature Selection via Monte Carlo Tree Search in High-Dimensional Financial Regression

arXiv:2610.13574v1 Announce Type: new Abstract: Feature selection is a central problem in high-dimensional financial regression, where large numbers of engineered variables and strong correlations amo