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Provable Benefits of Regularization: Fast Rates for Adversarial Imitation Learning

arXiv:2609.35698v1 Announce Type: new Abstract: We study adversarial imitation learning (AIL), in which an agent learns to imitate expert demonstrations by optimizing a policy against an adversarial r

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QAMM: Adjoint MeanFlow Matching for Few-Step Offline Reinforcement Learning

DGX agent

arXiv:2609.34497v1 Announce Type: new Abstract: Flow policies can model rich action distributions, but their iterative sampling limits decision speed. Adjoint matching uses the critic's action gradien

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29 Sept 2026

Quantile Head for Vision-Language-Action Models

arXiv:2609.34061v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models integrate pretrained Vision-Language Models (VLMs) with action heads for robot control. Common action heads have d

Query-aligned video frame selection for long video understanding

arXiv:2609.31668v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) process multimodal inputs by converting text, images, and videos into token sequences that are subsequently pro

Radiomap Blind Prediction under Incomplete Observation: Error Characterization and Correctable Propagation-Prior Learning

arXiv:2609.32836v1 Announce Type: cross Abstract: Radiomap blind prediction aims to infer radiomaps from observable representations of the propagation environment and base station configuration withou

RAGWarrant: Evidence-Preserving Governance for RAG Policy Promotion Under Quality, Cost, Latency, and Risk Constraints

arXiv:2609.34179v1 Announce Type: cross Abstract: Retrieval-augmented generation systems are extensively instrumented with metrics, benchmarks, traces, and automated judges, but these tools do not dec

Rank Collapse Is Recoverable, Growing |Q| Is Not: Out-of-Sample Early Warning for Value Divergence in High-UTD Soft Actor-Critic

arXiv:2609.32819v1 Announce Type: cross Abstract: Raising the update-to-data (UTD) ratio breaks off-policy critics in two ways grouped as 'plasticity loss': collapsing representations and growing valu

RE-0: Verified Recursive Improvement of Embodied Code-as-Policy Agents through Local On-Policy Distillation

arXiv:2609.32416v1 Announce Type: cross Abstract: Code-as-Policy agents accomplish long-horizon embodied tasks by generating and executing code, yet continually improving them with teachers that are s

RecastVLA: From Past Interaction to Future Control with Adaptive Policy States

arXiv:2609.32155v1 Announce Type: new Abstract: Sequential manipulation requires a robot to track what has already happened, even when the current scene no longer reveals it. Policies with explicit hi

ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation

arXiv:2609.35200v1 Announce Type: cross Abstract: Memory-dependent manipulation requires robots to make decisions using information that is no longer available to their current sensors, such as recall

Recommendation Ranking Off-Policy Evaluation under Ranking-Dependent Examination via Examination-Relevance Decomposition

arXiv:2609.35034v1 Announce Type: cross Abstract: Off-policy evaluation, which estimates evaluation policy performance from logged data, is key for recommender ranking policies. However, logged clicks

Reduced Cartesian Kinetostatics for Tendon-Driven Continuum Robots: Residual-Stabilized Full-Shape Propagation

arXiv:2609.31771v1 Announce Type: new Abstract: Many planning and control tasks for tendon-driven continuum robots (TDCRs) require the complete Cartesian backbone geometry. We present a reduced Cartes

Refreshing Less, Selecting Better: Reusing Stale Gradient Features for Efficient Influence-Based Data Selection

arXiv:2609.32888v1 Announce Type: new Abstract: Gradient-based data selection methods such as LESS score each candidate by the alignment between its gradient and a target validation gradient, and reco

Relation-Aware Graph Foundation Model

arXiv:2505.12027v2 Announce Type: replace Abstract: In recent years, large language models (LLMs) have demonstrated remarkable capability to generalize across diverse natural language processing tasks

Relational Compression: A Framework for Relational Fidelity in Constrained Representations

arXiv:2609.31816v1 Announce Type: new Abstract: What should a compressed representation preserve when the information of interest lies in relationships among elements rather than in the elements thems

Relevance Does Not Imply Applicability: Experience Activation for Personal GUI Agents

arXiv:2609.33304v1 Announce Type: new Abstract: Personal Graphical User Interface (GUI) agents rely on interaction history to infer what a user wants from ambiguous instructions and to anticipate recu

Reliable Replay through Spatial Coherence in Online Continual Learning

arXiv:2609.33725v1 Announce Type: cross Abstract: Continually adapting models to new tasks requires retaining earlier knowledge under limited memory and computation. Experience replay addresses this c

RepFlow: Reciprocal Supervision Improves Generation and Representation in Flow Models

arXiv:2609.33217v1 Announce Type: new Abstract: Generative models learn visual structure through denoising, yet their internal states are entangled with both noise level and network depth, making it d

Representation Editing for Multimodal Test-Time Adaptation

arXiv:2609.32263v1 Announce Type: new Abstract: Multimodal test-time adaptation (TTA) aims to adapt a pretrained multimodal model online to distribution shift across modalities using unlabeled test da

Residual Denoising Enables Sample-Efficient Multi-Agent Coordination on Demand

arXiv:2609.32129v1 Announce Type: new Abstract: Pretrained robot policies offer strong manipulation skills but are typically limited to single-agent settings, where a robot acts in isolation. In this

Rethinking Cross-Channel Importance in Time-Series Forecasting

arXiv:2609.32187v1 Announce Type: new Abstract: Cross-channel modeling is central to multivariate time-series forecasting, yet channels that are statistically related, predictively useful, and actuall

Rethinking Latent Visual Reasoning: Grounding Latent Reasoning in Visual Evidence

arXiv:2609.34563v1 Announce Type: new Abstract: Latent visual reasoning (LVR) enables multimodal large language models (MLLMs) to perform intermediate computation in continuous latent tokens rather th

Revision, Not Restart: Revisable Visual Plans for Closed-Loop World-Action Models

arXiv:2609.35439v1 Announce Type: cross Abstract: World-action models use predicted visual futures to condition robot actions, yet execution feedback can invalidate parts of a prediction while leaving

RewardExplainer: Learning Reward Model Explanations from Counterfactual Preference Feedback

arXiv:2609.33989v1 Announce Type: cross Abstract: Reward models (RMs) are a key component of large language model post-training, providing reward signals for subsequent reinforcement learning. However

RMB: Reward Model Boosting Mitigates Reward Hacking

arXiv:2609.33221v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is a powerful technique for aligning large language models (LLMs) with human preference. However, it o

RoboFFT: Finetuning generative robot policy via online reinforcement learning with forward process

arXiv:2609.32236v1 Announce Type: new Abstract: Generative models, such as diffusion and flow-based models, have shown strong promise for robot policy learning by capturing complex and multimodal acti

Robot-GST: geometry-aware spatial-temporal robot policy representation and evaluation

arXiv:2609.33872v1 Announce Type: cross Abstract: Robotic manipulation policies are advancing rapidly with increasing reliance on vision-language models for end-to-end decision making. However, reliab

Role Support in Knowledge-Graph Error Ranking: Predictor Regimes and Evaluation Policies

arXiv:2512.22318v3 Announce Type: replace Abstract: When do role-specific training counts improve knowledge-graph error ranking? We evaluate fifteen from-scratch predictors, nine development-selected

RoPE is Dead, Long Live RoPE: Towards Scalable Data-aware Positional Encodings

arXiv:2609.34556v1 Announce Type: cross Abstract: Transformers process tokens without any inherent notion of order, making positional encoding a fundamental requirement rather than an architectural re

RPA: Residual Patch-Token Adapter for Image Retrieval from EEG and MEG

arXiv:2609.31698v1 Announce Type: new Abstract: Most existing MEG and EEG (M/EEG) visual decoding methods align brain signals with a single global embedding extracted from a pretrained visual encoder,

RSD-Poker: Structure-Adaptive and Shift-Robust Risk-Utility Certification for Residual Policies in Imperfect-Information Games

arXiv:2609.33669v1 Announce Type: cross Abstract: Residual policy adaptation provides a lightweight way to modify a strong reference policy, but a shared scale and a fixed subgroup partition can hide

RubiCap: Rubric-Guided Reinforcement Learning for Dense Image Captioning

arXiv:2603.09160v2 Announce Type: replace Abstract: Dense image captioning is critical for vision-language pretraining and for text-to-image generation, but scaling expert-quality annotations is prohi

Sample What You Say: Aligning Language Models to Sample the Distributions They State

arXiv:2609.34929v1 Announce Type: new Abstract: Language models are increasingly used to sample from a specified distribution, for instance, to simulate survey respondents or generate synthetic data.

Save Your Saturated Data: Learning Beyond Reward Saturation in Group-Based RL

arXiv:2609.33126v1 Announce Type: new Abstract: Group-relative reinforcement learning (RL) relies on reward variation among sampled responses to estimate informative relative advantages. As language m

Scaffold Then Internalize: Representation Injection for Diffusion Transformers

arXiv:2609.35292v1 Announce Type: new Abstract: Recent representation alignment (REPA) methods accelerate diffusion transformer training by aligning projections of the transformer's hidden states with

Scalable Attribution and Control of Model Behavior During Training

arXiv:2609.33667v1 Announce Type: new Abstract: Attributing and controlling model behavior during training requires identifying each example's contribution quickly enough to act before the next update

Schrodinger--Follmer Actor--Critic: Diffusion Policy Improvement with Finite-Sample Analysis

arXiv:2609.33239v1 Announce Type: cross Abstract: Diffusion policies represent multimodal action distributions, but an advantage-weighted update does not specify how to sample from the resulting targe

SCOPD: Sparse-Context On-Policy Self-Distillation for Efficient Vision-Language Models

arXiv:2609.34044v1 Announce Type: new Abstract: Reasoning vision-language models (VLMs) process images and videos as long sequences of visual tokens, making inference expensive. Training-free token pr

SEES: A Self-Evolving Embodied System via Failure-Guided VLA Policy Adaptation

arXiv:2609.32698v1 Announce Type: new Abstract: Recent vision-language-action (VLA) policies demonstrate promising generalization across diverse short-horizon tasks. However, they remain unreliable on

Self-Confirming Superposition Traps in Reinforcement Learning

arXiv:2609.32966v1 Announce Type: new Abstract: Reinforcement learning (RL) trains representations on data selected by the agent's policy, which then uses the resulting returns to guide its next choic

Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence

arXiv:2609.35432v1 Announce Type: new Abstract: Vision-language-action (VLA) and world-action (WAM) models map observations and instructions directly to robot actions. This directness ties a policy to

Semantic Modality Compensation for Unsupervised Visible-Infrared Person Re-identification under Unpaired Settings

arXiv:2609.34294v1 Announce Type: new Abstract: Unsupervised visible-infrared person re-identification (USL-VI-ReID) learns person representations that can be compared across modalities without identi

SemReward-VL: Semantic Reward-Guided Video-Language Adaptation for Developmental Behavior Assessment

arXiv:2609.33082v1 Announce Type: new Abstract: Developmental screening videos show how children perform specific behaviors, but clinical records usually contain outcomes rather than descriptions of w

SGA-Flow-GRPO: Spatial Gradient-Guided Credit Assignment for Flow-GRPO

arXiv:2609.32340v1 Announce Type: new Abstract: Reinforcement Learning (RL) has proven effective in aligning flow-based generative models with human preferences. Recently, Flow-GRPO has emerged as an

SignFLIP: A Unified Model for Sign Language Translation and Generation via Stage-wise Alignment at Scale

arXiv:2609.35225v1 Announce Type: cross Abstract: Sign language translation and generation share the goal of bidirectional alignment between text and sign representations. However, existing approaches

SLIP-VLA: Single-Step Latent Imagination for Policy Learning in Vision-Language-Action Models

arXiv:2609.33575v1 Announce Type: new Abstract: Vision-Language-Action models are increasingly effective for robotic manipulation, yet most predict actions directly from current observations without e

Slot-RAE: Streamlining Object-Centric Learning via Direct Representation Auto-Encoders

arXiv:2607.11196v2 Announce Type: replace Abstract: Deploying object-centric models for real-world scene understanding typically requires complex pipelines to achieve both robust scene decomposition a

SNIP++: Fine-Grained Symbolic-Numerical Alignment for Symbolic Regression

arXiv:2609.31965v1 Announce Type: cross Abstract: Mathematical expressions and the numerical behavior they produce are two views of the same underlying function, and connecting them is central to scie