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Efficient Bilevel Optimization for Meta Label Correction in Noisy Label Learning

arXiv:2605.17833v1 Announce Type: cross Abstract: Training a deep neural network with noisy labels could reduce data annotation cost but may introduce noise into the learned model. In meta label corre

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Enabling Off-Policy Imitation Learning with Deep Actor Critic Stabilization

DGX agent

arXiv:2511.07288v2 Announce Type: replace-cross Abstract: Learning complex policies with Reinforcement Learning (RL) is often hindered by instability and slow convergence, a problem exacerbated by the

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19 May 2026

Enhancing Train-Free Infinite-Frame Generation for Consistent Long Videos

arXiv:2605.18233v1 Announce Type: new Abstract: Without incurring significant computational overhead, train-free long video generation aims to enable foundation video generation models to produce long

Entropy-Gradient Inversion: Moving Toward Internal Mechanism of Large Reasoning Models

arXiv:2605.17770v1 Announce Type: new Abstract: The advancement of Large Reasoning Models (LRMs) has catalyzed a paradigm shift from reactive ``fast thinking'' text generation to systematic, step-by-s

Equilibrium Selection in Multi-Agent Policy Gradients via Opponent-Aware Basin Entry

arXiv:2605.18078v1 Announce Type: new Abstract: Multi-agent policy-gradient methods have been shown to converge locally near stable Nash equilibria. Local convergence, however, does not determine whic

Estimating Item Difficulty with Large Language Models as Experts

arXiv:2605.18562v1 Announce Type: cross Abstract: Accurate estimates of item difficulty are essential for valid assessment and effective adaptive learning. However, for newly created tasks, response d

Face inpainting with Identity Preserving Latent Diffusion Models

arXiv:2605.16696v1 Announce Type: new Abstract: Face inpainting techniques recover missing or occluded facial regions in a visually realistic manner, but preserving the identity in the final output re

Factored Causal Representation Learning for Robust Reward Modeling in RLHF

arXiv:2601.21350v2 Announce Type: replace Abstract: A reliable reward model is essential for aligning large language models with human preferences through reinforcement learning from human feedback. H

Factual Inconsistencies in Multilingual Wikipedia Tables

arXiv:2507.18406v2 Announce Type: replace Abstract: Wikipedia serves as a globally accessible knowledge source with content in over 300 languages. Despite covering the same topics, the different versi

Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent

arXiv:2605.17767v1 Announce Type: cross Abstract: We study feature learning in two-layer neural networks within the linear-width regime, where the number of hidden neurons, sample size, and input dime

FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction

arXiv:2605.18055v1 Announce Type: cross Abstract: Predicting spatial gene expression from routine H&E enables large-scale molecular profiling, yet current models treat this as isolated pointwise tasks

Flow Matching with Optimized Subclass Priors for Medical Image Augmentation

arXiv:2605.16469v1 Announce Type: cross Abstract: Rare diseases dominate the diagnostic challenge in medical imaging yet are severely underrepresented in clinical datasets, causing classifiers to fail

Forget Many, Forget Right: Scalable and Precise Concept Unlearning in Diffusion Models

arXiv:2601.06162v4 Announce Type: replace-cross Abstract: Text-to-image diffusion models have achieved remarkable progress, yet their use raises copyright and misuse concerns, prompting research into

From a Single Demonstration to a General Policy for Contact-Rich Manipulation

arXiv:2605.17601v1 Announce Type: new Abstract: We present a Learning from Demonstration (LfD) framework that achieves one-shot generalization in multi-stage, contact-rich manipulation tasks. Central

From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes

arXiv:2605.16303v1 Announce Type: cross Abstract: Large language models (LLM) agents may offer tools to predict human responses to surveys. A common technique for defining these agents uses only demog

From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework

arXiv:2605.16281v1 Announce Type: cross Abstract: Artificial intelligence systems are increasingly deployed in high-stakes domains, yet it remains unclear whether existing governance frameworks ensure

FUNCanon: Learning Pose-Aware Action Primitives via Functional Object Canonicalization for Generalizable Robotic Manipulation

arXiv:2509.19102v2 Announce Type: replace-cross Abstract: General-purpose robotic skills from end-to-end demonstrations often leads to task-specific policies that fail to generalize beyond the trainin

FUSE: A Framework for Unified State Estimation in Robotic SLAM Systems

arXiv:2605.18047v1 Announce Type: new Abstract: Tightly coupled SLAM formulations under mixed-rate sensing often bind temporal processing, local geometric association, estimator formulation, and map-u

GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation

arXiv:2512.23180v3 Announce Type: replace Abstract: Driving World Models (DWMs) have been developing rapidly with the advances of generative models. However, existing DWMs lack 3D scene understanding

Generating Physically Consistent Molecules with Energy-Based Models

arXiv:2605.18381v1 Announce Type: new Abstract: Molecules in equilibrium follow a Boltzmann distribution, making the underlying energy landscape a physically grounded modeling objective. However, such

Generation Navigator: A State-Aware Agentic Framework for Image Generation

arXiv:2605.17969v1 Announce Type: new Abstract: Despite rapid advances in text-to-image generation, faithfully realizing user intent remains challenging, often requiring manual multi-turn trial and er

Geometry-aware 4D Video Generation for Robot Manipulation

arXiv:2507.01099v4 Announce Type: replace-cross Abstract: Understanding and predicting dynamics of the physical world can enhance a robot's ability to plan and interact effectively in complex environm

GeoWorld-VLM: Geometry from World Models for Vision-Language Models

arXiv:2605.16713v1 Announce Type: cross Abstract: Modern Vision-Language Models (VLMs) achieve strong semantic recognition, yet remain brittle on elementary spatial relations such as left of, on, behi

Goal-Conditioned Supervised Learning for LLM Fine-Tuning

arXiv:2605.16345v1 Announce Type: cross Abstract: Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divide

HCLM: A Hierarchical Framework for Cooperative Loco-Manipulation with Dual Quadrupeds

arXiv:2605.17300v1 Announce Type: new Abstract: We introduce HCLM, a hierarchical framework for general-purpose cooperative loco-manipulation with dual quadrupedal systems. Coordinating multi-robot co

Helpful to a Fault: Measuring Illicit Assistance in Multi-Turn, Multilingual LLM Agents

arXiv:2602.16346v3 Announce Type: replace Abstract: LLM-based agents execute real-world workflows via tools and memory. These affordances enable ill-intended adversaries to also use these agents to ca

Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach

arXiv:2605.18437v1 Announce Type: new Abstract: Vehicular edge computing (VEC) enables latency-sensitive vehicular applications by offloading computation-intensive tasks to nearby edge servers. Howeve

How Few-Shot Examples Add Up: A Causal Decomposition of Function Vectors in In-Context Learning

arXiv:2605.16591v1 Announce Type: cross Abstract: In-context learning (ICL) excels at new tasks from minimal examples, yet we still lack a mechanistic explanation of how few-shot prompts shape a model

How Loud Rumbles Hit Newsstands: A Data Analysis of Coverage and Spatial Bias in German News about Landslides Around the World

arXiv:2605.18105v1 Announce Type: new Abstract: Landslides often hit newsstands due to their destructive and potentially fatal effects. News are a valuable source of information for creating or enrich

How Off-Policy Can GRPO Be? Mu-GRPO for Efficient LLM Reinforcement Learning

arXiv:2605.17570v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has been a key driver of recent progress in reinforcement learning with verifiable rewards (RLVR) for large

How to Instruct Your Robot: Dense Language Annotations Power Robot Policy Learning

arXiv:2605.17077v1 Announce Type: cross Abstract: Scaling robot policy learning is bottlenecked by the cost of collecting demonstrations, while language annotations for existing demonstrations are com

How Wrong Can Your Counterfactual Be? Quantifying Confounding Bias for Continuous Treatments without a Control Group

arXiv:2603.07438v2 Announce Type: replace Abstract: Stress testing poses a causal question: how would portfolio credit losses change if the macroeconomy followed an adverse counterfactual path? Yet st

Identifying Latent Actions and Dynamics from Offline Data via Demonstrator Diversity

arXiv:2603.17577v2 Announce Type: replace-cross Abstract: Can latent actions and environment dynamics be recovered from offline trajectories when actions are never observed? We study this question in

'I'm Not Mad, Just Focused'': Understanding Human Emotions in Human-Robot Collaboration

arXiv:2605.16816v1 Announce Type: new Abstract: Human-robot collaboration (HRC) can benefit from robots' abilities to interpret human emotional states. However, current emotion recognition (ER) models

Implicit Hierarchical GRPO: Decoupling Tool Invocation from Execution for Tool-Integrated Mathematical Reasoning

arXiv:2605.18500v1 Announce Type: new Abstract: Large language models (LLMs) have increasingly leveraged tool invocation to enhance their reasoning capabilities. However, existing approaches typically

Improved Baselines with Representation Autoencoders

arXiv:2605.18324v1 Announce Type: cross Abstract: Representation Autoencoders (RAE) replace traditional VAE with pretrained vision encoders. In this paper, we systematically investigate several design

Improving MLLM Training Efficiency via Stage-Aware Sparsity

arXiv:2509.18150v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated outstanding performance across a variety of domains. However, training MLLMs is oft

Individual utilities of life satisfaction reveal inequality aversion unrelated to political alignment

arXiv:2509.07793v4 Announce Type: replace-cross Abstract: How should well-being be prioritised in society, and what trade-offs are people willing to make between fairness and personal well-being? We i

InFeR: Informed Failure Resilience in Learned Visual Navigation Control

arXiv:2510.24680v2 Announce Type: replace Abstract: While imitation learning (IL) has enabled successful visual navigation in many common environments, IL policies are prone to unpredictable failures

Interpretable epistemic uncertainty decomposition in sequential generative models via polynomial chaos surrogates

arXiv:2510.21523v2 Announce Type: replace Abstract: Sequential generative models conditioned on uncertain rewards are central to AI-driven scientific discovery, yet the epistemic uncertainty they inhe

LACE: Latent Visual Representation for Cross-Embodiment Learning

arXiv:2605.16743v1 Announce Type: new Abstract: Cross-embodiment learning from human demonstrations is hindered by the visual gap between human and robot embodiments. While self-supervised learning (S

Lance: Unified Multimodal Modeling by Multi-Task Synergy

arXiv:2605.18678v1 Announce Type: cross Abstract: We present Lance, a lightweight native unified model supporting multimodal understanding, generation, and editing for both images and videos. Rather t

Latent Action Control for Reasoning-Guided Unified Image Generation

arXiv:2605.16961v1 Announce Type: cross Abstract: Unified multimodal models can encode visual understanding and image generation within a shared backbone, yet understanding does not automatically tran

LatentUMM: Dual Latent Alignment for Unified Multimodal Models

arXiv:2605.17766v1 Announce Type: new Abstract: Unified multimodal models (UMMs) achieve strong performance in both understanding and generation by learning a shared latent space, yet they often exhib

Learning Fill-in Reduction Ordering via Graph Policy Optimization for Sparse Matrices

arXiv:2605.17362v1 Announce Type: new Abstract: Matrix reordering in large sparse solvers seeks a permutation that minimizes factorization fill-in to reduce memory and computation. Because the minimum

Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning

arXiv:2605.17058v1 Announce Type: new Abstract: The combination of exponentially large action spaces, stochastic dynamics, and long-horizon decision-making under limited resources makes Sequential Sto

Learning Native Continuation for Action Chunking Flow Policies

arXiv:2602.12978v2 Announce Type: replace-cross Abstract: Action chunking enables Vision Language Action (VLA) models to run in real time, but naive chunked execution often exhibits discontinuities at

Learning Relative Representations for Fine-Grained Multimodal Alignment with Limited Data

arXiv:2605.16834v1 Announce Type: cross Abstract: Multimodal pre-training demonstrates strong generalization performance, but this paradigm is often impractical in domains where paired data are scarce