12 papers
ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation
Yu Sun, Meng Cao, Yang Ping +24
Vision-Language-Action (VLA) models and world-action models have emerged as central paradigms for general-purpose robotic intelligence, yet their empirical progress remains constra…
A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model
Kaidong Zhang, Jian Zhang, Rongtao Xu +20
Vision-Language-Action (VLA) models have emerged as a powerful paradigm for open-world robot manipulation, but their practical deployment is often constrained by cost: billion-scal…
Graph-PiT: Enhancing Structural Coherence in Part-Based Image Synthesis via Graph Priors
Junbin Zhang, Meng Cao, Feng Tan +2
Achieving fine-grained and structurally sound controllability is a cornerstone of advanced visual generation. Existing part-based frameworks treat user-provided parts as an unorder…
CARE What Fails: Contrastive Anchored-REflection for Verifiable Multimodal Reasoning
Yongxin Wang, Zhicheng Yang, Meng Cao +5
Group-relative reinforcement learning with verifiable rewards (RLVR) often wastes the most informative data it already has the failures. When all rollouts are wrong, gradients stal…
Order from Chaos: Physical World Understanding from Glitchy Gameplay Videos
Meng Cao, Haoran Tang, Haoze Zhao +6
Understanding the physical world, including object dynamics, material properties, and causal interactions, remains a core challenge in artificial intelligence. Although recent mult…
Ground-R1: Incentivizing Grounded Visual Reasoning via Reinforcement Learning
Meng Cao, Haoze Zhao, Can Zhang +3
Large Vision-Language Models (LVLMs) have become powerful general-purpose assistants, yet their predictions often lack reliability and interpretability due to insufficient groundin…