World-action models jointly predict actions and future world states, but long-horizon manipulation further requires language reasoning to plan subtask objectives, assess execution progress, and guide action selection. We present LAW, a closed-loop VLM--WAM framework that unifies language reasoning, action generation, a...
Jia-Jun Cao, Kai Tang, Bo-Hua Zhang et al.· 0 citations
This work introduces a shared visual action interface, called action views, which render target joint configurations from four prescribed virtual cameras using URDF-based forward kinematics, and proposes a training-free, URDF-constrained multiview recovery mechanism, without a learned embodiment-specific decoder.
Xiang-Yu Zhu, Jin Xu, Yue (Sophie) Guo et al.· 0 citations
In this technical report, we propose Pelican-Sim 1.0, a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions to support downstream learning and decision making. The model incorporates four key design features: (1) Unified action representation:...
Shilong Zou, Shi-Lin Zhang, Yingji Zhang et al.· 1 citation
This work proposes TrustRoboReward, a multi-paradigm reward modeling framework equipped with Preference-Ordered Isotonic Score Editing (POISE), and constructs a unified four-paradigm dataset with trajectory progress scoring, video-QA answer quality scoring, and their pairwise counterparts.
Yi-Dong Wang, Yan Zhan, Ziteng Feng et al.· 0 citations
Recent unified open-vocabulary detection (OVD) supports heterogeneous prompts, including text queries, visual exemplars, and their combinations, but often rely on increasingly complex designs such as heavy cross-modal fusion, staged training, and iterative annotation pipelines. We revisit whether such complexity is nec...
Xiao-Yan Wei, Zhi-Min Yao, Rui-Lin Yang et al.· 0 citations
VLA-Trace is presented, a progressive diagnostic framework that analyzes VLA models through a unified evidence chain from representation dynamics to causal control attribution and behavioral manifestation to highlight future directions for representation-preserving adaptation, causal VLA circuits, and compositional sem...
Thinking-Once is proposed, a training-free, single-visual-pass evidence-routing method that reconstructs question-conditioned attention at this window, preserves core entity tokens and compact background context, and routes this evidence to later layers without extra visual encoding.
Z. Mao, Xian-Jie Liu, Tian-Yu Meng et al.· arXiv.org· 0 citations
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