Partially Relevant Video Retrieval (PRVR) seeks to retrieve untrim-med videos containing a moment that matches a text query, without temporal annotations. The relevant moment may last only seconds within a video spanning several minutes, creating an extremely low signal-to-noise ratio that makes PRVR more challenging t...
Zhen Liu, Le-Tian Li, Jinpeng Wang et al.· 0 citations
World Action Models (WAMs) combine visual dynamics modeling with action generation, but their high inference latency limits responsive robot control. Recent efforts accelerate inference by removing explicit future-video generation at test time, as in FastWAM, an approach that requires a specially tailored architectural...
Hua-Nan Liu, Ye Li, Kang-Ye Ji et al.· 0 citations
Robot foundation policies predict action chunks, but how many actions to execute before replanning depends on the current task phase. We introduce ChunkTrust, which treats the execution horizon as a latent variable inferred from action-expert evidence rather than a fixed hyperparameter. Its training-free Action-aware H...
Fan-Ding Huang, Jing-Yan Jiang, Shi-Feng Bao et al.· 0 citations
World Action Models (WAMs) augment robot action generation with future visual supervision. Existing WAMs commonly fuse main and wrist observations into one visual stream and train both with the same future-video objective, despite their different visual dynamics. A stable main camera reveals scene-level task evolution,...
Jie Wu, Yu-Zhi Huang, Jun-Qi Liu et al.· 0 citations
This work proposes SeekVLN, an evidence-seeking framework that couples semantic progress reasoning with active acquisition of task-relevant observations and introduces Counterfactual Contrastive Policy Optimization (C2PO) for reinforcement fine-tuning.
Zhi-Min Wang, Mei-Yuan Zhu, Duo Wu et al.· 0 citations
Humans perform long-horizon manipulation by retaining knowledge of what earlier actions have established while continuously adapting the motion underway. By contrast, action-chunked vision-language-action (VLA) policies repeatedly replan from the current input at each query. Existing methods preserve either long-term t...
Yuzhi Huang, Wei Bu, Ziyi Xiong et al.· 1 citation
TAU-Bench is introduced, a track-centric benchmark for jointly evaluating anomaly instance tracking and fine-grained anomaly understanding, and shows that models producing plausible anomaly interpretations may still fail to localize and track the correct instance reliably, revealing a persistent gap between semantic re...
Kepeng Yang, Dong-Xuan Liu, Rongxin Gao et al.· 1 citation
It is shown that TTA can increase confidence and reduce entropy even when the top-1 prediction and its correctness remain unchanged, a failure mode the authors term prediction-preserving sharpening, and proposed Zero-Shot-Anchored Entropy Calibration (ZAEC), a label-free post-hoc method that uses zero-shot entropy as a...
Jing-Yan Jiang, Yaru Sun, Xiao Chen et al.· 0 citations
GeniWorld is presented, an interactive world model for robots that generalizes robustly across unseen scenarios by explicitly decoupling embodiment kinematics from environmental dynamics, and generates diverse manipulation trajectories within the world model, improving downstream policy performance and robustness in co...
