This work proposes Counterfactual Relational Policy Optimization (CRPO), which explicitly trains Video LLMs to respond to controlled changes in the visual input, and introduces DyBench, a paired counterfactual benchmark with 3{,}014 videos and a strict pair-accuracy metric.
Da-Zhao Du, Jian Liu, Jia-Long Qin et al.· arXiv.org· 1 citation
A mobile MoE runtime that exploits the multi-token verification window of speculative decoding to decouple expert movement from single-token execution, enabling weight reuse, contiguous flash reads, and load-compute overlap.
Asynchronous Federated Learning (AFL) enhances the efficiency of edge collaborative learning systems by asynchronously aggregating client updates to prevent slowdowns from slow clients. However, in non-IID scenarios, such operations magnify uneven learning among various local data, degrading global model generalization...
Xiaoyi Pang, Yajie Zhou, Zhibo Wang et al.· IEEE Transactions on Mobile...· 0 citations
This work introduces CamChoreo, a benchmark of 4,229 real single-shot clips with expert-annotated temporal segments, and proposes CamDistill, which distills the same geometric knowledge into lightweight camera tokens during training and removes the 3D model at inference.
Da-Zhao Du, Shiyan Du, Jian Liu et al.· 0 citations
This work proposes Progressively Disentangled and Recurrent Prompt Tuning (PDRPT), an edge-efficient framework that decouples object and state updates before joint refinement, suppresses traction force from highly-entangled prompts, and preserves alignment with the natural language space of CLIP.
Xiaocheng Lu, Chuan He, Ziming Liu et al.· 0 citations
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