Foundation models are driving a paradigm shift in robotic intelligence, moving beyond low-level action prediction to goal-directed orchestration of data collection, model training, task planning, and real-world execution. We characterize this emerging paradigm as an agentic robots, in which agents reason over human ins...
Chonghan Qin, Dong-Ming Wu, Kai-Tuo Feng et al.· 0 citations
Streaming video LLMs must retain evidence before its relevance to future tasks is known and respond when sufficient evidence becomes available. The challenge is to form reusable factual memory without compromising real-time perception. We introduce OneStreamer, which jointly learns query-independent evidence recording...
Xiang-Yun Zeng, Yuan-Dong Yang, Zhi-Qiu Zhang et al.· 1 citation
GUI agents require high-quality interaction trajectories to learn how software environments respond to actions, maintain state, and support multi-step workflows. However, the diversity of available trajectories is constrained by the applications, interface states, and workflows accessible in the underlying environments...
Cheng Yang, Yi-Fan Wu, Yu-Tao Huang et al.· 0 citations
This work proposes Counterfactual Evidence Disentanglement (CED), a training-time evidence audit for VLM grounding, which outperforms prior RL-based post-training methods, with targeted analyses verifying its object-centric signal.
Haojie Huang, Xin-Lei Yu, Cheng-Ming Xu et al.· 3 citations
The results chart an engineering path toward recursive self-improvement, in which humans distill the principles of model building, agents operationalize them through evidence-driven experimentation, and each generation of AI becomes a more capable architect of the next.
Cheng Yang, Jiayang Lyu, Shang Liu et al.· 0 citations
The results support OPD as preparation for RL and favor forward KL when OPD is followed by RL in the authors' comparison, which highlights the importance of both objective choice and the states receiving supervision for subsequent RL.
Human evaluation confirms that RefCaptioner achieves the best overall performance among the open-source models while remaining competitive on standard video captioning benchmarks, and human evaluation further confirms that its captions are preferred by annotators and enable more source-faithful video reconstruction wit...
Teng-Fei Liu, Yang Shi, Yu-Ran Wang et al.· arXiv.org· 0 citations
Beacon, a novel agentic visual reasoning model trained with supervised fine-tuning and reinforcement learning, is proposed, which achieves the highest average score among the evaluated open-source models and ranks first on 11 benchmarks.
Qi-Xun Wang, Yang Shi, Le-Tian Cheng et al.· arXiv.org· 1 citation
LaMem-VLA is introduced, a latent-memory-native framework that reconstructs historical experience into latent memory tokens and directly interweaves them with VLA reasoning, and enables memory to directly participate in VLA reasoning and guide action generation under a bounded context.
Hongyu Qu, Jianzhe Gao, Xiaobin Hu et al.· 1 citation
This work introduces Latent On-Policy Self-Distillation (LOPD), which, rather than proposing another hand-crafted OPSD variant with a newly prescribed form of privileged context, makes the teacher's privileged context itself learnable end-to-end from experience.
Gui-Min Zhang, Jiayang Lyu, Ran Sun et al.· 6 citations
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