RACaP is introduced, an agentic framework that moves coding to evolution and uses a Reasoning-and-Acting loop to call frozen, typed Policy APIs at deployment and shows that separating reusable code from runtime decisions supports continued evolution, effective transfer, and efficient long-horizon control.
Ze-Xi Li, Ye-Hang Zhang, Hao-Jian Huang et al.· 1 citation
A literature review around the interfaces connecting contextual evidence to execution, distinguishing four families: context-conditioned policies, geometric demonstration transfer, world-model-based control, and skill- and agent-based execution.
Hao-Jian Huang, Ze-Xi Li, Ju-Hao Guo et al.· 1 citation
Robo-Harness K1 is introduced, a robot-use agent (RUA) framework that exposes perception as tools that makes 3D geometry accessible without changing the VLM architecture or training a depth encoder, and suggests that perception-augmented RUAs offer a promising route to sample-efficient, generalizable robotic policies t...
Ze-Xi Li, Ye-Hang Zhang, Wen-Qian Li et al.· 2 citations
This work introduces personalized product search (PPS), a testbed for agentic personalization under raw queries and diverse histories, and constructs Agent Personalized Benchmark (APeB) from action logs, pairing underspecified intents with rich histories and user-viewed candidate items.
Gary Yang, Zi-Zhe Chen, Xinru Chen et al.· 0 citations
Hierarchical Graph Retrieval-Augmented Generation (GraphRAG) organizes corpus knowledge at multiple levels of granularity, yet fixed context construction may fail to translate these multi-resolution representations into a context suited to the current query. We identify this mismatch as the representation--inference ga...
Yong-Feng Huang, Yuren Lai, Rui-Ying Chen et al.· 0 citations
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