Tabular foundation models (TFMs) achieve strong performance through in-context learning, but context-dependent inference imposes substantial latency and memory costs, hindering large-scale deployment. We propose GEAR (\emph{Generative Expansion and Real Anchoring}), a modular two-stage framework that distills TFMs into...
Large Language Models (LLMs) are increasingly used for agent-based virtual cell modeling, yet existing frameworks rely on unstructured retrieval or static prompt engineering, injecting noisy evidence and wasting inference budget on redundant tool-use trajectories. We propose VCAgent, a self-evolving framework that opti...
Zhiyun Li, Rong Han, Xiao-Yong Wang et al.· Proceedings of the 32nd ACM...· 0 citations
VCAgent is proposed, a self-evolving framework that optimizes how biological knowledge from external APIs is structured and integrated into LLM reasoning, and introduces a schema-based slot filling mechanism that transforms raw API responses into compact, typed representations, replacing unstructured text concatenation...
Zhiyun Li, Rong Han, Xiao-Yong Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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