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Beyond Prompt-Based Planning: MCP-Native Graph Planning-based Biomedical Agent System

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arXiv:2606. 04494v1 Announce Type: new Abstract: Biomedical agents promise to automate complex biological workflows, yet current systems face two fundamental bottlenecks: bioinformatics tools are highly heterogeneous in interfaces and execution environments, while agent planning still relies on flat prompt-retrieved tool descriptions.

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Authors: Zhangtianyi Chen, Florensia Widjaja, Wufei Dai, Xiangjun Zhang, Yuhao Shen, Juexiao Zhou
Released: 2026-06-04 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Beyond Prompt-Based Planning: MCP-Native Graph Planning-based Biomedical Agent System overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, soft, eval, line, stage, candidate, capability. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.04494

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