小型模型本地部署执行药物发现规划，Llama 3.2-3B 工具选择 F1 达 0.998
Toward a Locally Deployable Agentic Co-Scientist: Small-Model Planning for Early-Stage Drug Discovery
本地小模型也能当药物发现的规划器了，Llama 3.2-3B 微调后工具调用准得吓人，就是碰到没见过的流程还会翻车。
一篇 arXiv 论文提出轻量级工具增强框架，让可本地部署的紧凑语言模型规划调用 18 个模块化工具，用于早期计算药物发现。团队构建了 1,263 条人工精修的查询-规划对，并对三个紧凑模型家族做 LoRA 微调。在查询级切分下，Llama 3.2-3B 的工具选择 F1 达 0.998，序列精确匹配 0.979，参数 F1 0.960。在更严格的 workflow-grouped 切分下，序列精确匹配降至 0.452 到 0.548，说明紧凑模型在生成训练时未见的完整工作流路径上仍有困难。
Toward a Locally Deployable Agentic Co-Scientist: Small-Model Planning for Early-Stage Drug Discovery
Early-stage computational drug discovery requires coordinating heterogeneous scientific tools across multi-step workflows. We present a lightweight, tool-augmented framework in which a locally deployable compact language model plans calls to 18 modular tools. A Unified Molecular Schema maintains shared molecular records, while a plug-in interface supports tool replacement and extension. We construct 1,263 manually refined query-plan pairs through workflow-graph path coverage and apply LoRA fine-tuning to three compact model families. Under the query-level split, all fine-tuned models generate fully parseable and schema-compliant plans on 47 held-out cross-group queries. Llama 3.2-3B achieves a tool-selection F1 of 0.998, sequence exact match of 0.979, and argument F1 of 0.960. Under the stricter workflow-grouped split, which excludes identical ordered tool sequences across partitions, sequence exact match reaches 0.452 to 0.548, highlighting the remaining difficulty for compact models in generating complete workflow paths unseen during training. These results demonstrate the feasibility of compact, locally deployable planning while identifying compositional generalization as an important direction for further improvement.
