Unleashing Scientific Reasoning for Bio-experimental Protocol Generation
  via Structured Component-based Reward Mechanism
Authors: name: Haoran Sun, name: Yankai Jiang, name: Zhenyu Tang, name: Yaning Pan, name: Shuang Gu, name: Zekai Lin, name: Lilong Wang, name: Wenjie Lou, name: Lei Liu, name: Lei Bai, name: Xiaosong Wang

The foundation of reproducible science lies in protocols that are precise,
logically ordered, and executable. The autonomous generation of these protocols
through natural language queries could greatly improve the efficiency of the
reproduction process. However, current leading large language models (LLMs)
often generate incomplete or inconsistent protocols, limiting their utility. To
address this limitation, we first introduce SciRecipe, a large-scale dataset of
over 12K structured protocols spanning 27 biological subfields and encompassing
both comprehension and problem-solving tasks. To further improve protocol
generation, we propose the "Sketch-and-Fill" paradigm, which separates
analysis, structuring, and expression to ensure each step is explicit and
verifiable. Complementing this, the structured component-based reward mechanism
evaluates step granularity, action order, and semantic fidelity, aligning model
optimization with experimental reliability. Building on these components, we
develop Thoth, trained through a staged Knowledge-to-Action process that
progresses from knowledge acquisition to operational reasoning and ultimately
to robust, executable protocol generation. Across multiple benchmarks, Thoth
consistently surpasses both proprietary and open-source LLMs, achieving
significant improvements in step alignment, logical sequencing, and semantic
accuracy. Our approach paves the way for reliable scientific assistants that
bridge knowledge with experimental execution. All data, code, and models will
be released publicly.