Unleashing Scientific Reasoning for Bio-experimental Protocol Generation
  via Structured Component-based Reward Mechanism
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.
68f85e4d7669bcaeecce0da6
Haoran Sun
68f85e4d7669bcaeecce0da7
Yankai Jiang
68f85e4d7669bcaeecce0da8
Zhenyu Tang
68f85e4d7669bcaeecce0da9
Yaning Pan
68f85e4d7669bcaeecce0daa
Shuang Gu
68f85e4d7669bcaeecce0dab
Zekai Lin
68f85e4d7669bcaeecce0dac
Lilong Wang
68f85e4d7669bcaeecce0dad
Wenjie Lou
68f85e4d7669bcaeecce0dae
Lei Liu
68f85e4d7669bcaeecce0daf
Lei Bai
68f85e4d7669bcaeecce0db0
Xiaosong Wang
Unleashing Scientific Reasoning for Bio-experimental Protocol Generation
  via Structured Component-based Reward Mechanism
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.
Thoth, a large language model trained with the Sketch-and-Fill paradigm and structured component-based reward mechanism, generates more reliable and executable scientific protocols compared to existing models.
large language models SciRecipe Sketch-and-Fill structured component-based reward mechanism Knowledge-to-Action process step alignment logical sequencing semantic accuracy