
Developed and introduced the SIRBench-V1 benchmark within the thunlp/SIR-Bench repository, enabling robust evaluation of large language models on scientific inductive reasoning tasks spanning biology and chemistry. Leveraged the OpenCompass framework and Python to design seven distinct tasks that emphasize inferring scientific rules from examples, moving beyond traditional equation-based assessments. Enhanced project maintainability by refining documentation, clarifying installation and API key configuration, and streamlining CI/CD workflows using YAML and Markdown. These improvements facilitated easier onboarding and collaboration for contributors, while the technical approach ensured reproducibility and scalability for future LLM evaluation and scientific reasoning research within the repository.
In Sep 2025, delivered core SIRBench-V1 benchmark introduction and supporting documentation/CI enhancements for SIR-Bench, enabling robust evaluation of LLMs on scientific inductive reasoning and improving onboarding and maintainability.
In Sep 2025, delivered core SIRBench-V1 benchmark introduction and supporting documentation/CI enhancements for SIR-Bench, enabling robust evaluation of LLMs on scientific inductive reasoning and improving onboarding and maintainability.

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