tracking: LifeSciBench-inspired evaluation principles for CDS design review

[kkyungseo]

Idea

OpenAI introduced LifeSciBench, an expert-written and expert-reviewed benchmark for realistic life-science research tasks.

https://openai.com/index/introducing-life-sci-bench/

Relevant takeaways for FactorForge:

• Real-world life-science tasks require evidence handling, analysis, design/optimization, scientific reasoning, validation/operations, translation, and communication.
• Evaluation should not rely only on final-answer correctness; detailed rubrics are needed for scientific validity, caveats, formatting, and operational usefulness.
• Artifact-heavy and exact-output tasks remain difficult for frontier AI systems.
• Benchmark performance should not be treated as direct evidence of downstream research impact; live workflow and wet-lab validation remain necessary.

FactorForge is positioned as a constraint-based CDS design and pre-synthesis sequence review engine.

This suggests that FactorForge should continue to prioritize:

• deterministic sequence-level checks;
• reproducible design metadata;
• explicit validation boundaries;
• public-safe wet-lab feedback collection;
• benchmark scripts that test reviewability, not only optimization output;
• clear separation between AI-assisted explanation and deterministic sequence validation.

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Take a look when you have a chance.
I think this could offer useful reference points for shaping FactorForge’s benchmark design, validation boundaries, and AI-assisted review strategy.

Thanks!

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