Labwright v0.1.0
Labwright — a verifiable wet-lab AI copilot. The LLM proposes raw inputs; deterministic calculators compute every derived number; a verifier re-proves each one (hard gate). A number you type is not trusted.
What's in v0.1.0
- Hard-gate architecture: goal → LLM proposes raw inputs → deterministic calculators (microfluidics + plate cell culture + dosing) → verifier re-proves every derived number → SOP + design JSON. Naive alternatives (type-the-numbers, soft self-check) rejected.
- Benchmark: on 24 reading goals — Labwright 88–100 % usable designs, 0.000 hallucination vs bare LLM 0–12 % usable, ~1.0 hallucination.
- Reverse-verification: same calculators check published protocols (kidney/lung) and catch unit mix-ups, order-of-magnitude errors.
- Extractor: fine-tuned Qwen2.5-1.5B goal → raw-inputs model (
json_parse_rate 1.0, consistency 0.998) + deterministic eval. - CLI:
labwright audit "…",labwright tools,labwright benchmark. - Python API + Gradio space demo, CI (165 tests), issue/PR templates.
Honest boundary
On blind goals the gate can't supply physiology the model doesn't know — usable drops, hallucination stays 0.000. The reported numbers come from the calculators' real physics/biology constants, each pinned to a citable source.
Full docs, figures, and the graphical abstract in the README.