CADS: Measuring the Incremental Contribution of LLM Proposers in Sequential Biological Screens
We present Certified Agentic Discovery Screens (CADS), an error-controlled measurement framework that quantifies a proposer's incremental value against an identical LLM-free loop.
ProofPaper ↗
Key points
- Large language models are increasingly used to guide sequential biological screens, yet standard evaluations rely on raw hit counts that conflate a proposer's value with the discovery capacity of the underlying algorithmic harness.
- Adaptive sampling and corrupted experimental readouts also leave standard feedback comparisons without a false-discovery guarantee.
- By integrating capped likelihood-ratio bounds, e-processes, and complete-family e-BH, CADS controls the terminal false discovery rate under the stated null and corruption conditions despite arbitrary dependence, adaptive sampling, and optional stopping.
- In DepMap-backed simulations, CADS exposes two evaluation artifacts: the LLM-free harness alone clears the feedback gate, and raw origin counts overstate LLM contributions because LLM picks displace fallback discoveries.
Sources (1)
- [1]CADS: Measuring the Incremental Contribution of LLM Proposers in Sequential Biological ScreensarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 10:02 AM
We present Certified Agentic Discovery Screens (CADS), an error-controlled measurement framework that quantifies a proposer's incremental value against an identical LLM-free loop.
Large language models are increasingly used to guide sequential biological screens, yet standard evaluations rely on raw hit counts that conflate a proposer's value with the discovery capacity of the underlying algorithmic harness.
Extractive summary: sentences quoted from the sources.
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