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CADS: Measuring the Incremental Contribution of LLM Proposers in Sequential Biological Screens

Guanxing Chen, Nihui Shao, Haohuai He, Zhenchao Tang, Pengwei Hu, Zhi-An Huang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.24597 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

大型言語の適切な間隔モデルはますます連続的な生物学的画面を指導するために使用されていますが、標準的な評価は、プロポーターの値をベースのアルゴリズムハーネスの発見能力と混同する原始ヒットカウントに依存しています。適応サンプリングと腐敗した実験的な読み取りは、誤発見の保証なしに標準的なフィードバック比較を残しています。我々はCADSを提供します Certified Agentic Discovery Screens(CADS)、これらのエラーコントロールされた測定フレームワークは、同一のLLMフリーループに対してプロポーターの増加値を量化します。カプペドの確率比比限、eプロセス、および完全なe-BHを統合

Machine translation (Meta M2M100); the English original follows.

Abstract

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. 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. 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. On the initial 20 seeds, supplying tested-candidate history to Qwen2.5-7B shifts its net contribution from +0.35, with a 95% interval covering zero, to +2.90, with the interval above zero. This initial net effect did not persist in a 50-seed matched-quota replication. These results support net gain over an LLM-free loop, measured between separately certified sets, as the appropriate measure of proposer contribution.

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Topics: Computer science, Measure (data warehouse), Interval (graph theory), Value (mathematics), Conflation, Certification

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