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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

Portuguese

Grandes modelos de intervalo apropriado são cada vez mais usados para guiar telas biológicas sequenciais, no entanto as avaliações padrão dependem de contagens brutas que confundem o valor de um proponente com a capacidade de descoberta do loop algorítmico subjacente. Amostragem adaptativa e leituras experimentais corrompidas também deixam comparações padrão de feedback sem uma garantia de falsa descoberta. Nós apresentamos Certified Agentic Discovery Screens (CADS), um quadro de medição controlado por erro que quantifica o valor incremental de um proponente contra um loop LLM-free idêntico. Ao integrar limites de probabilidade-rata capped, e-processos e família completa e-BH, o CADS controla a taxa de descoberta falsa do terminal sob

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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