CADS: Measuring the Incremental Contribution of LLM Proposers in Sequential Biological Screens
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.
Related
- The Shape of Biological Metadata: Measuring Repository Richness with Entity-Based NLP Metrics
- Do LLM Proposers Improve Budgeted Search for Ionizable Lipids?
- Clinical-Anchored Incremental Survival Learning: A Stability-Constrained Framework for Molecularly Enhanced Survival Prediction in Lung Adenocarcinoma
- ACOR: incremental evidence accumulation towards real-time DNA storage reconstruction
- Reassessing the contribution of the histone H3 tail to KRAB-DNMT3L-mediated epigenetic silencing
BibTeX
@misc{chen2026cads,
title = {{CADS: Measuring the Incremental Contribution of LLM Proposers in Sequential Biological Screens}},
author = {Guanxing Chen and Nihui Shao and Haohuai He and Zhenchao Tang and Pengwei Hu and Zhi-An Huang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.24597},
eprint = {2610.24597},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.24597},
}
RIS
TY - UNPB
TI - CADS: Measuring the Incremental Contribution of LLM Proposers in Sequential Biological Screens
AU - Guanxing Chen
AU - Nihui Shao
AU - Haohuai He
AU - Zhenchao Tang
AU - Pengwei Hu
AU - Zhi-An Huang
PY - 2026
DA - 2026/10/17
PB - arXiv
DO - 10.48550/arxiv.2610.24597
UR - https://arxiv.org/abs/2610.24597
AB - 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.
ER -