Evaluating AI-Assisted Discovery Workflows Through Independent Replication and Reuse

Abstract

AI-assisted laboratories and research systems have demonstrated task-specific capabilities, but general scientific reliability requires independent replication, provenance and evidence that results can be reused. This paper proposes a workflow joining research allocation, replication, evidence portfolios and model-failure records. The central hypothesis is that the integrated workflow can reduce the time and total cost of reaching a useful independently verified finding. A proposed matched-resource pilot in a bounded nonclinical domain would compare conventional and assisted workflows, with component ablations. Quantum computation is optional and admissible only for tasks with defensible accuracy and total-cost advantages. No pilot result, general autonomous-scientist capability or broad quantum advantage is reported. The contribution is an evaluation-oriented integration proposal whose practical benefit remains unestablished and requires independent specialist review. ChatGPT (OpenAI), under the author’s direction, assisted with research, critical review, drafting, calculations, visual development and document preparation; the author retains responsibility.