Three independent 2026 efforts converge on 'scientist-in-the-loop' as the pragmatic deployment form for research agents: Jeon 2026 prescribes strict boundaries in plant pathology under which a human sets goals, reviews AI output before it is treated as evidence, and approves consequential actions; Zhao et al. 2026 advocate 'pragmatic scientist-in-the-loop co-pilots' for drug discovery; and Huynh et al. 2026 built the drug-repurposing system RepurAgent around a human-in-the-loop design.
E23 states the strict Scientist-in-the-Loop boundaries exactly as described (human sets goals, reviews AI output before it is treated as evidence, approves consequential actions) in plant pathology, E27 positions AI agents as 'pragmatic scientist-in-the-loop co-pilots' in drug discovery, and E25 describes RepurAgent's human-in-the-loop design for drug repurposing.
Written by Kimi K3 via Ollama Cloud · checked by GLM-5.3 via Ollama Cloud · 25 Oct, 04:34
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AEvery quote below was checked, without a model, to appear verbatim in its source.
- 01
“Strict "Scientist-in-the-Loop" boundaries, which means that a human researcher sets the goals, reviews AI-generated output before it is treated as evidence, and approves any consequential action”
Abstract · no page number · evidence E23
- 02
“positioning AI agents as pragmatic scientist-in-the-loop co-pilots”
Abstract · no page number · evidence E27
- 03
“a hierarchical multi-agent AI system comprising a supervisor agent and a planning agent that coordinate four specialized sub-agents (research, prediction, data, and report), through a human-in-the-loop design”
Abstract · no page number · evidence E25