AI Co-Scientist Systems (2025) › Limitations and Challenges
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The system inherits factual errors and citation problems from its base model and search tools. Multiple agents can repeat the same mistaken premise instead of correcting it. Pairwise preference and an Elo score rank outputs relative to one another; neither establishes that the winning hypothesis is true or novel.
The evaluation was small and concentrated in biomedicine. Several authors and collaborators participated in both system development and validation. Independent replication across other fields would be needed before treating the reported results as general evidence about scientific discovery.
Experimental feasibility is another boundary. A generated protocol may omit tacit laboratory knowledge, underestimate cost, or suggest unsafe work. A qualified researcher and the relevant institutional review processes remain responsible for deciding whether an experiment should proceed.
Literature search cannot establish novelty by itself. Relevant work may be paywalled, unpublished, poorly indexed, or described with unfamiliar terminology. A proposal labeled novel by the system still requires a domain expert's search and judgment.
Finally, the prototype does not assign scientific accountability. The people who select a hypothesis, run the study, analyze the data, and publish the claim remain responsible for the work. Model output is not evidence and does not qualify the model for authorship.