Position: AI Agents in Scientific Teams Should Be Studied as Human-Agent Systems

Key points

• Problem: LLM-based agents are entering scientific work as collaborators, yet most AI Scientist research focuses on growing agent autonomy and limits human participation to setting goals and verifying work. The authors argue that this overlooks the social side of scientific teamwork and that this literature has largely been isolated from the socio-technical realities of science.
• Argument: This is a position paper arguing that studying AI Scientists as human-agent systems (HAS), where the unit of analysis is the human-agent pair, is underexplored and undervalued, and that new HAS research is needed to understand human-AI synergy. The authors argue that the HAS lens can help better understand and potentially mitigate near-term risks, citing 100 confirmed hallucinated citations found in 51 accepted NeurIPS 2025 papers and an expert review that found noticeable plagiarism in 36% of LLM and AI Scientist-generated papers. They also cite a study in which AI-augmented research produced three times as many papers with a 5% drop in the diversity of explored topics.
• Taxonomy: The authors group AI Scientist systems by the main channel through which human feedback enters, namely artifact-level, discovery-phase-level, research-plan-level, and asynchronous steering and interruption, and tag each with Zou et al.'s feedback type (evaluative, guidance, corrective), granularity, and phase. The review finds that most systems give humans supervisory roles such as scoping, auditing, and approval, that artifact-level feedback is the most common, and that only a handful support flexible asynchronous interaction.
• Evidence: In a preliminary experiment with AstaBench's ReAct (an agent style that alternates reasoning and acting) agent harness and GPT-5-mini on 10 End-to-End Discovery (E2E) development tasks, adding an ask user tool and the feedback taxonomy to the prompt produced 1 question, and the E2E score went from 0.39 to 0.36. Forcing a question before every action raised questions to 92, and in the average over three LLM judges 0.71 of them were evaluative and 0.04 corrective, with an E2E score of 0.24. In this experiment an automatic continuation prompt, the AstaBench default, answered the questions instead of a human.
• Cases and proposal: In one published case, Gemini 3 Pro produced a plan (plan.md) and a draft manuscript (paper.tex) from an initial prompt and then expanded proofs and exposition over eight prompt turns, while the expert pointed out missing prior work and corrected a wrong assumption in a lemma. In another, GPT-5 drafted the PDE (partial differential equation) and numerical workflow for a reduced physics model of thermonuclear burn, while the expert tuned the conditions by hand and judged when outputs were still noise. The authors propose a toy model in which team utility is the collaboration advantage CA (how much the team beats its best member) minus the collaboration disadvantage CD (communication and coordination cost).
• Limits: The preliminary experiment used 10 development tasks with an automatic prompt answering the questions, which the authors defend as a deep rather than broad measure because each task is a full idea-to-report workflow. The case studies are qualitative readings of two published accounts, and the toy model and proxy metrics such as Review Time, Intervention Density, and Simulated Oracle Synergy are proposed as directions for future work. The authors address the opposing view that the HAS lens may add little once evaluation costs are counted, and acknowledge that centaur evaluations comparing HAS with humans alone and agents alone are currently expensive and hard to reproduce.

Paper links

External research summaries. These are not HDATF publications or measured product results.