Personalized Auto-Research: Towards a True AI Co-Scientist

Key points

• Problem: Current AI co-scientists are researcher-agnostic: given the same goal, they produce the same distribution of outputs whether the requester is a first-year doctoral student or a senior professor. The authors trace this to an objective that optimizes quality only against the goal, literature, evidence, or reviewer model, and to scientist-in-the-loop systems that take only manual, session-level guidance.
• Input and output: This is a position paper that offers a problem definition, Algorithm 1, and a research agenda. Algorithm 1 takes a research goal, an initial workspace, a literature index, a research graph, and the researcher's signals (papers, citations, code, review history, venue preferences, explicit constraints). It returns several personalized research packages, each holding the hypothesis, its personalized score, code, outputs, figures, paper, automated review, provenance log, and the context and evidence that conditioned the run.
• Method: Algorithm 1 adds a researcher u to the existing agentic tree search loop with an experiment manager, code execution, figure refinement, manuscript writing, and automated review. A graph encoder builds the researcher representation z_u, a context encoder builds c_u, and retrieval picks the top k documents by goal and researcher jointly. Hypotheses are ranked by a weighted sum U(h|g,u) of personalized novelty, relevance, and feasibility instead of a binary, global novelty filter.
• Argument: The authors hold that publications alone give a thin description of a researcher, and propose learning the researcher representation from the researcher's position in a research graph with researcher, paper, venue, institution, method, dataset, and topic nodes and co-authorship, citation, publication, affiliation, usage, and topic-assignment edges. They see the most valuable direction as often a structural hole that bridges the researcher's region to a nearby region not yet connected. Pipeline personalization separates exploitation of the researcher's trajectory from exploration that keeps the candidate space broad, personalizing the how without narrowing the what.
• Evaluation proposal: The authors argue that predicting a researcher's next papers penalizes recommendations better than what the researcher pursued, and propose combining feasibility alignment, blinded expert assessment, and multi-year impact. As a scalable necessary condition, they propose a held-out protocol that builds c_u from the record before time t and measures fidelity (recovering the hypothesis and experimental path the researcher pursued) and contrast (different contexts diverging on the same concept).
• Limits: The authors list open problems: a cold start in which the objective itself changes with career stage, a social-choice problem when member preferences are aggregated into one team context, and harder multi-party privacy. The held-out protocol only confirms that c_u carries researcher-specific signal, and the authors state that judging the value of a recommended direction needs expert panels, longitudinal studies, or counterfactual designs.

Paper links

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