RubricAtlas: Mining Type-Level Rubrics from Expert Reports to Evaluate Deep Research Agents

Authors: Delin Wang , Sinuo Wang , Rui Sun , Beidi Luan , Qiming Shi , Jing Li , Daxin Jiang , Zuo Bai

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Abstract: Deep research agents conduct multi-step Web searches and synthesise retrieved information into analytical reports. Evaluating these outputs requires checking which analyses they deliver, beyond the presence of relevant facts. Query-specific binary rubrics make individual requirements explicit, but task-level checklists may leave the overall analytical structure underexamined. We introduce RubricAtlas, which derives type-level analytical requirements from expert reports on different objects within the same research type and organises detailed checks around recurring analytical questions. The requirements give related tasks a shared standard for what to analyse, without prescribing their answers. Our framework links binary checkpoints to report passages and evaluates completeness separately from argument validity. Built from 3,079 Chinese financial research reports, RubricAtlas covers 99 research types. Reference-report selection experiments show that individual reports cover only about half of their type's analytical questions, and that selecting requirements from different reports changes evaluation scores and agent rankings. Across ten agents, two judge pipelines achieve checkpoint agreement of $\kappa$ = 0.71 and agent-ranking agreement of Kendall $\tau$ = 0.87. Controlled deletions reduce scores on the affected analyses, while padding produces no detectable increase. These findings support type-level rubrics for comparing the analytical completeness of Web deep research outputs, with individual decisions traceable to report evidence.

Subjects:

Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL)

Cite as:

arXiv:2610.36878 [cs.AI]

(or

arXiv:2610.36878v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36878

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arXiv-issued DOI via DataCite (pending registration)

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From: Zuo Bai [ view email ]

[v1]

Tue, 27 Oct 2026 09:18:00 UTC (188 KB)

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