FinAutoRubric: Expert-Guided Automatic Rubric Gener... | AI Research

What the paper is about

Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on fixed, per-item rubrics, which are costly to extend and cannot encode each institution's own standard. In FinAutoRubric, experts specify reusable evaluation guidance, while agents and code carry out query-specific rubric generation, review, and validation. This expert guidance governs every agent, as prompts and as rules that code enforces, and a Task Bank of reusable criteria carries it across tasks. In long-horizon loops that follow the expert guidance, a writer agent researches every expected value and a reviewer agent verifies it, and failures escalate to a human. On three expert-authored finance benchmarks, its rubrics track expert scoring as closely as the strongest evaluated generator while stating the expert rubric's expected value for more criteria, their scores agree with human grading, and in-house analysts prefer them in a blind review. The released 100-query FinAutoRubric Benchmark, built from in-house analysts' key questions across 78 tasks and eight asset classes, shows that rubrics from an earlier model generation still leave headroom for a later one.

What it covers

Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on fixed, per-item rubrics, which are costly to extend and cannot encode each institution's own standard. In FinAutoRubric, experts specify reusable evaluation guidance, while agents and code carry out query-specific rubric generation, review, and validation. This expert guidance governs every agent, as prompts and as rules that code enforces, and a Task Bank of reusable criteria carries it across tasks. In long-horizon loops that follow the expert guidance, a writer agent researches every expected value and a reviewer agent verifies it, and failures escalate to a human. On three expert-authored finance benchmarks, its rubrics track expert scoring as closely as the strongest evaluated generator while stating the expert rubric's expected value for more criteria, their scores agree with human grading, and in-house analysts prefer them in a blind review. The released 100-query FinAutoRubric Benchmark, built from in-house analysts' key questions across 78 tasks and eight asset classes, shows that rubrics from an earlier model generation still leave headroom for a later one.
--> Computer Science > Artificial Intelligence arXiv:2609.35744 (cs) [Submitted on 28 Sep 2026] Title: FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents Authors: Hoyoung Lee , Suyeol Yun , Jack Haverty , Yunju Cho , Meesong Kim , Daekyung Park , Sumin Kim , Jihoon Kwon , Jasmine Jia Geng , Andrew Chin , Yin Luo , Edward Tong , Yu Yu , Zach Golkhou , Minkyu Kim , Igor Halperin , Young Cha , Alejandro Lopez-Lira , Chanyeol Choi , Yongjae Lee View a PDF of the paper titled FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents, by Hoyoung Lee and 19 other authors View PDF Abstract: Evaluating finance research agents requires rubrics that reflect expert standards and fix the values correct as of an information cutoff. Expert-reviewed finance benchmarks rely on fixed, per-item rubrics, which are costly to extend and cannot encode each institution's own standard. In FinAutoRubric, experts specify reusable evaluation guidance, while agents and code carry out query-specific rubric generation, review, and validation. This expert guidance governs every agent, as prompts and as rules that code enforces, and a Task Bank of reusable criteria carries it across tasks. In long-horizon loops that follow the expert guidance, a writer agent researches every expected value and a reviewer agent verifies it, and failures escalate to a human. On three expert-authored finance benchmarks, its rubrics track expert scoring as closely as the strongest evaluated generator while stating the expert rubric's expected value for more criteria, their scores agree with human grading, and in-house analysts prefer them in a blind review. The released 100-query FinAutoRubric Benchmark, built from in-house analysts' key questions across 78 tasks and eight asset classes, shows that rubrics from an earlier model generation still leave headroom for a later one. Comments: preprint Subjects: Artificial Intelligence (cs.AI) ; Computational Finance (q-fin.CP) Cite as: arXiv:2609.35744 [cs.AI] (or arXiv:2609.35744v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2609.35744 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Hoyoung Lee [ view email ] [v1] Mon, 28 Sep 2026 17:55:06 UTC (659 KB) Full-text links: Access Paper: View a PDF of the paper titled FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents, by Hoyoung Lee and 19 other authors View PDF TeX Source view license Current browse context: cs.AI The ai agents story also surfaces in Arm unveils AI-native mobile platform for..., adding another angle.
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