FinAutoRubric: Expert-Guided Automatic Rubric Generation for Evaluating Financial Research Agents

Paper: arXiv 2609.35744

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

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.

Complexity vs Empirical Score

• Math Complexity: 0.0/10
• Empirical Rigor: 0.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: Scoring failed: Could not find valid JSON in LLM response: line 1 column 1 (char 0)

Research Flowchart

  flowchart TD
    A[Research Goal: Evaluate Financial Research Agents with Expert-Standard Rubrics] --> B{Key Methodology Steps};
    B -- Expert Input --> C[Reusable Evaluation Guidance Specification];
    B -- Agent-Driven --> D[Query-Specific Rubric Generation, Review & Validation];
    C & D -- Governed by --> E[Task Bank of Reusable Criteria];
    E --> F[Computational Processes: Writer Agent Research & Reviewer Agent Verification];
    F -- Failures escalate to --> G[Human Intervention];
    G --> H[Key Findings/Outcomes];
    H --> I{Rubrics track expert scoring & improve coverage};
    H --> J{Scores agree with human grading & analyst preference};
    H --> K[FinAutoRubric Benchmark shows headroom for later models];

Cited References (71)

1. FrontierFinance: A Challenging Benchmark for Measuring Frontier Intelligence of Finance Agents (2026) — link
2. FinResearchBench II: A Deep Research Benchmark with Consensus-Derived Gold Rubrics for Distinguishing Financial Report Quality (2026) — link
3. Can LLMs Write Reliable Rubrics? A Meta-Evaluation for Experiment Reproduction (2026) — link
4. When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis (2026) — link
5. BigFinanceBench: A Workflow-Grounded Benchmark for Financial-Research Agents (2026) — link
6. PReMISE: Policy Rubrics as Measurement Specifications for LLM Judges (2026) — link
7. FinTradeBench: A Financial Reasoning Benchmark for LLMs — → 2603.19225
8. FinSheet-Bench: From Simple Lookups to Complex Reasoning, Where LLMs Break on Financial Spreadsheets (2026) — link

3 of 71 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.