FinanceHarness：自主金融深度研究框架

摘要

受大型語言模型與自主智能體進展之推動，深度研究已成為最廣泛採用的智能體產品之一。然而，多數深度研究系統撰寫的是通用型報告，此類報告不足以應付金融深度研究。金融研究需要專業知識來分析歷史模式並預測未來事件。因此，自動化金融深度研究既需要一個分層式框架來驅動研究智能體，也需要一個可驗證、防止未來資訊洩漏的時點基準。我們提出了 FinanceHarness，這是一個運行金融導向工具與從業者引導工作流程的框架，可端到端自動化金融深度研究：包括環境與資料建構、智能體執行迴圈，以及獎勵建模。我們進一步提出 FinanceGym，其中包含以論點驅動的研究問題，以及結合截止前與截止後標準的評分量表。專業專家驗證產生了 82% 的通過率。即使是領先的大型語言模型與智能體，在評分量表上的得分也低於 40%，顯示 FinanceGym 具有挑戰性，並留有相當大的提升空間。在相同的開放權重基礎模型下，FinanceHarness 將整體評分從 25.3% 提升至 32.4%。FinanceHarness 可於 https://github.com/Yijia-Xiao/FinanceHarness 取得。

English

Powered by advances in LLMs and autonomous agents, deep research has become one of the most widely adopted agentic products. However, most deep research systems write general-purpose reports, which are inadequate for financial deep research. Financial research demands specialized knowledge to analyze historical patterns and forecast upcoming events. Automating financial deep research therefore requires both a layered harness to drive the research agent and a verifiable, point-in-time benchmark that prevents leakage of future information. We present FinanceHarness, a harness that runs finance-oriented tools and practitioner-guided workflows, automating financial deep research end to end: environment and data construction, the agent execution loop, and reward modeling. We further propose FinanceGym, comprising thesis-driven research questions and rubrics that combine pre-cutoff and post-cutoff criteria. Professional expert validation yields an 82% pass rate. Even leading LLMs and agents score below 40% on the rubrics, showing that FinanceGym is challenging and leaves substantial headroom. With the same open-weight backbone, FinanceHarness improves the overall rubric score from 25.3% to 32.4%. FinanceHarness is available at https://github.com/Yijia-Xiao/FinanceHarness.