FinanceHarness: Autonomous Financial Deep Research Framework
For financial analysts and AI researchers, this provides a specialized benchmark and harness to advance automated financial research, though it is domain-specific and incremental.
FinanceHarness introduces a framework for automating financial deep research, including a benchmark (FinanceGym) that prevents data leakage. Expert validation shows an 82% pass rate, while leading LLMs score below 40%, and using FinanceHarness improves rubric scores from 25.3% to 32.4%.
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.