titleFinanceHarness: Autonomous Financial Deep Research Framework
authorsYijia Xiao, Rujun Han, Yanfei Chen, Zifeng Wang, Ke Jiang, Zhongying CuiZhu, Vishy Tirumalashetty, Wei Wang, Burak Gokturk, Tomas Pfister, Chen-Yu Lee
date2026-08-07
venuearXiv (preprint, not peer reviewed)
idarXiv:2607.27853
upvotes11 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractPowered 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. With the same open-weight backbone, FinanceHarness improves the overall rubric score from 25.3% to 32.4%, demonstrating the effectiveness of our specialized harness design. However, even pairing FinanceHarness with the most cutting edge LLM (e.g. Opus-5), the FinanceGym score is below 45%, showing that it is a challenging benchmark for financial deep research. Leaderboard is available at: https://financegym.github.io/ and FinanceHarness code is available at: https://github.com/Yijia-Xiao/FinanceHarness.

cite

@misc{xiao2026financeharness,
  title={FinanceHarness: Autonomous Financial Deep Research Framework},
  author={Yijia Xiao and Rujun Han and Yanfei Chen and Zifeng Wang and Ke Jiang and Zhongying CuiZhu and Vishy Tirumalashetty and Wei Wang and Burak Gokturk and Tomas Pfister and Chen-Yu Lee},
  year={2026},
  eprint={2607.27853},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2607.27853}
}