FinHopper: A Progressive Multi-Hop Benchmark for Complex Financial Agents with Step-wise Evaluation

Yuxuan Wu; Miaopei Lin; Qiufang Ying; Lin Dan; Hao Zhao; Qirui Liu; Yuteng Shen; Shaohui Wu; Weihong Luo; Xiku Du; Xing Tang; Hao Chen

arXiv · 2610.26036

FinHopper: A Progressive Multi-Hop Benchmark for Complex Financial Agents with Step-wise Evaluation

Yuxuan Wu·Miaopei Lin·Qiufang Ying·Lin Dan·Hao Zhao·Qirui Liu·Yuteng Shen·Shaohui Wu·Weihong Luo·Xiku Du·Xing Tang·Hao Chen

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Abstract

Financial agents are expected to answer factual investment queries through open search, but financial multi-hop retrieval differs from general multi-hop QA in a crucial way: each intermediate answer is an exact typed constraint, such as an entity, date, event, or numerical indicator, that determines the next query. This strict answer-conditioned dependency makes small intermediate errors cascade into plausible but wrong search trajectories. We introduce FinHopper, a benchmark for complex financial agents with controlled reasoning depth from 1 to 6 hops, covering stocks, funds, and macroeconomic facts, with gold intermediate answers for process-level evaluation. To construct FinHopper at scale, we design a state-transition-based synthesis framework that composes verified atomic financial QA patterns into dependency-preserving chains, followed by targeted in-synthesis verification, model-ensemble checking, and public-search validation by financial experts. Experiments with 20 LLM-agent configurations show that public search tools are necessary but insufficient: performance drops sharply as hop depth increases, the best model remains far below human experts, and process-aware scoring reveals partial reasoning progress hidden by final-answer-only evaluation. The benchmark is available at https://github.com/justajustin/FinHopper/.

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Yuxuan Wu, Miaopei Lin, Qiufang Ying, Lin Dan, Hao Zhao, Qirui Liu, Yuteng Shen, Shaohui Wu, Weihong Luo, Xiku Du, Xing Tang, Hao Chen. 2026-10-19. FinHopper: A Progressive Multi-Hop Benchmark for Complex Financial Agents with Step-wise Evaluation. https://arxiv.org/abs/2610.26036

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