Towards Reliable Financial Agents: Mitigating Financial Hallucinations through Evidence-Grounded Execution and Verified Experience

Despite rapid progress in LLM capabilities, constructing financial agents remains a long-standing challenge due to hallucinations caused by evidence misalignment, context noise accumulation, and unreliable experience reuse. To address these issues, we introduce FinHarness, a financial agent framework that improves workflow reliability by integrating task-adaptive execution, candidate evidence verification, and verified skill accumulation. Extensive experiments show that FinHarness consistently enhances financial reasoning across various LLM backbones on the professional financial reasoning benchmark, achieving gains of up to 19.89% over state-of-the-art baselines while reducing the hallucination rate among erroneous responses by up to 73.23%. Moreover, incorporating accumulated skills further increases FinHarness's financial reasoning score from 41.86% to 52.08%. To evaluate cross-task generalization, we transfer FinHarness and the accumulated skills to the out-of-domain stock-trading task, where the skill-augmented framework outperforms all training-free baselines. These results show that evidence-aligned financial agent workflows can improve financial reasoning while reducing financial hallucinations across execution, context construction, and experience reuse.