FinAcumen: Financial Multimodal Reasoning via Self-Evolving Experience Memory Harness

• URL: http://arxiv.org/abs/2606.17642v2
• Date: Mon, 22 Jun 2026 05:24:56 GMT
• Title: FinAcumen: Financial Multimodal Reasoning via Self-Evolving Experience Memory Harness
• Abstract summary: FinAcumen is a financial reasoning framework centered on selective experience memory for tool-augmented multimodal reasoning.<n>FinAcumen accumulates financially grounded reasoning experience from prior trajectories, distilling successful strategies and failure-derived cautionary rules into a persistent memory bank.<n>Across four financial multimodal reasoning benchmarks, FinAcumen consistently improves a frozen 8B vision-language model.
• Score: 8.305723007493903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Financial multimodal reasoning requires agents to coordinate numerical computation, retrieval, visual interpretation, and temporal grounding across heterogeneous evidence sources. Existing tool-augmented agents improve execution fidelity, yet remain largely stateless across episodes, repeatedly rediscovering reasoning strategies and failure patterns. In high-stakes financial settings, this leads to unreliable tool routing, noisy retrieval, and hallucination-prone reasoning. We present FinAcumen, a financial reasoning agent framework centered on selective experience memory for tool-augmented multimodal reasoning. FinAcumen accumulates financially grounded reasoning experience from prior trajectories, distilling successful strategies and failure-derived cautionary rules into a persistent memory bank. During inference, retrieved experiences condition reasoning only when semantic relevance exceeds a calibrated threshold, while irrelevant memory is explicitly suppressed through a fallback mechanism. A deterministic financial tool environment further grounds numerical computation, retrieval, visual decoding, and answer verification.Across four financial multimodal reasoning benchmarks, FinAcumen consistently improves a frozen 8B vision-language model over finance-specialized models and approaches leading proprietary general-purpose models. Further analysis shows that selective experience activation improves reasoning reliability under retrieval uncertainty. Our code is anonymously available at https://anonymous.4open.science/r/FinAcumen

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