FinAcumen: Financial Multimodal Reasoning via Self-Evolving Experience Memory Harness
Quick Answer
FinAcumen is a financial reasoning agent that enhances multimodal reasoning by utilizing selective experience memory, outperforming specialized models and proprietary systems across four benchmarks.
Quick Take
It improves reasoning reliability under uncertainty by conditioning on relevant past experiences, leading to more accurate financial decision-making.
Key Points
- FinAcumen accumulates reasoning experiences to build a persistent memory bank.
- The framework selectively activates relevant memories to enhance reasoning accuracy.
- It consistently outperforms finance-specialized models and leading proprietary systems.
- A deterministic tool environment grounds numerical computation and answer verification.
- The code is available at an anonymous repository for further research.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2606. 17642v1 Announce Type: new 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 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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