# Knowledge-Augmented Large Language Model Agents for Explainable Financial Decision-Making

arXiv: https://arxiv.org/abs/2512.09440 · HTML: https://mlsanity.com/paper/2512.09440/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection](https://mlsanity.com/paper/2310.11511/) (2023)
- [ExpeL: LLM Agents Are Experiential Learners](https://mlsanity.com/paper/2308.10144/) (2023)
- [Augmented Language Models: a Survey](https://mlsanity.com/paper/2302.07842/) (2023)
- [Agent-SafetyBench: Evaluating the Safety of LLM Agents](https://mlsanity.com/paper/2412.14470/) (2024)
- [FinanceBench: A New Benchmark for Financial Question Answering](https://mlsanity.com/paper/2311.11944/) (2023)
- [Large Language Models in Finance: A Survey](https://mlsanity.com/paper/2311.10723/) (2023)
- [AIOS: LLM Agent Operating System](https://mlsanity.com/paper/2403.16971/) (2024)
- [A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges](https://mlsanity.com/paper/2406.11903/) (2024)
- [AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System](https://mlsanity.com/paper/2402.15538/) (2024)
- [DictAS: A Framework for Class-Generalizable Few-Shot Anomaly Segmentation via Dictionary Lookup](https://mlsanity.com/paper/2508.13560/) (2025)
- [Collaborative Evolution of Intelligent Agents in Large-Scale Microservice Systems](https://mlsanity.com/paper/2508.20508/) (2025)
- [Autonomous Resource Management in Microservice Systems via Reinforcement Learning](https://mlsanity.com/paper/2507.12879/) (2025)
- [Integrating Structure-Aware Attention and Knowledge Graphs in Explainable Recommendation Systems](https://mlsanity.com/paper/2510.10109/) (2025)
- [Dynamic Topic Evolution with Temporal Decay and Attention in Large Language Models](https://mlsanity.com/paper/2510.10613/) (2025)

## Built on by (most cited first, 3 of 3)

- [Structured Prompt Optimization for Few-Shot Text Classification via Semantic Alignment in Latent Space](https://mlsanity.com/paper/2602.23753/) (2026)
- [Coordinated Semantic Alignment and Evidence Constraints for Retrieval-Augmented Generation with Large Language Models](https://mlsanity.com/paper/2603.04647/) (2026)
- [Unsupervised Graph Modeling for Anomaly Detection in Accounting Subject Relationships](https://mlsanity.com/paper/2604.26216/) (2026)

## Abstract (arXiv)

This study investigates an explainable reasoning method for financial decision-making based on knowledge-enhanced large language model agents. To address the limitations of traditional financial decision methods that rely on parameterized knowledge, lack factual consistency, and miss reasoning chains, an integrated framework is proposed that combines external knowledge retrieval, semantic representation, and reasoning generation. The method first encodes financial texts and structured data to obtain semantic representations, and then retrieves task-related information from external knowledge bases using similarity computation. Internal representations and external knowledge are combined through weighted fusion, which ensures fluency while improving factual accuracy and completeness of generated content. In the reasoning stage, a multi-head attention mechanism is introduced to construct logical chains, allowing the model to present transparent causal relationships and traceability during generation. Finally, the model jointly optimizes task objectives and explanation consistency objectives, which enhances predictive performance and reasoning interpretability. Experiments on financial text processing and decision tasks show that the method outperforms baseline approaches in accuracy, text generation quality, and factual support, verifying the effectiveness of knowledge enhancement and explainable reasoning. Overall, the proposed approach overcomes the limitations of traditional models in semantic coverage and reasoning transparency, and demonstrates strong practical value in complex financial scenarios.