Retrieval-Augmented Generation (RAG) mitigates factual hallucinations in Large Language Models but struggles with complex reasoning. Graph-based RAG (GraphRAG) addresses this gap yet can exhibit a failure pattern: semantic compression during triple extraction may reduce the preservation of fine-grained facts such as precise quantities, temporal constraints, and entity attributes. We formalize this limitation as the Fine-Grained Fact Retrieval (FGFR) cascade and introduce stage-wise diagnostic metrics, including R_rep, R_sel, and R_end, to quantify information loss. To stress-test FGFR, we construct a diagnostic evaluation suite from classic novels that provide controlled long-context scenarios containing explicit entity attributes and temporal constraints. F-GraphRAG, our framework, features a granularity-aware extractor that preserves atomic details and an information-preserving filter that counters global importance bias. On this evaluation suite, F-GraphRAG outperforms GraphRAG by 12.6% in accuracy while using 33.8% fewer relationships, and achieves gains of 5.3–11.2% across nine public benchmarks. These results suggest that improving evidence completeness can provide a more effective alternative to increasing graph connectivity.

Trading range breakout is a key method in the technical analysis of financial trading, widely employed by traders in financial markets such as stocks, futures, and foreign exchange. However, distinguishing between true and false breakout and providing the correct rationale cause significant challenges to investors. Traditional quantitative methods require large amounts of data and cannot directly present the reasoning process to users, making them less than perfect in this field. Recently, large language models have achieved success in various downstream applications, but their effectiveness in the domain of financial breakout detection has been subpar. The reason is that the unique data and specific knowledge are required in breakout detection. To address these issues, we created the first financial breakout dataset and introduce FinLLM-B, the premier large language model for financial breakout detection, which enhances the effectiveness of breakout trading strategies. Furthermore, we have developed a novel framework for large language models, namely multi-stage structure, effectively reducing mistakes in downstream applications. Experimental results indicate that compared to GPT-3.5, FinanceGPT-B improves the average accuracy of answers and rational by 49.97%, with the multi-stage structure contributing 9.72% to the improvement. Additionally, it outperforms ChatGPT-4 by 42.38%.