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Dependency-Aware Chain-of-Thought Compression for Financial Reasoning

2026-07-10 04:07:32
Wenjun Wu, Lei Fu, Kejian Tong, Tao Ning, Sichen Zhao
arXiv_AI
         
Abstract

Chain of thought prompting improves complex reasoning, but its long intermediate traces create substantial inference cost and hinder practical deployment in financial settings. We present a Hierarchical Semantic Distillation Network, HSDN, for compressing reasoning chains while preserving answer accuracy and logical coherence. The framework combines semantic segmentation, dependency graph construction, dual encoder importance scoring, constrained segment selection, and local boundary rewriting. A frozen Qwen3 4B model is used only for feature extraction and final answer generation, while the compression process remains structured and interpretable. On the AFAC2025 benchmark, HSDN achieves 91.0% accuracy with 68.4% compression, outperforming strong compression baselines in overall score and reasoning coherence. The results show that graph guided compression is effective for high stakes financial reasoning tasks.

Abstract (translated)

URL

https://arxiv.org/abs/2609.00413

PDF

https://arxiv.org/pdf/2609.00413.pdf

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