Distilling Tacit Procedural Knowledge: From Scholarly Literature to Skill-Guided Multimodal Reasoning

Large language models (LLMs) possess extensive factual knowledge but often struggle with expert problems that require coordinated use of heterogeneous evidence. We argue that a key missing component is tacit procedural knowledge—the knowledge of selecting, integrating and revising evidence. We propose Explicit-to-Tacit-to-Skill (E2T2S), a framework that extracts structured knowledge from scholarly papers, aggregates it across each scholar's research to recover reusable procedural patterns, and consolidates them into an Expert Skill Library for dynamic skill-guided reasoning. We evaluate E2T2S on Chinese Chu bamboo-slip character interpretation, where models must infer a modern Chinese character from its glyph image, textual context, external evidence, and scholarly literature. On a controlled held-out evaluation set, our method outperform both generic chain-of-thought and retrieval-augmented baselines. Our results demonstrate that scholarly literature can serve not only as a source of knowledge, but also as a source of reusable expert procedures for LLM reasoning.