Knowledge Graph-Driven Expert-Level Reasoning for Neuroscience
Knowledge graph (KG) is an abstraction that can be extracted from text corpora and used for in-depth reasoning. Prior work has leveraged KGs to fine-tune language models (LMs), enabling domain-specific superintelligence. In this work, we explore whether KG-driven in-depth reasoning capabilities can emerge in neuroscience using only information contained within a single authoritative textbook. The central hypothesis is that structured knowledge, when distilled into a high-quality KG and converted into KG-grounded question-answer (QA) supervision, is sufficient to produce expert-level reasoning through a fine-tuned LM that surpasses large language models (LLMs) in accuracy, while employing ...
| Authors | Jake Stephen, Niraj K. Jha |
|---|---|
| Primary category | cs.CL |
| Published | 24 May 2026 |
| Last updated at source | 27 May 2026 |
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