Knowledge Vector of Logical Reasoning in Large Language Models
Logical reasoning serve as a central capability in LLMs and includes three main forms: deductive, inductive, and abductive reasoning. In this work, we study the knowledge representations of these reasoning types in LLMs and analyze the correlations among them. Our analysis shows that each form of logical reasoning can be captured as a reasoning-specific knowledge vector in a linear representation space, yet these vectors are largely independent of each other. Motivated by cognitive science theory that these subforms of logical reasoning interact closely in the human brain, as well as our observation that the reasoning process for one type can benefit from the reasoning chain produced by a...
| Authors | Zixuan Wang, Yuanyuan Lei |
|---|---|
| Primary category | cs.CL |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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