Study Finds Emergent Symbolic Mechanisms Support Abstract Reasoning in LLMs
MengdiWang10 · x · 2026-08-25
This research paper offers a complementary perspective to MATH-Perturb by investigating the internal mechanisms supporting abstract reasoning in LLMs.
The study identifies an emergent symbolic architecture that implements abstract reasoning through three computational steps:
- Symbol Abstraction Heads (Early layers): Convert input tokens to abstract variables based on their relations.
- Symbolic Induction Heads (Middle layers): Perform sequence induction over these abstract variables.
- Retrieval Heads (Late layers): Predict the next token by retrieving the value associated with the predicted abstract variable.
These findings suggest a resolution to the debate between symbolic and neural approaches, indicating that emergent reasoning in neural networks may depend on the emergence of internal symbolic mechanisms.
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