ICML 2025

Emergent Symbolic Mechanisms Support
Abstract Reasoning in Large Language Models
Yukang Yang 1 Declan Campbell 2 Kaixuan Huang 1 Mengdi Wang 1 Jonathan Cohen 2 Taylor Webb 3
Abstract
Many recent studies have found evidence for
emergent reasoning capabilities in large language
models (LLMs), but debate persists concerning
the robustness of these capabilities, and the ex-
tent to which they depend on structured reason-
ing mechanisms. To shed light on these issues,
we study the internal mechanisms that support
abstract reasoning in LLMs. We identify an emer-
gent symbolic architecture that implements ab-
stract reasoning via a series of three computations.
In early layers, symbol abstraction heads con-
vert input tokens to abstract variables based on
the relations between those tokens. In interme-
diate layers, symbolic induction heads perform
sequence induction over these abstract variables.
Finally, in later layers, retrieval heads predict
the next token by retrieving the value associated
with the predicted abstract variable. These re-
sults point toward a resolution of the longstanding
debate between symbolic and neural network ap-
proaches, suggesting that emergent reasoning in
neural networks depends on the emergence of
symbolic mechanisms.
1. Introduction
Large language models (LLMs) have become the domi-
nant paradigm in artificial intelligence, but there is ongoing
debate concerning the limits and reliability of their capabili-
ties. One major focus of this debate has been the question
of whether they can reason systematically in an abstract or
human-like manner. Many studies have documented im-
pressive performance on various reasoning tasks (Wei et al.,
1Department of Electrical and Computer Engineering, Prince-
ton University, Princeton, NJ 2Princeton Neuroscience Insti-
tute, Princeton University, Princeton, NJ 3Microsoft Research,
New York, NY. Correspondence to:
Taylor Webb <tay-
lor.w.webb@gmail.com>.
Proceedings of the 42 nd International Conference on...