Unraveling the cognitive patterns of Large Language Models through
  module communities
Large Language Models (LLMs) have reshaped our world with significant
advancements in science, engineering, and society through applications ranging
from scientific discoveries and medical diagnostics to Chatbots. Despite their
ubiquity and utility, the underlying mechanisms of LLM remain concealed within
billions of parameters and complex structures, making their inner architecture
and cognitive processes challenging to comprehend. We address this gap by
adopting approaches to understanding emerging cognition in biology and
developing a network-based framework that links cognitive skills, LLM
architectures, and datasets, ushering in a paradigm shift in foundation model
analysis. The skill distribution in the module communities demonstrates that
while LLMs do not strictly parallel the focalized specialization observed in
specific biological systems, they exhibit unique communities of modules whose
emergent skill patterns partially mirror the distributed yet interconnected
cognitive organization seen in avian and small mammalian brains. Our numerical
results highlight a key divergence from biological systems to LLMs, where skill
acquisition benefits substantially from dynamic, cross-regional interactions
and neural plasticity. By integrating cognitive science principles with machine
learning, our framework provides new insights into LLM interpretability and
suggests that effective fine-tuning strategies should leverage distributed
learning dynamics rather than rigid modular interventions.
68ae6de1364411bea07df71e
Kushal Raj Bhandari
claimed_verified
2025-08-27T07:10:51.902Z
64dfbcb18e2084e1d7b51b46
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Kushal Raj Bhandari
user
KBhandari11
68ae6de1364411bea07df71f
Pin-Yu Chen
68ae6de1364411bea07df720
Jianxi Gao
Unraveling the cognitive patterns of Large Language Models through
  module communities
Large Language Models (LLMs) have reshaped our world with significant
advancements in science, engineering, and society through applications ranging
from scientific discoveries and medical diagnostics to Chatbots. Despite their
ubiquity and utility, the underlying mechanisms of LLM remain concealed within
billions of parameters and complex structures, making their inner architecture
and cognitive processes challenging to comprehend. We address this gap by
adopting approaches to understanding emerging cognition in biology and
developing a network-based framework that links cognitive skills, LLM
architectures, and datasets, ushering in a paradigm shift in foundation model
analysis. The skill distribution in the module communities demonstrates that
while LLMs do not strictly parallel the focalized specialization observed in
specific biological systems, they exhibit unique communities of modules whose
emergent skill patterns partially mirror the distributed yet interconnected
cognitive organization seen in avian and small mammalian brains. Our numerical
results highlight a key divergence from biological systems to LLMs, where skill
acquisition benefits substantially from dynamic, cross-regional interactions
and neural plasticity. By integrating cognitive science principles with machine
learning, our framework provides new insights into LLM interpretability and
suggests that effective fine-tuning strategies should leverage distributed
learning dynamics rather than rigid modular interventions.
A network-based framework links cognitive skills, LLM architectures, and datasets, revealing unique emergent skill patterns in LLMs that benefit from dynamic, cross-regional interactions.
Large Language Models LLMs network-based framework cognitive skills module communities emergent skill patterns avian and small mammalian brains skill acquisition dynamic cross-regional interactions neural plasticity cognitive science principles machine learning LLM interpretability effective fine-tuning strategies distributed learning dynamics