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NeuroGenesis: A Self-Evolving Compiler That LearnsIts Own Optimization Law
Dhadi Sai Praneeth Reddya,∗, M Jithender Reddyb
aAI Research, Atlas AI Labs, Hyderabad, India
bDepartment of Computer Science and Engineering, Vasavi College of Engineering,
Hyderabad, India
Abstract
Compiler optimization has traditionally relied on static heuristics and man
ually designed transformation rules, limiting adaptability to evolving work
loads. We introduce NeuroGenesis, a self-evolving compiler that formulates optimization as a continual learning process. Instead of executing fixed
passes, the system autonomously discovers and refines optimization rules
through interaction with program executions.
NeuroGenesis combines sequence modeling over intermediate representa
tions, structural feature extraction, pattern mining, and reinforcement learn
ing to learn dynamic optimization policies. A rule formalization module
converts recurring patterns into interpretable transformation rules with as
sociated confidence and applicability constraints, while an execution-driven
feedback loop enables self-improvement without manual heuristics.
Experiments across synthetic and structured benchmarks show consistent
gains in efficiency, adaptability, and generalization over static baselines. Ab
lation studies further demonstrate the necessity of learning, feedback, and
rule formalization components.
This work establishes a new paradigm for compiler design, moving from
static pipelines toward autonomous, self-improving optimization systems.
Keywords: Self-evolving systems, Compiler optimization, Reinforcement
learning, Meta-learning, Autonomous systems, AI compiler
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