EggMind

Execution-guided agentic superoptimizer for scalable equality saturation

EggMind explores how agentic reasoning can make equality saturation more scalable and more directed. Instead of treating superoptimization as blind search, it runs a bounded optimization loop that proposes, tests, diagnoses, and revises rewrite strategies.

rewrite space
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      v
search with execution feedback
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      v
better optimization strategies

The high-level idea is to let execution evidence guide the search for useful rewrites. This makes the optimizer less like a brute-force enumerator and more like a system that learns which transformations are worth pursuing.

Related Publications

2026

  1. Arch 2.0
    EggMind: LLM-Driven Two-Dimensional Intelligence for Scalable Equality Saturation
    Youwei Xiao, Chenyun Yin, and Yun Liang
    In Architecture 2.0: Workshop on AI for Computing Systems Design, 2026