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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search with execution feedback
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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
- Arch 2.0EggMind: LLM-Driven Two-Dimensional Intelligence for Scalable Equality SaturationIn Architecture 2.0: Workshop on AI for Computing Systems Design, 2026