Evolutionary coding agents achieve most benchmark gains through a small subset of edit types and by cycling previously deleted code lines rather than developing new algorithmic structures.
Understanding the Challenges in Iterative Generative Optimiza- tion with LLMs, March 2026.https://arxiv.org/abs/2603.23994
3 Pith papers cite this work. Polarity classification is still indexing.
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End-to-end prompt optimization in compound AI systems is no better than chance unless the task has exploitable output structure the model can produce but does not default to.
Memory, skills, and rules in LLM agents sit on one compression spectrum, and no system yet supports adaptive cross-level compression.
citing papers explorer
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What Do Evolutionary Coding Agents Evolve?
Evolutionary coding agents achieve most benchmark gains through a small subset of edit types and by cycling previously deleted code lines rather than developing new algorithmic structures.
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Prompt Optimization Is a Coin Flip: Diagnosing When It Helps in Compound AI Systems
End-to-end prompt optimization in compound AI systems is no better than chance unless the task has exploitable output structure the model can produce but does not default to.
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Experience Compression Spectrum: Unifying Memory, Skills, and Rules in LLM Agents
Memory, skills, and rules in LLM agents sit on one compression spectrum, and no system yet supports adaptive cross-level compression.