arXiv:2603.23994v2 Announce Type: replace Abstract: Generative optimization uses large language models (LLMs) to iteratively improve artifacts (such as code, workflows or prompts) using execution feedback. It is a promising approach to building self-improving agents, yet in practice remains brittle: despite active research, only 9% of surveyed agents used any automated optimization. We argue that this brittleness arises because, to set up a learning loop, an engineer must make ``hidden'' design choices: What can the optimizer edit and what is the "right" learning evidence to provide at each up
The proliferation of LLMs has made iterative generative optimization a core technique, but practical implementation challenges are becoming clearer through research and real-world application.
Understanding the brittleness and 'hidden design choices' in iterative generative optimization is critical for advancing autonomous AI agents and ensuring their practical efficacy.
The focus for developing self-improving agents shifts from merely conceptualizing iterative loops to rigorously defining and optimizing the underlying design choices and feedback mechanisms.
- · AI researchers focusing on agent architectures
- · Developers of robust AI development platforms
- · Companies investing in foundational AI tooling
- · Developers implementing naive generative optimization
- · Companies over-relying on un-optimized agentic systems
Research efforts will likely intensify on formalizing and standardizing design patterns for learning loops in generative optimization.
The development of more reliable and effective self-improving agents will accelerate, impacting a wider range of industries.
Improved agent reliability could lead to faster automation of complex tasks, potentially reshaping white-collar labor markets more rapidly.
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Read at arXiv cs.LG