arXiv:2604.15097v2 Announce Type: replace-cross Abstract: This beta technical report asks how reusable experience should be represented so that it can function as effective test-time control and as a substrate for iterative evolution. We study this question in 4.590 controlled trials across 45 scientific code-solving scenarios. We find that documentation-oriented Skill packages provide unstable control: their useful signal is sparse, and expanding a compact experience object into a fuller documentation package often fails to help and can degrade the overall average. We further show that repres
The accelerating pace of AI development and the drive for more autonomous systems necessitate improved methods for representing and evolving 'experience' in AI agents.
This research directly addresses the challenge of creating more robust, adaptable, and autonomous AI systems, which is critical for future advancements in AI agents and general-purpose AI.
The findings suggest that current documentation-oriented approaches for representing AI experience are inefficient, pushing research towards more effective, potentially 'genetic' or 'strategy-based' methods for AI learning and evolution.
- · AI agents developers
- · Reinforcement learning researchers
- · Autonomous systems
- · Software engineering
- · Documentation-centric AI training methods
- · Manual code optimization
- · Static AI architectures
More efficient and resilient AI agents capable of learning and evolving at test-time.
Reduced human oversight requirements for complex AI systems, leading to broader deployment in critical applications.
The development of highly adaptive 'strategy genes' could lead to truly self-improving AI that operates beyond human pre-programming capacity.
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Read at arXiv cs.CL