arXiv:2606.24151v1 Announce Type: cross Abstract: Self-evolving agents improve over time by distilling experience from past executions and reusing it in future tasks. Existing systems represent such experience either as natural-language text injected into the agent context or as code exposed as callable tools. However, the choice between these representations is typically made at design time rather than derived from the characteristics of the experience itself, leaving the trade-offs between them poorly understood. We present the first controlled study that isolates text memory and code memory
The proliferation of context-window based and tool-using AI systems highlights the current limitations in how agents manage and recall information, making this type of memory optimization crucial for advanced agent development.
This research addresses a fundamental bottleneck in agentic AI capabilities, potentially leading to more adaptive, efficient, and capable autonomous systems that can learn and apply knowledge more effectively over extended periods.
The ability of AI agents to fluidly integrate and reason across both natural language and executable code memories will make them significantly more powerful and versatile for complex, multi-step tasks.
- · AI Agent developers
- · Automation platforms
- · Software development
- · Enterprise AI solutions
- · Monolithic AI agent architectures
- · Tasks requiring human-in-the-loop for memory management
AI agents become more efficient and capable of handling complex, long-running tasks by intelligently selecting optimal memory representations.
This improved memory management leads to a significant reduction in computational costs and an increase in the reliability of autonomous agents across various applications.
The enhanced self-evolving capabilities could accelerate the development of truly autonomous systems, potentially disrupting white-collar workflows at an increased pace.
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Read at arXiv cs.AI