A Survey on Self-Improving Modern AI Agents
TheTuringPost · x · 2026-07-19
This is a comprehensive survey on the self-improvement of modern agents, detailing how they continuously improve through experience without requiring humans to manually patch every single step.
The paper categorizes existing methods into two main approaches:
- Model Improvement: Updating model parameters using generated examples, feedback, and experiential data.
- Scaffolding Improvement: Upgrading prompts, memory, tools, controllers, and overall agent orchestration without altering model parameters.
The survey also covers:
- Sources of self-generated data, feedback, and experience
- Tool creation and complete agent redesigns
- Applications in domains like coding, science, and robotics
- Evaluation, safety, and stability issues
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