Self-Improvements in Modern Agentic Systems: A Survey
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| Authors | Zhe Ren et al. |
| Year | 2026 |
| HF Upvotes | 26 |
| arXiv | 2607.13104 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Self-improving autonomous agents are moving from research prototypes to deployed systems. The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input. This survey frames modern self-improving agents as adaptive systems that convert experience into accumulated capability gains. We offer a system-level framework that represents a modern agent as a configuration coupling a foundation model with an operational scaffold of prompts, memory, tools, and control logic. Within this framework, self-improvement is formalized as a self-induced update operator that obtains and commits updates to model parameters or scaffold components. We organize prior work by update target and by the signals that drive change, then review applications and discuss evaluation, before closing with open problems and future directions. For convenience, we track technical updates on https://github.com/selfimproving-agent/awesome-Self-Improving-Agents.
Engineering Breakdown
The Problem
Self-improving autonomous agents are moving from research prototypes to deployed systems.
The Approach
The primary goal is controllable evolution, or adaptation, from experience with minimal or even no human input.
Key Results
For convenience, we track technical updates on https://github.com/selfimproving-agent/awesome-Self-Improving-Agents.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Selfimprovements
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