Self-improving agents & learning loops
What is Self-improving agents & learning loops?
AI systems that automatically improve themselves by learning from their own actions, mistakes, and feedback rather than waiting for human retraining, creating cycles where the system gets better at solving problems over time.
This represents a path toward AI systems that can autonomously advance their capabilities, which could dramatically accelerate progress but also raises questions about whether these improvements stay aligned with human intent.
References
- MAGIC: Mixed-Granularity Agent Graphs via Incremental Construction with Dense-Reward Reinforcement Learning — ArXiv
- MedRSI: Recursive Self-Improvement for Medical Agents via Clinically Aligned Self-Evolution — ArXiv
- Google's Procedural Graphs — Self-Evolving Execution of LLM Agents — Medium: AI Agents
- OpenAI researcher on agent swarms & recursive self-improvement — YouTube
- CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents — ArXiv
- Dream-RSI: Recursive Self-Improvement Through Evolving Worlds — Hacker News
- ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents — ArXiv
- Self-Emergence Agent Architecture: Behavior-Inertia HMM, Reflexive Metacognition, and Social-Contrastive Self-Modeling — ArXiv