Towards Healthy Evolution: Exploring the Role and Mechanisms of Human-Agent Interaction in Self-Evolving Systems
arXiv:2606.06114v1 Announce Type: new Abstract: Self-evolving agents improve through continual self-play and self-generated learning signals, but autonomous evolution can also cause capability degradation and safety drift. Although human feedback has proven effective for static and post-trained agents, its role in self-evolving systems remains underexplored. We introduce Agent Norm Correction through Human-like Oversight and Review (ANCHOR), an LLM-based framework that simulates human supervision and delivers feedback at various phases of self-evolution. With ANCHOR, we evaluate two representa
As AI agents become more sophisticated and autonomous, the challenge of ensuring their safe and beneficial evolution becomes critical and requires proactive solutions.
This research addresses the fundamental problem of controlling advanced AI systems, preventing unintended consequences, and maintaining alignment with human values as they self-improve.
The explicit focus on human-agent interaction within self-evolving systems introduces a new paradigm for guiding AI development, moving beyond post-training feedback.
- · AI safety researchers
- · Developers of autonomous AI systems
- · Ethical AI frameworks
- · Human-AI collaboration platforms
- · Unconstrained autonomous AI development
- · AI systems prone to safety drift
- · AI models lacking continuous human oversight
This research provides a framework for integrating human-like supervision into the evolutionary loop of AI agents, potentially leading to more robust and aligned AI.
Successful implementation could accelerate the development and deployment of advanced AI agents in sensitive domains by mitigating risks of 'capability degradation and safety drift.'
Widespread adoption might lead to new regulatory standards for AI evolution, emphasizing human-in-the-loop mechanisms and accountability for agentic systems.
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Read at arXiv cs.AI