arXiv:2605.21240v1 Announce Type: new Abstract: LLM agents have shown strong performance across a wide range of complex tasks, including interactive environments that require long-horizon decision making. But these agents cannot learn on the fly at test time. Self-evolving agents address this by accumulating memory and reflection across episodes rather than requiring model-weight updates. However, these agents often suffer from exploration collapse: as memory grows, behavior concentrates around familiar high-reward routines, reducing the chance of discovering better alternatives. To address th
The proliferation of LLM agents highlights the need for continuous learning and adaptation beyond initial training, making 'on-the-fly' evolution a critical next step in AI development.
Overcoming exploration collapse in self-evolving agents could unlock more robust and adaptive AI systems capable of operating effectively in complex, dynamic environments without constant human intervention or retraining.
AI agents will be able to discover novel, high-performance behaviors autonomously over longer periods, moving beyond fixed routines and initial programming.
- · AI development platforms
- · Robotics and automation
- · Complex systems operators
- · AI researchers
- · Tasks requiring frequent human supervision of AI
- · Static AI models
- · Inefficient AI training methodologies
Autonomous agents will become more versatile and effective in real-world applications.
This could accelerate the deployment of AI in highly dynamic and critical sectors, potentially reducing operational costs and increasing efficiency.
The enhanced autonomous capability of AI agents might lead to new ethical and control challenges as they diverge further from pre-programmed behaviors.
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