arXiv:2512.07287v3 Announce Type: replace Abstract: As intents unfold and environments change, multi-turn agents face continuously shifting decision contexts. Although reusing past experience is intuitively appealing, existing approaches remain limited: full trajectories are often too context-specific to transfer, while tool-level reuse ignores the surrounding context and environment. In this paper, we introduce a hybrid episodic-procedural memory strategy (H-EPM) that enables experience-induced self-evolution of multi-turn tool-use policies by adaptively reusing partially overlapping successf
The ongoing development in AI research, particularly in multi-turn agents and tool-use, naturally leads to exploring more adaptive and efficient memory strategies to enhance agent performance in complex, dynamic environments.
This development represents a significant step towards more robust and self-improving AI agents, capable of learning from diverse experiences and adapting to evolving tasks and contexts, which will accelerate automation.
AI agents will become more autonomous and less reliant on explicit reprogramming for new scenarios, shifting towards experience-induced self-evolution through advanced memory architectures.
- · AI software developers
- · Automation industries
- · Generative AI platforms
More capable and adaptable AI agents emerge, improving performance in multi-turn interactions.
Increased efficiency and autonomy of AI systems could lead to faster integration of AI into complex workflows and applications.
The enhanced ability of AI to self-evolve through experience could accelerate the development of general-purpose AI and reduce the need for constant human supervision in agent deployment.
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