PRIME: Training Free Proactive Reasoning via Iterative Memory Evolution for User-Centric Agent

• URL: http://arxiv.org/abs/2604.07645v1
• Date: Wed, 08 Apr 2026 23:11:12 GMT
• Title: PRIME: Training Free Proactive Reasoning via Iterative Memory Evolution for User-Centric Agent
• Abstract summary: PRIME is a gradient-free learning framework for continuous agent evolvement.<n>It distills multi-turn interaction trajectories into structured, human-readable experiences.<n>Our experiments demonstrate that PRIME achieves competitive performance with gradient-based methods.
• Score: 2.291770711277359
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The development of autonomous tool-use agents for complex, long-horizon tasks in collaboration with human users has become the frontier of agentic research. During multi-turn Human-AI interactions, the dynamic and uncertain nature of user demands poses a significant challenge; agents must not only invoke tools but also iteratively refine their understanding of user intent through effective communication. While recent advances in reinforcement learning offer a path to more capable tool-use agents, existing approaches require expensive training costs and struggle with turn-level credit assignment across extended interaction horizons. To this end, we introduce PRIME (Proactive Reasoning via Iterative Memory Evolution), a gradient-free learning framework that enables continuous agent evolvement through explicit experience accumulation rather than expensive parameter optimization. PRIME distills multi-turn interaction trajectories into structured, human-readable experiences organized across three semantic zones: successful strategies, failure patterns, and user preferences. These experiences evolve through meta-level operations and guide future agent behavior via retrieval-augmented generation. Our experiments across several diverse user-centric environments demonstrate that PRIME achieves competitive performance with gradient-based methods while offering cost-efficiency and interpretability. Together, PRIME presents a practical paradigm for building proactive, collaborative agents that learn from Human-AI interaction without the computational burden of gradient-based training.

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