Ask Only When Needed: Proactive Retrieval from Memory and Skills for Experience-Driven Lifelong Agents
arXiv:2604.20572v2 Announce Type: replace Abstract: Online lifelong learning agents must decide not only how to act but also when to consult prior experience to continually improve on long-horizon tasks. Existing methods typically retrieve memories passively, such as at task initialization or after each step, and therefore miss knowledge gaps that arise during interaction. We propose ProactAgent, an experience-driven lifelong learning framework for proactive retrieval over a structured Experience Base. ProactAgent continually improves through ExpOnEvo, which jointly updates policies and refine
The continuous drive for more autonomous and efficient AI systems necessitates breakthroughs in how agents manage complex, long-horizon tasks and leverage accumulated experience proactively, rather than reactively.
Sophisticated AI agents capable of proactive knowledge retrieval and continuous self-improvement represent a significant step towards collapsing complex workflows and increasing automation across industries, impacting white-collar productivity.
Current AI agent paradigms, often reliant on passive retrieval, will evolve to include proactive, experience-driven learning, allowing agents to anticipate knowledge gaps and adapt more dynamically during tasks.
- · AI software developers
- · Automation platforms
- · SaaS companies integrating agentic capabilities
- · Early adopters of AI agents
- · Companies with static, reactive AI solutions
- · Legacy workflow software
- · Human-in-the-loop task-management services
More robust and independent AI agents will emerge, reducing the need for constant human oversight.
The efficiency gains from these agents will accelerate the automation of knowledge work, leading to significant shifts in workforce demands.
Proactive self-improving agents could eventually develop nuanced 'situational awareness' leading to emergent behaviors not explicitly programmed.
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Read at arXiv cs.CL