arXiv:2604.15877v2 Announce Type: replace Abstract: As LLM agents scale to long-horizon, multi-session deployments, efficiently managing accumulated experience becomes a critical bottleneck. Agent memory systems and agent skill discovery both address this challenge, extracting reusable knowledge from interaction traces, yet a citation analysis of 1{,}136 references across 22 primary papers reveals a cross-community citation rate below 1\%. We propose the \emph{Experience Compression Spectrum}, a unifying framework that positions memory, skills, and rules as points along a single axis of increa
The proliferation of LLM agents in long-horizon, multi-session deployments is revealing critical bottlenecks in experience management, prompting innovation in memory and skill integration.
Efficiently managing and compressing experience is fundamental to scaling autonomous AI agents, determining their cognitive abilities and economic viability.
A unifying framework for agent memory, skills, and rules will accelerate the development of more capable and robust AI agents, moving towards more generalized autonomy.
- · AI agent developers
- · Enterprises adopting AI automation
- · Cloud computing providers
- · Companies with inefficient AI agent solutions
Improved performance and scalability of LLM-based autonomous agents in complex tasks.
Accelerated displacement of human white-collar workflows by more capable AI agents.
New forms of economic value creation as AI agents orchestrate and manage entire business processes autonomously.
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Read at arXiv cs.AI