MCMem: Metacognitive Memory for Self-Aware Reliance in Long-Horizon LLM Agents

The memory mechanisms have long been recognized as a reusable knowledge reservoir that supports large language model (LLM) agents to self-evolve in open-ended environments. Nevertheless, the universal utility of a piece of memory across task environments is hardly determinable at the time of abstraction. In this paper, we argue that an ideal memory mechanism should also enable an agent to progressively recognize its own capability boundaries when augmented with accumulated memories, so that it could strike a balance between exploiting existing ones and exploring more in open-ended environments. Based on this insight, we propose a MetaCognitive Agentic Memory mechanism (MCMem) that augments an agent with adaptable and reliable memory management and utilization throughout self-evolution. It is built on a hierarchical memory graph that progressively abstracts episodic trajectories into reusable memories while preserving provenance links to the underlying evidence. On top of that, a metacognitive layer continuously calibrates the agent's reliance on each memory as new experiential evidence accumulates, enabling sustained utilization of memory over long task horizons. Experiments on long-horizon agent benchmarks validate MCMem's superiority in terms of long-term task success and exploration-exploitation balance.