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This paper presents TraceRetain, a lightweight framework for bounded external memory in frozen LLM agents, demonstrating that selective retention differentiates from cache heuristics primarily when memory streams contain noise, offering task-success and efficiency benefits.
SOLAR proposes a self-optimizing autonomous agent that leverages parameter-level meta-learning and multi-level reinforcement learning to enable lifelong adaptation of LLMs to non-stationary data streams, outperforming baselines on reasoning tasks.