Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation

• URL: http://arxiv.org/abs/2510.19897v1
• Date: Wed, 22 Oct 2025 17:58:03 GMT
• Title: Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation
• Authors: Jackson Hassell, Dan Zhang, Hannah Kim, Tom Mitchell, Estevam Hruschka,
• Abstract summary: We investigate how agents built on pretrained large language models can learn target classification functions from labeled examples without parameter updates.<n>Our framework uses episodic memory to store instance-level critiques and distill these into reusable, task-level guidance.<n>Our findings highlight the promise of memory-driven, reflective learning for building more adaptive and interpretable LLM agents.
• Score: 11.819481846962447
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We investigate how agents built on pretrained large language models can learn target classification functions from labeled examples without parameter updates. While conventional approaches like fine-tuning are often costly, inflexible, and opaque, we propose a memory-augmented framework that leverages both labeled data and LLM-generated critiques. Our framework uses episodic memory to store instance-level critiques-capturing specific past experiences-and semantic memory to distill these into reusable, task-level guidance. Across a diverse set of tasks, incorporating critiques yields up to a 24.8 percent accuracy improvement over retrieval-based (RAG-style) baselines that rely only on labels. Through extensive empirical evaluation, we uncover distinct behavioral differences between OpenAI and opensource models, particularly in how they handle fact-oriented versus preference-based data. To interpret how models respond to different representations of supervision encoded in memory, we introduce a novel metric, suggestibility. This helps explain observed behaviors and illuminates how model characteristics and memory strategies jointly shape learning dynamics. Our findings highlight the promise of memory-driven, reflective learning for building more adaptive and interpretable LLM agents.

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