Learning to Forget: Emotional Salience as a Compression Mechanism for Long-Term AI Memory

Abstract

Recent advancements in Large Language Model (LLM) agents have largely focused on extending context windows or implementing massive Retrieval-Augmented Generation (RAG) systems to retain long-term history. However, this store-everything approach causes high computational costs and digital hoarding, paradoxically leading to digital amnesia where key emotional contexts are buried under trivial data. To challenge this paradigm, we introduce the Affective Memory Architecture, drawing from the amygdala's role in memory modulation to equip AI agents with the essential capacity to actively forget. Unlike static summarization, our framework structures multimodal inputs into an Affective Scene Graph (ASG) and dynamically adjusts the memory resolution based on emotional salience. High-arousal core memories are preserved in rich, high-resolution episodic detail; low-salience routines are aggressively downsampled using novel Optical Context Compression to minimal vision tokens; and frequently reactivated patterns are consolidated into crystallized semantic insights. Through quantitative proof-of-concept modeling, we demonstrate that systematically managing the trivial not only resolves the digital hoarding problem but actively reduces proactive interference, enhancing overall recall clarity. Ultimately, this work offers a scalable, privacy-friendly blueprint for resource-efficient AI capable of evolving with users over time, fundamentally shifting the goal of AI memory from total recall to meaningful retention.