Solving Agent Memory Loss: KL-Divergence Compaction for Continual Learning
kalomaze · x · 2026-08-14
The developer points out that current AI agents can easily lose constantly queried crucial information (like SSH aliases) after just a single context compaction.
He notes that existing literature relies on heuristics or LM judges, and proposes a potential solution: learning a compression function that minimizes KL divergence across future turns, which could act as pseudo-continual learning in text space.
Related event: AI Agent Context Compaction Causes Critical Information Loss(3 posts)→
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