Pain Points of Agent Context Compaction and a Potential Solution
kalomaze · x · 2026-08-14
A developer pointed out a severe flaw in current AI agent context compaction: information (like SSH aliases) can be completely lost after a single compaction, even if it was being constantly queried.
He noted that existing compaction methods in the literature mostly rely on heuristics or LM judges. He proposed a new approach: training a compaction model to learn a function that compresses the latent implied state of the trajectory across diverse possible-next-turn KL divergences. He believes this could be a ticket to pseudo-continual learning within the text space.
Related event: AI Agent Context Compaction Causes Critical Information Loss(3 posts)→
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