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This article introduces ABBEL, a framework designed to mitigate performance degradation caused by recursive summarization in long-horizon LLM interactions. By supervising natural-language belief states instead of relying on lossy context compaction, the method preserves critical information for complex tasks like collaborative code generation without sacrificing efficiency. This approach directly addresses scalability limits in agent-based workflows where maintaining full interaction history is impractical.

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Teaching LLMs to Update Beliefs for Efficient Long-Horizon Interaction

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Overview of ABBEL compared to traditional recursive summarization: Beliefs replace the full interaction history as the agent's working context, and belief grading improves performance by supervising the contents of each belief state. As task horizons grow, LLM contexts can't scale forever. Self-summarization enables concise, interpretable contexts, but at a significant performance cost, especially for human assistance domains where high quality data is scarce, e.g., collaborative code generation. We address this with ABBEL: a framework that isolates and supervises the information content of summaries in the form of natural-language belief states. For language models to effectively assist with increasingly complex tasks such as software development, they must be able to interact with us over hundreds or even thousands of…

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