BAIR Introduces ABBEL: Optimizing Long-Horizon Agent Context via Natural-Language Beliefs
berkeley_ai · x · 2026-07-30
As task horizons expand, LLMs cannot retain infinite interaction histories. While recursive summarization helps compress context, it leads to significant performance degradation.
To address this, UC Berkeley researchers introduced ABBEL. Instead of simple truncation, the framework isolates and supervises summaries as natural-language belief states, replacing the full interaction history as the agent's working context. This approach effectively improves model performance in long-horizon tasks like collaborative code generation.
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