ByteDance Paper: Keeping Full History Cuts Costs by 19% vs. Compression
AGI Hunt · wechat · 2026-08-31
ByteDance's Chain-of-Experience paper investigates how AI agents should handle past attempts during iterative solving. Contrary to intuition that compression saves context, experiments show retaining the full history with feedback signals achieves a 71.0% average accuracy across 8 models and 6 benchmarks, outperforming non-iterative solving (66.8%). Despite longer context windows, faster convergence reduces total API costs by 19%. The study suggests that feedback on "why it went wrong" holds more value than compressed result summaries, validating a "less is more" context strategy.
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