When Can Agents Forget Their Reasoning? ICLR for Long-Horizon Agent Context Compression
Long horizon language model agents continually accumulate reasoning history, increasing context length and inference cost even after earlier decisions have been executed and observed. Unlike static Chain of Thought compression, removing historical reasoning can change future actions and the resulting interaction trajectory. We study when such reasoning can be safely forgotten. We propose Interaction Aware Compression for Long Horizon Reasoning (ICLR), a training free online method that ranks reasoning blocks using frozen proxy entropy while preserving actions, tool calls, and observations. On 260 WorkBuddyBench tasks, ICLR improves average reward from 0.699 to 0.718, while reducing input,...
| Authors | Mingxuan Wang, Fei Luo, Bo Wang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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