CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
Agents often work on complex problems that require millions of tokens of context, which necessitates compacting across sessions due to limited context windows. We develop CliffCompaction, an autocompaction technique that reduces cost by up to 50% under a bounded context while maintaining or improving performance on Terminal-Bench and achieving new levels of efficiency for test-time scaling and state-of-the-art results on KernelBench. The per-rollout savings of CliffCompaction make the performance--cost trade-off of test-time scaling more efficient, adding over 10 percentage points on Terminal-Bench for less than the cost of two full-context runs. Under parallel test-time scaling, CliffCom...
| Authors | Trang Nguyen, Eulrang Cho, Bingqing Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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