CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
CliffCompaction cuts long-horizon coding-agent context cost by up to 50% without rewriting retained text.
CliffCompaction compacts long agent contexts by truncating or dropping original content only, never rewriting it or compacting a prior compaction. The authors report up to 50% lower cost under a bounded context while maintaining or improving Terminal-Bench results and improving test-time scaling efficiency. Under parallel scaling, Kimi K2.6 matches Opus 4.7 and exceeds Opus 4.6 and GPT-5.3 Codex at lower cost. On KernelBench it reports 2.23x CUDA kernel speedups after 200 steps and 3.58x after 400 steps, and they open-source a scaffold-agnostic API proxy for Claude Code, Codex, and other harnesses.
- Compaction only truncates or drops original content and never rewrites it.
- Prior compacted output is discarded so context drift does not accumulate.
- Reports up to 50% cost reduction while holding Terminal-Bench performance.
- Kimi K2.6 matches Opus 4.7 under parallel scaling at lower cost.
- Open-source API proxy works with Claude Code, Codex, and other harnesses.
- Vendors
- Moonshot AIAnthropicOpenAI
- Products
- CliffCompactionClaude CodeCodex
From the original article
1 min read at arxiv.orgAgents often work on complex problems that require millions of tokens of context, which necessitates compacting across sessions due to limited context windows.
By Trang Nguyen · arxiv.org
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