Cursor Composer Self-Summarization
Source: https://cursor.com/blog/self-summarization Author: Cursor team Date: 2025-2026
Summary
Cursor trains Composer to summarize its own context when hitting context limits, rather than using external summarization. Trained via RL: the reward signal applies to both problem-solving steps and summary quality. Summaries average ~1,000 tokens vs 5,000+ for manual summarization prompts, 50% fewer compaction errors.
Key Claims
- Self-summarization baked into training — not a bolt-on external tool
- Key insight: “By making self-summarization part of Composer’s training, we can get training signal from trajectories much longer than the model’s max context window”
- Process: generate → hit threshold → self-compress → continue with compressed context + metadata
- Learned summaries: ~1,000 tokens (vs 5,000+ for manual prompts) + 50% error reduction
- Demonstrated: Composer solved “make-doom-for-mips” in 170 turns, compressing 100K+ tokens
- RL reward applies to summary quality — model learns what information is essential
Connection to Other Sources
Directly addresses the problem in Context Rot — degradation as context grows. Self-summarization is Cursor’s solution.
Entities
- Cursor (coding agent company)
Concepts
- Context Engineering — self-summarization as context management strategy
- Agent Memory — summary as compressed working memory
- Coding Agents — enabling 170-turn problem solving