MEMENTO: Teaching LLMs to Manage Their Own Context
Reasoning models think in long, unstructured streams with no mechanism for compressing or organizing their own intermediate state. We introduce MEMENTO: a method that teaches models to segment reasoning into blocks, compress each block into a memento, i.e., a dense state summary, and reason forward by attending only to mementos, reducing context, KV cache, and compute. To train MEMENTO models, we release OpenMementos, a public dataset of 228K reasoning traces derived from OpenThoughts-v3, segmented and annotated with intermediate summaries. We show that a two-stage SFT recipe on OpenMementos is effective across different model families (Qwen3, Phi-4, Olmo 3) and scales (8B--32B parameters...
| Authors | Vasilis Kontonis, Yuchen Zeng, Shivam Garg et al. |
|---|---|
| Primary category | cs.AI |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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