Doc-to-Atom: Learning to Compile and Compose Memory Atoms
Long input sequences are central to document understanding and multi-step reasoning in Large Language Models, yet the quadratic cost of attention makes inference both memory-intensive and slow. Context distillation mitigates this by compressing contextual information into model parameters, and recent work such as Doc-to-LoRA amortizes context distillation into a single forward pass that generates one LoRA adapter per document. However, producing a single monolithic adapter for all queries leads to irrelevant-query interference, limited compositional recall, and poor scalability to long-document reasoning. To address these challenges, we propose Doc-to-Atom (Doc2Atom), a compositional para...
| Authors | Xingjian Diao, Wenbo Li, Yashas Malur Saidutta et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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