@inproceedings{ma-etal-2025-recall,
    title = "Recall with Reasoning: Chain-of-Thought Distillation for Mamba{'}s Long-Context Memory and Extrapolation",
    author = "Ma, Jun-Yu  and
      Fang, Tianqing  and
      Zhang, Zhisong  and
      Zhang, Hongming  and
      Mi, Haitao  and
      Yu, Dong",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.235/",
    doi = "10.18653/v1/2025.emnlp-main.235",
    pages = "4714--4720",
    ISBN = "979-8-89176-332-6",
    abstract = "Mamba{'}s theoretical infinite-context potential is limited in practice when sequences far exceed training lengths. This work explores unlocking Mamba{'}s long-context memory ability by a simple-yet-effective method, Recall with Reasoning (RwR), by distilling chain-of-thought (CoT) summarization from a teacher model. Specifically, RwR prepends these summarization as CoT prompts during fine-tuning, teaching Mamba to actively recall and reason over long contexts. Experiments on LONGMEMEVAL and HELMET show that RwR outperforms existing long-term memory methods on the Mamba model. Furthermore, under similar pre-training conditions, RwR improves the long-context performance of Mamba relative to comparable Transformer/hybrid baselines while preserving short-context capabilities, all without changing the architecture."
}