@inproceedings{ling-etal-2026-reusable,
    title = "Reusable Experiences: Latent Routing and Modular Composition in {LLM}s",
    author = "Ling, Shuai  and
      Liao, Lizi  and
      Jiang, Dongmei  and
      Guan, Weili",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1388/",
    pages = "30087--30100",
    ISBN = "979-8-89176-390-6",
    abstract = "Large language models (LLMs) have remarkable capabilities, but adapting them to specialized domains poses a fundamental question: \textit{how should accumulated experience be represented and leveraged?} Existing approaches represent experience either as explicit textual artifacts in prompts (\textit{e.g.}, retrieved documents or dialogues) or implicitly within model weights via fine-tuning (\textit{e.g.}, LoRA adapters). However, textual methods are limited by context windows and cannot internalize knowledge, while parametric fine-tuning yields one adapter per task with minimal cross-task skill reuse. We propose \textbf{ReX} (\textbf{Re}usable e\textbf{X}perience), an experience-centric adaptation framework that treats latent experiences {---} recurring reasoning patterns and skills {---} as fundamental units for LLM specialization. Our method learns a shared Experience Bank of foundational skill vectors and uses a VAE-based encoder to map each input to a low-dimensional experience code. An Experience Router then dynamically composes the relevant skill vectors from this bank into a lightweight adapter for that input. By reusing skills across inputs, \textbf{ReX} enables implicit knowledge sharing across tasks without any explicit task identifiers. Experiments on multi-task NLP benchmarks show that this approach outperforms standard task-specific fine-tuning, yielding improved generalization through flexible skill reuse. Code is available at \url{https://github.com/iLearn-Lab/ACL26-ReX}."
}