@inproceedings{saravanan-etal-2026-aurora,
    title = "{AURORA}: Neuro-Symbolic Continual Indexing for Evolving {RAG} Systems",
    author = "Saravanan, Manoj  and
      Salla, Rohit Kumar  and
      Amancherla, Ramya Manasa",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.495/",
    pages = "10179--10195",
    ISBN = "979-8-89176-395-1",
    abstract = "Retrieval-Augmented Generation (RAG) systems depend on non-parametric indices to access external knowledge, yet most retrieval infrastructure assumes a stationary query document distribution after index construction. In dynamic settings involving continual knowledge updates or evolving terminology, this assumption often fails, leading to degraded retrieval performance, while full re-indexing remains computationally expensive. We propose AURORA, a neuro-symbolic framework for adapting retrieval indices under distribution shift by treating index maintenance as a few-shot continual learning problem. AURORA decouples discrete index structure from continuous metric representations, enabling efficient adaptation of neural components while preserving index topology. A lightweight Bayesian routing policy further balances stability and plasticity by dynamically selecting among adaptive neural indices and static fallbacks based on uncertainty estimates. Across dense, learned sparse (SPLADE), and generative (DSI) retrieval settings, AURORA recovers up to +26.9{\%} Recall@10 on novel topics compared to static baselines, while adapting significantly faster than full retraining (28 ms vs. 5.1 s)."
}