@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://aclanthology.org/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)."
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<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).</abstract>
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%0 Conference Proceedings
%T AURORA: Neuro-Symbolic Continual Indexing for Evolving RAG Systems
%A Saravanan, Manoj
%A Salla, Rohit Kumar
%A Amancherla, Ramya Manasa
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F saravanan-etal-2026-aurora
%X 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).
%U https://aclanthology.org/2026.findings-acl.495/
%P 10179-10195
Markdown (Informal)
[AURORA: Neuro-Symbolic Continual Indexing for Evolving RAG Systems](https://aclanthology.org/2026.findings-acl.495/) (Saravanan et al., Findings 2026)
ACL