@inproceedings{zhao-etal-2026-ponsrag,
title = "{P}ons{RAG}: A Pons-Inspired {RAG} Bridging Cognitive Islands for Coordinated Long Narrative Reasoning",
author = "Zhao, Rongchen and
Chen, Yu and
Wang, Juyuan and
Mo, Zhouting and
Yu, Jianxing and
Chen, Wenqing and
Liu, Jingping",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2299/",
pages = "52276--52293",
ISBN = "979-8-89176-459-0",
abstract = "Long Narrative Reasoning is an essential capability for processing and reasoning over complex narratives. While retrieval-augmented generation provides a promising framework, existing methods still face two critical challenges: cognitive islanding and cross-layer evidence disconnection. To address these issues, we propose PonsRAG, a coordinated RAG framework inspired by the biological pons. PonsRAG consists of two key components: Triple-Layer Indexing, which organizes documents into a connected knowledge structure to bridge cognitive islands, and Coordinated Reasoning, which retrieves evidence across distinct layers and integrates cross-layer information into a unified context. We evaluate PonsRAG on four long-context narrative benchmarks, and experimental results show that it outperforms the strongest baseline, achieving a 11.56{\%} relative improvement in average accuracy on multi-choice tasks."
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%0 Conference Proceedings
%T PonsRAG: A Pons-Inspired RAG Bridging Cognitive Islands for Coordinated Long Narrative Reasoning
%A Zhao, Rongchen
%A Chen, Yu
%A Wang, Juyuan
%A Mo, Zhouting
%A Yu, Jianxing
%A Chen, Wenqing
%A Liu, Jingping
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F zhao-etal-2026-ponsrag
%X Long Narrative Reasoning is an essential capability for processing and reasoning over complex narratives. While retrieval-augmented generation provides a promising framework, existing methods still face two critical challenges: cognitive islanding and cross-layer evidence disconnection. To address these issues, we propose PonsRAG, a coordinated RAG framework inspired by the biological pons. PonsRAG consists of two key components: Triple-Layer Indexing, which organizes documents into a connected knowledge structure to bridge cognitive islands, and Coordinated Reasoning, which retrieves evidence across distinct layers and integrates cross-layer information into a unified context. We evaluate PonsRAG on four long-context narrative benchmarks, and experimental results show that it outperforms the strongest baseline, achieving a 11.56% relative improvement in average accuracy on multi-choice tasks.
%U https://aclanthology.org/2026.emnlp-main.2299/
%P 52276-52293
Markdown (Informal)
[PonsRAG: A Pons-Inspired RAG Bridging Cognitive Islands for Coordinated Long Narrative Reasoning](https://aclanthology.org/2026.emnlp-main.2299/) (Zhao et al., EMNLP 2026)
ACL