用脑干结构启发，让长篇叙事推理更连贯。
PonsRAG: A Pons-Inspired RAG Bridging Cognitive Islands for Coordinated Long Narrative Reasoning
- 模仿脑干设计三层索引与协同推理机制
- 多选任务平均准确率提升11.56%
- 适合需要跨段落逻辑推理的场景
长篇叙事推理是处理复杂故事的关键能力。尽管检索增强生成提供可行框架，现有方法仍面临认知断层和跨层证据脱节两大挑战。为此，我们提出受脑干启发的PonsRAG框架，包含双核心组件：三层次索引将文档组织为连通知识结构以弥合认知断层，协同推理则在不同层级间检索并整合证据形成统一上下文。我们在四个长文本叙事基准上评估PonsRAG，实验结果表明其超越最强基线，在多选任务上平均准确率相对提升11.56%。
原文摘要 · Abstract (English)
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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