发表机构
Suzhou Institute for Advanced Research, University of Science and Technology of China; School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学苏州高等研究院; 中国科学技术大学生命科学与医学部生物医学工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多跳问答中注意力衰减和错误积累问题，提出受大脑启发的θ-γ分层振荡推理框架THOR，经实验验证，该框架能提高答案准确性和鲁棒性，减轻相关限制。
AI 中文摘要
多跳问答需要从多个上下文中检索和整合证据。尽管当前研究取得了快速进展，但多跳推理仍受两个持续存在的限制：注意力衰减，即随着推理链增长，模型对主要问题的关注会下降；错误积累，即错误会跨跳传播并导致最终失败。受θ-γ分层振荡启发，我们提出了THOR，一个受大脑启发的θ-γ分层振荡推理框架。大量对比实验和特定验证实验表明，THOR提高了答案准确性和鲁棒性，同时减轻了局限性。
英文摘要
Multi-hop question answering requires retrieving and integrating evidence from multiple contexts. Despite the rapid progress of current research, multi-hop reasoning remains constrained by two persistent limitations: attention decay, where the model's focus on main question degrades as the reasoning chain grows, and error accumulation, where mistakes propagate across hops and compounds into final failure. Inspired by Theta-Gamma hierarchical oscillation which decouples global planning from local retrieval, enabling efficient attention transfer between hops and a verification and repair mechanism that interrupts the accumulation of errors in the wrong paths, we present THOR, a brain-inspired Theta-Gamma hierarchical oscillatory reasoning framework. Extensive comparative experiments and specific validation experiments on multi-hop QA benchmarks demonstrate that THOR improves answer accuracy and robustness while mitigating limitations, showcasing its generalization across different backbones.
