用语言结构增强记忆，让对话更连贯持久
Semantic Anchoring in Agentic Memory: Leveraging Linguistic Structures for Persistent Conversational Context
- 在向量存储外加入语法、语篇和指代关系等语言结构
- 在长对话中提升事实回忆和语义连贯性最高达18%
- 适合需要长期记忆的智能助手与多轮对话系统
大型语言模型在对话中表现出色，但在多轮和长期交互中因记忆持久性不足而受限。传统检索增强生成（RAG）系统将对话历史存储为密集向量，虽能捕捉语义相似性，却忽略句法依存、语篇关系和指代链接等精细语言结构。本文提出语义锚定（Semantic Anchoring），一种融合代理记忆的混合架构，通过依赖分析、语篇关系标注和指代消解，构建结构化记忆条目，增强对复杂上下文的召回能力。在改进的长期对话数据集上的实验表明，该方法相比强基线RAG，在事实回忆和语篇连贯性上提升最高达18%。进一步的消融实验、人工评估和错误分析验证了其鲁棒性和可解释性。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated impressive fluency and task competence in conversational settings. However, their effectiveness in multi-session and long-term interactions is hindered by limited memory persistence. Typical retrieval-augmented generation (RAG) systems store dialogue history as dense vectors, which capture semantic similarity but neglect finer linguistic structures such as syntactic dependencies, discourse relations, and coreference links. We propose Semantic Anchoring, a hybrid agentic memory architecture that enriches vector-based storage with explicit linguistic cues to improve recall of nuanced, context-rich exchanges. Our approach combines dependency parsing, discourse relation tagging, and coreference resolution to create structured memory entries. Experiments on adapted long-term dialogue datasets show that semantic anchoring improves factual recall and discourse coherence by up to 18% over strong RAG baselines. We further conduct ablation studies, human evaluations, and error analysis to assess robustness and interpretability.
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