用图结构增强大模型推理，破解长时序实体任务规划难题，值得一读。
arXiv:2608.07905v1 Announce Type: new Abstract: Embodied agents using LLM-based planners often struggle with physical hallucinations, poor generalizat…
用图结构增强大语言模型，提升空间搜索能力，是AI与地理信息交叉的前沿探索。
arXiv:2606.22909v1 Announce Type: cross Abstract: There have been many recent improvements in the ability of Large Language Models (LLMs) to perform c…
用图结构优化大模型长期对话记忆，效率与连贯性双提升的学术新突破
arXiv:2606.13115v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have advanced open-domain dialogue systems, maintaining long-term…
将图结构融入LLM Agent策略优化，显著提升多步推理和任务完成能力。
arXiv:2510.26270v2 Announce Type: replace Abstract: Multi-step LLM agents in interactive environments represent a crucial step toward long-horizon dec…
新型图增强LLM框架Ex-GraphRAG，通过可解释证据路由解决GNN编码器节点贡献纠缠问题。
arXiv:2605.21994v1 Announce Type: cross Abstract: GraphRAG conditions language models on subgraphs retrieved from knowledge graphs, encoded via messag…
图增强生成新方案：语义调优与尾部自适应检索，提升复杂查询效果。
arXiv:2605.18765v1 Announce Type: cross Abstract: To augment Large Language Models (LLMs) for multi-hop question answering, a mainstream solution with…
图增强RAG架构如何破解向量搜索在复杂企业数据中的结构盲区，实现多跳推理与精准关联。
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard archite…