English(EN) Evolve on Graph: Self-Evolving Knowledge Graph Reasoning without Question Annotations

LLM通过自我博弈学习知识图谱推理，无需标注

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员开发了一个名为Evolve on Graph (EoG) 的新框架，使大型语言模型 (LLM) 能够在无需问题-答案标注的情况下提高其知识图谱推理能力。该系统使用“图谱自我博弈”机制，其中一个LLM充当问题提出者，另一个充当求解者。为了确保问题可验证，一种称为符号可验证拓扑合成的技术会对知识图谱上的推理路径进行采样并生成正确的答案集。这使得提出者能够学会提出与求解者当前技能水平相符的多跳问题，从而在各种基准测试中实现持续的性能提升。 AI

影响使LLM能够在没有昂贵人工标注的情况下提高知识图谱上的复杂推理能力。

排序理由学术论文，详细介绍了一种新的LLM推理方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 12 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了一种新的LLM推理方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, model release; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: Same-day; Cluster formed today. Ranking reflects the current source set at time of score.

完整方法见我们的编辑标准。

报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Zongyuan Sun, Shuai Zhao, Ke Shu, Ziyue Zhu, Shangyang Wu, Yuanlin Lin, Tao Wang, Shiyue Li, Yingjun Du, Anh Tuan Luu, Haoran Luo · 2026-10-27 04:00

Evolve on Graph：无需问题标注的自演化知识图谱推理

arXiv:2610.35598v1 Announce Type: new Abstract: Knowledge graphs (KGs) provide reliable external knowledge for multi-hop factual reasoning with large language models (LLMs), yet most KGs in real-world domains such as finance and industry lack question--answer (QA) annotations. Wi…

本文如何被排名

报道来源 [1]

Evolve on Graph：无需问题标注的自演化知识图谱推理

相关实体

相关话题