PersonalAI 2.0: Enhancing knowledge graph traversal/retrieval with planning mechanism for Personalized LLM Agents

2026-05-13

AI Summary

Existing Graph Retrieval-Augmented Generation (GraphRAG) approaches for personalized large language model (LLM) agents suffer from static retrieval and a lack of planning mechanisms, leading to low factual accuracy and increased hallucination in question answering. To address these limitations, this work proposes the PersonalAI 2.0 framework, which introduces a planning-based dynamic graph traversal strategy. By integrating entity extraction, node matching, and LLM-driven query generation, the framework enables adaptive, iterative knowledge graph retrieval and reasoning. It combines graph traversal algorithms such as BeamSearch and WaterCircles, achieving an average 4% improvement across six QA benchmarks under LLM-as-a-Judge evaluation. The incorporation of planning yields an 18% performance gain, while graph traversal outperforms flat retrieval by 6%. Notably, PersonalAI 2.0 attains state-of-the-art results on MINE-1 with an 89% information retention rate.

Abstract

We introduce PersonalAI 2.0 (PAI-2), a novel framework, designed to enhance large language model (LLM) based systems through integration of external knowledge graphs (KG). The proposed approach addresses key limitations of existing Graph Retrieval-Augmented Generation (GraphRAG) methods by incorporating a dynamic, multistage query processing pipeline. The central point of PAI-2 design is its ability to perform adaptive, iterative information search, guided by extracted entities, matched graph vertices and generated clue-queries. Conducted evaluation over six benchmarks (Natural Questions, TriviaQA, HotpotQA, 2WikiMultihopQA, MuSiQue and DiaASQ) demonstrates improvement in factual correctness of generating answers compared to analogues methods (LightRAG, RAPTOR, and HippoRAG 2). PAI-2 achieves 4% average gain by LLM-as-a-Judge across four benchmarks, reflecting its effectiveness in reducing hallucination rates and increasing precision. We show that use of graph traversal algorithms (e.g. BeamSearch, WaterCircles) gain superior results compared to standard flatten retriever on average 6%, while enabled search plan enhancement mechanism gain 18% boost compared to disabled one by LLM-as-a-Judge across six datasets. In addition, ablation study reveals that PAI-2 achieves the SOTA result on MINE-1 benchmark, achieving 89% information-retention score, using LLMs from 7-14B tiers. Collectively, these findings underscore the potential of PAI-2 to serve as a foundational model for next-generation personalized AI applications, requiring scalable, context-aware knowledge representation and reasoning capabilities.

Problem

• Graph Retrieval-Augmented Generation
• knowledge graph traversal
• personalized LLM agents
• factual correctness
• hallucination reduction

Innovation

• Graph Retrieval-Augmented Generation
• knowledge graph traversal
• planning mechanism
• adaptive query processing
• personalized LLM agents