PersonalAI 2.0 (PAI‑2) is a new framework that improves large language model (LLM) systems by integrating external knowledge graphs through a dynamic, multistage query‑processing pipeline. It performs adaptive, iterative information search guided by extracted entities, matched graph vertices, and generated clue‑queries, and has been evaluated on five benchmarks (Natural Questions, TriviaQA, HotpotQA, 2WikiMultihopQA, and MuSiQue). The results show a 9% average gain in factual correctness over comparable methods, an 18% boost from the search‑plan enhancement mechanism, and state‑of‑the‑art performance on the MINE‑1 benchmark, indicating that PAI‑2 effectively reduces hallucinations and increases precision for personalized AI applications.
arXiv:2511. 05385v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability.
ArcRAG is a retrieval-augmented generation system that operates over knowledge graphs without relying on construction‑specific schemas or relation names. It dynamically induces a query‑local graph interface at inference time, plans multi‑hop traversals, and executes them as Cypher queries. Experiments on MuSiQue, HotpotQA, and TriviaQA show that ArcRAG remains effective across both schema‑guided and open‑information‑extraction graph constructions, demonstrating that retrieval can be construction‑agnostic.
arXiv:2606. 30133v1 Announce Type: cross Abstract: Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging graph structure.
arXiv:2608.12129v2 Announce Type: replace Abstract: While retrieval-augmented generation (RAG) has proven effective at giving LLMs access to external knowledge, mainstream dense-retrieval implementat...
arXiv:2610.34866v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown remarkable capabilities across tasks, but they still struggle with complex reasoning that requires integrating...
SAG (SQL‑Retrieval Augmented Generation) is a structured retrieval framework that indexes documents as event‑entity pairs, forming latent hyperedges that preserve n‑ary relations without building a global knowledge graph. At query time, shared entities act as join keys, dynamically creating a query‑scoped neighborhood of related events while keeping each evidence chunk intact. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that SAG outperforms existing dense‑retrieval baselines, achieving the highest recall and end‑to‑end QA performance, especially as reasoning‑chain complexity grows.
arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.
MARS is a scalable knowledge‑graph question‑answering system that integrates large language models with knowledge graphs without requiring model fine‑tuning. It uses a structured retrieval process that links question entities to the graph, iteratively fetching next‑hop information and deciding when to stop and generate a SPARQL query. Evaluated on three KGQA benchmarks—including multilingual settings—MARS matches state‑of‑the‑art performance while remaining efficient and scalable.
arXiv:2606. 05658v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding their responses in external knowledge, but conventional pipelines rely on static, single-step retrieval that limits performance on complex queries.
arXiv:2602. 03315v2 Announce Type: replace Abstract: Agent memory systems must accommodate continuously growing information while supporting efficient, context-aware retrieval for downstream tasks.
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-worl...
arXiv:2607. 22652v1 Announce Type: new Abstract: Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream knowledge-intensive tasks, particularly knowledge graph question answering (KGQA).