agent/papers/items/2026-2605-28175-mixture-of-experts-knowledge-graph-retrieval-augmented-generation-for-multi-agen.md

Paper: Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation

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type: paper title: Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation authors: Shijie Wang, Chengyi Liu, Yujuan Ding, Shanru Lin, See-Kiong Ng, Xu Xin, Wenqi Fan year: 2026 venue: arXiv url: https://arxiv.org/abs/2605.28175 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-05-27 updated_at: 2026-05-29 status: queued relevance: high topics:

• agent-evaluation
• agent-safety
• multi-agent
• rag methods:

benchmarks:

models:

datasets:

• cs.IR related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 14 collection_queries: rag-agent

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: rag-agent
• inferred topics: agent-evaluation, agent-safety, multi-agent, rag
• arXiv categories: cs.IR
• collection score: 14

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2605.28175