Is MMGraphRAG superseded?

MMGraphRAG (Retrieval-augmented generation): superseded — cited as a baseline and beaten by newer methods. 2 paper(s) critique it, 2 beat it on benchmarks — #52 of 1179 most-superseded. Sub-problem: cluster led by MMGraphRAG. Newer alternatives in the same sub-problem include VideoStir, Graph-to-Frame RAG, MG$^2$-RAG, AutoThinkRAG, AgenticOCR.

Method Drift›Retrieval-augmented generation

Superseded baseline#52 of 1,179 most-superseded

MMGraphRAG

MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge Graphs

Retrieval-augmented generation · first seen Jul 28, 2025

superseded — cited as a baseline and beaten by newer methods

2 papers critique it · 2 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites MMGraphRAG as a baseline.

“such approaches typically convert visual content into textual graph nodes via MLLMs, effectively reducing multimodal structure to text-centric representations. As a result, fine-grained visual evidence may be abstracted away, limiting faithful cross-modal reasoning.”
— MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
“MMGraphRAG links scene graphs with textual representations but suffers from structural blindness—treating tables and formulas as plain text without proper entity extraction, losing structural information for reasoning”
— RAG-Anything: All-in-One RAG Framework

Beaten on benchmarks

Head-to-head results where a newer method reports beating MMGraphRAG. Values are copied from the source paper's tables — verify against the cited paper.

MG²-RAG beats MMGraphRAG · E-VQA Single-Hop [Qwen2.5-VL-7B backbone]
62.88 vs 19.12
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · E-VQA All [Qwen2.5-VL-7B backbone]
53.36 vs 16.52
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · InfoSeek Unseen-Q [Qwen2.5-VL-7B backbone]
39.17 vs 0.69
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · InfoSeek Unseen-E [Qwen2.5-VL-7B backbone]
38.15 vs 0.39
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · InfoSeek All [Qwen2.5-VL-7B backbone]
38.65 vs 0.50
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · BC [GPT-5.2 backbone]
72.30 vs 68.40
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · MC [GPT-5.2 backbone]
56.10 vs 44.03
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · MC-m [GPT-5.2 backbone]
56.70 vs 44.66
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · Avg. [GPT-5.2 backbone]
61.70 vs 50.94
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · NAT [Qwen3.5-27B backbone]
98.45 vs 81.08
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · SOC [Qwen3.5-27B backbone]
95.28 vs 68.62
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation
MG²-RAG beats MMGraphRAG · LAN [Qwen3.5-27B backbone]
98.73 vs 80.09
MG$^2$-RAG: Multi-Granularity Graph for Multimodal Retrieval-Augmented Generation

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.