Multimodal CoLRAG-TF: Triple-Filtered Retrieval for Complex PDFs
This paper proposes Multimodal CoLRAG-TF, a four-axis fusion architecture that integrates dense text embeddings, BM25 keyword matching, knowledge-graph triple filtering, and image-based similarity for robust retrieval over complex PDFs. The system constructs a multimodal index of 2,403 blocks from 43 Japanese disaster lesson PDFs and extracts 11,414 OpenIE triples for compositional reasoning. Bayesian optimization reveals that the triple axis must dominate (α=0.44) to counter lexical bias. On a 457-pair benchmark, it achieves a Recall of 0.9909 and a 71.6% improvement in multi-hop answer similarity over single-hop queries.
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[Submitted on 7 Jul 2026]
Title:Multimodal CoLRAG-TF: Triple-Filtered Retrieval for Complex PDFs
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Abstract:Retrieval-augmented generation (RAG) over heterogeneous PDF collections remains challenging due to multimodal content, domain-specific terminology, and the need for multi-hop reasoning across dispersed evidence. We present Multimodal CoLRAG-TF, a four-axis fusion architecture that integrates dense text embeddings, BM25 keyword matching, knowledge-graph triple filtering, and image-based similarity for robust retrieval over complex documents. Our system constructs a multimodal index of 2,403 blocks extracted from 43 Japanese disaster lesson PDFs, supported by a hybrid OCR pipeline and LLM-based caption generation. To enhance compositional reasoning, we extract 11,414 OpenIE triples and index them with FAISS, enabling sub-second triple lookup and hierarchical propagation of relevance signals. A HippoRAG2-inspired coarse-to-fine retriever (volume $\to$ chapter $\to$ block) narrows the search space before final fusion scoring. Bayesian optimization over fusion weights reveals that the triple axis must dominate ($\alpha_\text{triple} = 0.44$) to counteract lexical bias and sustain multi-hop retrieval quality. Evaluated on a 457-pair benchmark, Multimodal CoLRAG-TF achieves a Retrieval Recall of 0.9909 and a 71.6$\%$ improvement in multi-hop answer similarity over single-hop queries. An image-to-lesson pipeline using a vision LLM further demonstrates the applicability of the approach to visual inputs. These results show that triple-filtered multimodal fusion is essential for structured reasoning over noisy, heterogeneous PDFs and provides a general framework applicable beyond the disaster domain.
Comments: 18 pages, 8 tables, 9 figures
Subjects:
Machine Learning (cs.LG)
ACM classes: H.3.3; I.5.4; J.4
Cite as: arXiv:2607.20517 [cs.LG]
(or arXiv:2607.20517v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.20517
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Takato Yasuno [view email] [v1] Tue, 7 Jul 2026 23:00:52 UTC (341 KB)
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