GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG

[2608.15056] GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2608.15056 (cs) [Submitted on 15 Aug 2026] Title: GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG Authors: Zafar Ali , Asad Khan , Aalia Malik , Pavlos Kefalas View a PDF of the paper titled GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG, by Zafar Ali and 3 other authors View PDF HTML (experimental) Abstract: Multimodal retrieval-augmented generation (RAG) systems often rely on long unstructured contexts or aggressively expanded evidence graphs, which can introduce noisy evidence, weaken multi-hop reasoning, and increase unsupported generation. We present GraphLoom, a reliability-calibrated multimodal knowledge-graph RAG framework for compact and faithful evidence routing. Given a question and its associated multimodal input, GraphLoom constructs an instance-level multimodal knowledge graph from grounded scene descriptions, extracted relational triples, and external commonsense knowledge. Instead of injecting all retrieved evidence into the generator, GraphLoom performs reliability-aware subgraph retrieval with bounded expansion and selectively routes high-utility evidence through hierarchical graph memory slots and joint graph-sequence attention in a frozen language model. To improve robustness in complex reasoning settings, GraphLoom further combines interleaved retrieval with budgeted corrective retrieval, enabling adaptive multi-hop evidence refinement under noisy retrieval conditions. We evaluate GraphLoom on ScienceQA, MultiModalQA, and OK-VQA, including large distractor evidence pools that approximate noisy external knowledge retrieval. Experimental results show consistent gains in answer quality and evidence faithfulness over strong multimodal RAG, graph-retrieval, and open-source vision-language baselines, with improved retrieval quality on MultiModalQA and stable performance under noisy evidence pools. Additional analyses using MiniCheck-based verification, human evaluation, and latency profiling show that reliability-calibrated graph evidence routing provides an effective alternative to long-context multimodal evidence injection. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.15056 [cs.AI] (or arXiv:2608.15056v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.15056 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Zafar Ali [ view email ] [v1] Sat, 15 Aug 2026 06:02:23 UTC (1,490 KB) Full-text links: Access Paper: View a PDF of the paper titled GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG, by Zafar Ali and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• GraphLoom: Reliability-Calibrated Graph Evidence Routing for Multimodal KG-RAG#611009
• Sequential Bayesian diagnosis and prognosis of structural deterioration from measured structural responses#1117713
• From Anomalies to Failures: Constructing Causal Error Graphs for Agentic Trace Diagnosis#1116845
• MURAL: Multimodal Uncertainty-aware Recommendation via Adaptive edge Learning#927850
• Measuring Structure in Graph Benchmark Datasets Using Graph Invariants#1143750