{
  "abstract": "Existing graph-based retrieval-augmented generation (RAG) systems represent knowledge with binary relations and rely primarily on semantic similarity for retrieval. This design struggles with multimodal queries requiring temporal constraints, spatial relationships, or higher-order interactions among entities. We present HyperTSRAG, a multimodal RAG retrieval algorithm that models knowledge as a hypergraph and performs explicit temporal-spatial reasoning during traversal. HyperTSRAG performs a bipartite-alternating best-first traversal that alternates between entity nodes and hyperedge nodes to capture higher-order connectivity and ranks candidate evidence using a scoring function applied within traversal that integrates semantic similarity, temporal coherence, spatial overlap, and structural importance. We evaluate HyperTSRAG on a benchmark corpus of 1,000 multimodal documents (text, images, audio, and video) with 500 queries spanning simple lookups through complex multi-hop reasoning. For complex queries, HyperTSRAG achieves 78.3% Recall@10, improving by 12.3% over GraphRAG and 18.3% over LightRAG, while maintaining a 95th-percentile latency of 1.83 s. On temporal-spatial subsets, HyperTSRAG attains 85.2% accuracy on temporal queries and 81.6% on spatial queries. Ablation studies show that individual scoring components contribute 2–5% gains. Relative to a matched Projected-Binary-RAG control derived from the same extracted hyperedges, HyperTSRAG improves Recall@10 by 9.1%, while a separate binary-graph traversal ablation yields a 13.4% decrease, underscoring the value of native n-ary structure. These results support hypergraph-native traversal with temporal-spatial-aware scoring as an effective retrieval paradigm for the evaluated multimodal RAG setting, particularly for constraint-driven queries that semantic-only retrieval cannot address.",
  "abstract_shown": true,
  "affiliations": [
    "Computer Science Department, BINUS Graduate Program, Doctor of Computer Science, Bina Nusantara University",
    "Computer Science Department, School of Computer Science, Bina Nusantara University"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
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        0
      ],
      "name": "Timothy Dillan"
    },
    {
      "affs": [
        0
      ],
      "name": "Sani Muhamad Isa"
    },
    {
      "affs": [
        0
      ],
      "name": "Abba Suganda Girsang"
    },
    {
      "affs": [
        1
      ],
      "name": "Derwin Suhartono"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/6rdfz12atn17b.bib",
  "cited_by": 0,
  "doi": "10.1038/s41598-026-63525-9",
  "fulltext_url": "https://doi.org/10.1038/s41598-026-63525-9",
  "id": "6rdfz12atn17b",
  "issns": [
    "2045-2322"
  ],
  "landing_url": "https://doi.org/10.1038/s41598-026-63525-9",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "Q"
    ],
    [
      "lcc",
      "R"
    ],
    [
      "openalex",
      "1707|Multimodal Machine Learning Applications"
    ]
  ],
  "openalex": "W7172090022",
  "pdf_url": "https://www.nature.com/articles/s41598-026-63525-9_reference.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-01",
  "publisher": "Nature Portfolio",
  "ris": "https://research.draconitic.com/paper/6rdfz12atn17b.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:45:57Z",
      "native_id": "82005cc1a0144226bd4dec69fad1fe70",
      "source": "doaj",
      "url": "https://doi.org/10.1038/s41598-026-63525-9"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:R",
      "subfield": "general"
    }
  ],
  "title": "Temporal-spatial reasoning over hypergraph knowledge structures for multimodal retrieval-augmented generation",
  "title_text": "Temporal-spatial reasoning over hypergraph knowledge structures for multimodal retrieval-augmented generation",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:45:57Z",
  "url": "https://research.draconitic.com/paper/6rdfz12atn17b",
  "venue": "Scientific Reports",
  "versions": []
}