Temporal-spatial reasoning over hypergraph knowledge structures for multimodal retrieval-augmented generation

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.

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Cite

BibTeX RIS

BibTeX

@article{dillan2026temporalspatial,
  title = {{Temporal-spatial reasoning over hypergraph knowledge structures for multimodal retrieval-augmented generation}},
  author = {Timothy Dillan and Sani Muhamad Isa and Abba Suganda Girsang and Derwin Suhartono},
  journal = {Scientific Reports},
  year = {2026},
  month = aug,
  doi = {10.1038/s41598-026-63525-9},
  url = {https://doi.org/10.1038/s41598-026-63525-9},
  note = {CC BY},
}

RIS

TY  - JOUR
TI  - Temporal-spatial reasoning over hypergraph knowledge structures for multimodal retrieval-augmented generation
AU  - Timothy Dillan
AU  - Sani Muhamad Isa
AU  - Abba Suganda Girsang
AU  - Derwin Suhartono
PY  - 2026
DA  - 2026/08/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-63525-9
UR  - https://doi.org/10.1038/s41598-026-63525-9
AB  - 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.
SN  - 2045-2322
ER  -

Abstract

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