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    "headline": "Hard Negatives Stayed Diverse Instead of Collapsing to a Few",
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    "dek": "FlowNeg uses a hierarchical generative flow network to sample informative knowledge-graph counterexamples across modes.",
    "summary": "FlowNeg uses a hierarchical generative flow network to sample informative knowledge-graph counterexamples across modes.",
    "body_text": "Across a five-seed grid of five architectures and five benchmarks, FlowNeg had higher mean reciprocal rank than two comparison methods in 24 of 25 cells. A separate 15-seed control on FB15k-237 with RotatE reported 0.359 versus 0.346 MRR, with fixed diagnostic and compute budgets.",
    "why_it_matters": "FlowNeg uses a hierarchical generative flow network to sample informative knowledge-graph counterexamples across modes.",
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    "tags": [
      "knowledge graphs",
      "negative sampling",
      "GFlowNets"
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  "sources": [
    {
      "source_id": "source-2026-08-26-014",
      "title": "arXiv preprint 2608.23849",
      "publisher": "arXiv",
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      "published_at": "2026-08-24T17:38:34.000-04:00",
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    "title": "Hard Negatives Stayed Diverse Instead of Collapsing to a Few",
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