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    "story_id": "mp-2026-10-25-014",
    "source_story_id": "tmp-story-crane-multimodal-counterfactuals",
    "edition_id": "mp-2026-10-25-morning-0107",
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    "position": 14,
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    "section": "benchmarks-evals",
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    "headline": "The Counterfactual Must Be Close in Every Modality",
    "slug": "the-counterfactual-must-be-close-in-every-modality",
    "dek": "CRANE retrieves real training examples whose text, image or other components jointly resemble the query while belonging to the alternate class.",
    "summary": "CRANE retrieves real training examples whose text, image or other components jointly resemble the query while belonging to the alternate class.",
    "body_text": "Most counterfactual explanations are built for one data type, while multimodal attribution methods often explain each input independently. CRANE is a post-hoc, model-agnostic retrieval framework that searches real training examples from the target class. Its strongest strategy intersects nearest-neighbor sets from each modality, grounding the explanation in a jointly observed example rather than synthesizing an unconstrained alternative. Across four heterogeneous datasets and three fusion strategies, multimodal consensus retrieval produced the best or joint-best result on three datasets, while single-modality methods remained competitive when one channel dominated the decision. The approach inherits the coverage and biases of the training set and does not show that a nearby example is a causal explanation.",
    "why_it_matters": "CRANE retrieves real training examples whose text, image or other components jointly resemble the query while belonging to the alternate class.",
    "limitations": [
      "The approach inherits the coverage and biases of the training set and does not show that a nearby example is a causal explanation."
    ],
    "importance": 8,
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    "first_published_at": "2026-10-25T09:00:00.000-04:00",
    "modified_at": "2026-10-25T09:00:00.000-04:00",
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        "text": "CRANE retrieves real training examples whose text, image or other components jointly resemble the query while belonging to the alternate class.",
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        "qualification": "The approach inherits the coverage and biases of the training set and does not show that a nearby example is a causal explanation."
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    "source_ids": [
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    "tags": [
      "explainability",
      "multimodal models",
      "counterfactuals",
      "retrieval"
    ],
    "image_url": null,
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-10-25-014",
      "title": "arXiv preprint 2610.31594, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2610.31594v1",
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      "published_at": "2026-10-22T13:26:58.000-04:00",
      "accessed_at": "2026-10-25T08:17:14.361-04:00",
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  "publisher": {
    "name": "The Machine Press",
    "url": "https://themachinepress.com",
    "description": "A daily newspaper for the age of artificial intelligence."
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  "cite_this_report": {
    "title": "The Counterfactual Must Be Close in Every Modality",
    "publisher": "The Machine Press",
    "published_at": "2026-10-25T09:00:00.000-04:00",
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