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    "story_id": "mp-2026-11-08-004",
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    "headline": "Opaque User Embeddings Split Into Named Modes",
    "slug": "opaque-user-embeddings-split-into-named-modes",
    "dek": "Sparse autoencoders recover behavioral components that can be queried in language without retraining the recommendation model.",
    "summary": "Sparse autoencoders recover behavioral components that can be queried in language without retraining the recommendation model.",
    "body_text": "The authors treat dense user embeddings as superpositions of sparse behavioral modes and recover them with dictionary-learning methods. On synthetic data with known ground truth, sparse autoencoders outperformed classical sparse baselines, including for transformer-encoded embeddings. Applied to classical and transformer-based embeddings from two real datasets, the recovered modes could be named and used for zero-shot audience retrieval over representations that were never trained on text. The method is post-hoc and model-agnostic, although interpretability of recovered labels still depends on the evaluation setup.",
    "why_it_matters": "Sparse autoencoders recover behavioral components that can be queried in language without retraining the recommendation model.",
    "limitations": [],
    "importance": 9,
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    "first_published_at": "2026-11-08T09:00:00.000-05:00",
    "modified_at": "2026-11-08T09:00:00.000-05:00",
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    "tags": [
      "recommendation",
      "sparse autoencoders",
      "interpretability"
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  "sources": [
    {
      "source_id": "source-2026-11-08-004",
      "title": "arXiv preprint 2611.08487, original v1",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2611.08487v1",
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      "published_at": "2026-11-05T13:59:42.000-05:00",
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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": "Opaque User Embeddings Split Into Named Modes",
    "publisher": "The Machine Press",
    "published_at": "2026-11-08T09:00:00.000-05:00",
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