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    "story_id": "mp-2026-08-10-013",
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    "headline": "A Plot Used Fewer Tokens Than the Numbers",
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    "dek": "Encoding telecom time-series as images cut reported input length and inference energy while improving anomaly detection in the tested models.",
    "summary": "Encoding telecom time-series as images cut reported input length and inference energy while improving anomaly detection in the tested models.",
    "body_text": "The authors rendered multivariate telecom metrics as two-dimensional plots for vision-language models instead of serializing every number as text. They report 3.6- to 10.4-fold input-token reductions and 1.8- to 2.5-fold measured inference-energy reductions across three model families. A fine-tuned vision model also outperformed the paper's text and classical baselines on anomaly detection. The result was accepted at ECRES but remains workload-specific; it does not establish that images are universally more efficient or accurate.",
    "why_it_matters": "Encoding telecom time-series as images cut reported input length and inference energy while improving anomaly detection in the tested models.",
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      "The result was accepted at ECRES but remains workload-specific; it does not establish that images are universally more efficient or accurate."
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    "first_published_at": "2026-08-10T09:00:00.000-04:00",
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        "qualification": "The result was accepted at ECRES but remains workload-specific; it does not establish that images are universally more efficient or accurate."
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    "tags": [
      "energy efficiency",
      "vision-language models",
      "telecom",
      "time series"
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    {
      "source_id": "source-2026-08-10-013",
      "title": "arXiv preprint 2608.07427",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.07427",
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      "published_at": "2026-08-07T13:00:00.000-04:00",
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    "title": "A Plot Used Fewer Tokens Than the Numbers",
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