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    "story_id": "mp-2026-09-01-002",
    "source_story_id": "tmp-feature-long-horizon-navigation",
    "edition_id": "mp-2026-09-01-morning-0054",
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    "section": "robotics",
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    "headline": "The Planner Learned to Keep the Journey Open",
    "slug": "the-planner-learned-to-keep-the-journey-open",
    "dek": "NavMCP couples high-level reasoning to a navigation foundation model and carries evidence, negative findings and unfinished goals across repeated trips.",
    "summary": "NavMCP couples high-level reasoning to a navigation foundation model and carries evidence, negative findings and unfinished goals across repeated trips.",
    "body_text": "The framework assigns a vision-language model the long-horizon work of choosing evidence, search locations and stopping conditions, while a navigation foundation model executes each semantic sub-goal in a closed loop. Intent, observation and memory channels turn isolated trips into a persistent investigation without retraining either model. The authors report state-of-the-art results on three embodied-question-answering benchmarks, a 14.9-point advantage over an episodic interface on HM-EQA with matched backbones, and 78.3 percent success on a Unitree Go2. Those figures describe the paper's controlled tasks and robot setup, not general autonomous navigation.",
    "why_it_matters": "NavMCP couples high-level reasoning to a navigation foundation model and carries evidence, negative findings and unfinished goals across repeated trips.",
    "limitations": [
      "Those figures describe the paper's controlled tasks and robot setup, not general autonomous navigation."
    ],
    "importance": 10,
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    "first_published_at": "2026-09-01T09:00:00.000-04:00",
    "modified_at": "2026-09-01T09:00:00.000-04:00",
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        "text": "NavMCP couples high-level reasoning to a navigation foundation model and carries evidence, negative findings and unfinished goals across repeated trips.",
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        "qualification": "Those figures describe the paper's controlled tasks and robot setup, not general autonomous navigation."
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    "tags": [
      "embodied agents",
      "navigation",
      "persistent memory"
    ],
    "image_url": "https://themachinepress.com/issues/2026-09-01/feature-long-horizon-navigation.png",
    "corrections": []
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  "sources": [
    {
      "source_id": "source-2026-09-01-002",
      "title": "arXiv preprint 2608.30396",
      "publisher": "arXiv",
      "url": "https://arxiv.org/abs/2608.30396",
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      "source_type": "primary_research",
      "is_primary_source": true,
      "published_at": "2026-08-31T03:50:16.000-04:00",
      "accessed_at": "2026-09-01T08:25:35.000-04:00",
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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 Planner Learned to Keep the Journey Open",
    "publisher": "The Machine Press",
    "published_at": "2026-09-01T09:00:00.000-04:00",
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