{
  "version": "1.0.0",
  "checkedDate": "2026-07-22",
  "rows": [
    {
      "claimId": "WAM-STAGE-001",
      "entityId": "nvidia-wam-stage-framing",
      "sourceReportedVersion": "NVIDIA Technical Blog, 2026-07-15",
      "field": "stage boundary",
      "value": "video/world-model pretraining; robot-action fine-tuning",
      "unit": "stage",
      "primaryUrl": "https://developer.nvidia.com/blog/pretrained-to-imagine-fine-tuned-to-act-the-rise-of-world-action-models/",
      "sourceType": "vendor",
      "sourceId": "docs-nvidia-world-action-models",
      "locator": "Sections ‘Pretrained to Imagine’ and ‘Fine-Tuned to Act’",
      "checkedDate": "2026-07-22",
      "retrievalHash": "unknown — no immutable publisher snapshot is exposed",
      "confidence": "medium",
      "status": "human",
      "wamStage": "pretraining → robot-action fine-tuning",
      "paradigm": "two-stage hybrid",
      "modalityReportedUnit": "video for pretraining; robot trajectories for fine-tuning; quantity not reported",
      "whatItProves": "The source separates video/world-model pretraining from later robot-action fine-tuning.",
      "limitations": "source-reported, not independently validated; this vendor framing is not proof that every WAM uses paired actions during pretraining.",
      "buyerImplication": "Budget passive video and embodiment-aligned robot actions as different acquisition stages."
    },
    {
      "claimId": "WAM-DISCOURSE-002",
      "entityId": "awesome-wam-research-area",
      "sourceReportedVersion": "Awesome-WAM repository, checked 2026-07-22",
      "field": "research-area status",
      "value": "named curated area",
      "unit": "discourse signal",
      "primaryUrl": "https://github.com/OpenMOSS/Awesome-WAM",
      "sourceType": "project",
      "sourceId": "project-github-com-openmoss-awesome-wam",
      "locator": "Repository README taxonomy and paper list",
      "checkedDate": "2026-07-22",
      "retrievalHash": "unknown — repository commit was not exposed by the reviewed page",
      "confidence": "medium",
      "status": "human",
      "wamStage": "field taxonomy",
      "paradigm": "multiple WAM formulations",
      "modalityReportedUnit": "not applicable; catalog, not a training report",
      "whatItProves": "The repository curates work under the World Action Model name.",
      "limitations": "source-reported, not independently validated; a curated list does not prove performance, dataset scale, or one settled definition.",
      "buyerImplication": "Treat WAM as an evolving family name and require a stage-specific data specification from each model team."
    },
    {
      "claimId": "WAM-PARADIGM-003",
      "entityId": "world-model-survey-taxonomy",
      "sourceReportedVersion": "arXiv:2605.00080",
      "field": "world-model paradigm",
      "value": "passive; controllable; inverse-dynamics; joint; latent",
      "unit": "paradigm",
      "primaryUrl": "https://arxiv.org/pdf/2605.00080",
      "sourceType": "paper",
      "sourceId": "paper-arxiv-org-abs-2605-00080",
      "locator": "Survey taxonomy sections",
      "checkedDate": "2026-07-22",
      "retrievalHash": "unknown — no retrieval snapshot is committed",
      "confidence": "medium",
      "status": "human",
      "wamStage": "architecture selection before data specification",
      "paradigm": "passive / controllable / inverse-dynamics / joint / latent",
      "modalityReportedUnit": "varies by paradigm; the survey does not report one universal data unit",
      "whatItProves": "The survey distinguishes multiple world-model formulations rather than one universal action-label requirement.",
      "limitations": "source-reported, not independently validated; this preprint taxonomy may change and does not by itself validate a robot policy.",
      "buyerImplication": "Ask which formulation is being trained before specifying action labels, controls, or evaluation data."
    },
    {
      "claimId": "WAM-JOINT-004",
      "entityId": "wa-rl-joint-optimization",
      "sourceReportedVersion": "CVPR 2026 Workshops proceedings",
      "field": "online optimization target",
      "value": "world model and actor jointly optimized",
      "unit": "model component",
      "primaryUrl": "https://openaccess.thecvf.com/content/CVPR2026W/GigaBrainChallenge/supplemental/Qian_WA-RL_World-Action_Model_CVPRW_2026_supplemental.pdf",
      "sourceType": "paper",
      "sourceId": "paper-cvf-wa-rl-2026",
      "locator": "Method and supplemental experiments",
      "checkedDate": "2026-07-22",
      "retrievalHash": "unknown — no retrieval snapshot is committed",
      "confidence": "high",
      "status": "human",
      "wamStage": "online robot optimization",
      "paradigm": "joint world model + actor",
      "modalityReportedUnit": "expert trajectories and online environment interaction; universal quantity not reported",
      "whatItProves": "WA-RL is a concrete formulation that jointly optimizes a world model and actor through online interaction.",
      "limitations": "source-reported, not independently validated; the workshop result is setting-specific and does not establish a generally deployable recipe.",
      "buyerImplication": "Plan an online-interaction and held-out evaluation budget if the selected formulation updates both components after demonstrations."
    }
  ]
}