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        "summary": "现有消融未隔离世界模型预测目标本身的收益 contradicts 路线赌注:让跨具身迁移涌现的因是世界建模(预测未来视频)本身,不只是数据量——WAM架构优于纯VLA",
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        "paper_id": "jepa_wam_learning_vision_language_action_policies_with_joint_embedding_world_modeling_2026_08",
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        "summary": "真机领先包含后训练差异，不能全算成结构收益 validates 五组消融:联合目标 79.2 对未来单独目标 77.3,保空间结构对 iREPA 卷积 74.7",
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        "relation": "same_track",
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    "type": "author_opinion",
    "text": "📚 ω0同样关注移动与操作的协同。WholeBodyWAM更聚焦已有WAM的人形迁移：把预训练先验、控制器语义和协调机制接起来，让已有操作能力获得全身执行通路。\n\n❇️ 半年后回看，关键是同数据真机对照中结构收益能否保留，以及换控制器时仍需多少映射和微调成本。这两项才直接检验这条适配路径的复用价值。"
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