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        "paper_id": "pointworld_scaling_3d_world_models_for_in_the_wild_robotic_manipulation_2026_08",
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        "summary": "用预训练DINO特征算语义差分(DeltaDINO)效果逼近甚至反超LAPO,在LIBERO上是全场最佳 validates 核心主张:介入点的选择本身就是关键设计变量,而非实现细节",
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        "title": "不是\"要不要世界模型\"，是\"它吐出来的东西有没有结构\"",
        "url": "https://haiguangboy.com/posts/robointer15_a_holistic_intermediate_representation_suite_for_embodied_world_mode",
        "relation": "same_track",
        "summary": "不是\"要不要世界模型\"，是\"它吐出来的东西有没有结构\"",
        "strength": "strong"
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        "paper_id": "causally_debiased_latent_action_model_for_embodied_action_conditioned_world_mode_2026_07",
        "title": "世界模型不听话，是潜在动作被污染了",
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        "relation": "same_track",
        "summary": "所有代理指标(包括论文自己认为最可靠的FDM重建指标)都只能做粗筛,不能替代真实下游任务评测——这是对整个LAM研究方法论的一次警示 validates 评测只在 EgoDex/AgiBot 各 300 clip 的离线 rollout 上,没有闭环任务成功率;训练需 96 张 H100",
        "strength": "strong"
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      {
        "paper_id": "omega_eva_envision_verify_and_act_with_latent_interactive_world_models_2026_08",
        "title": "世界模型：推理时到底要不要想象",
        "url": "https://haiguangboy.com/posts/omega-eva",
        "relation": "same_track",
        "summary": "核心设计原则:潜在动作不该只当预训练阶段的辅助监督信号,必须在下游策略学习全程都保持参与,才能发挥最大价值 validates 问题拆解:现有世界模型用法分三类,没有一类让候选动作真正被自己的想象结果检验并修正",
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    "text": "这篇最锋利的判断是:所有代理指标都只能做粗筛,排不出精细名次,不能替代真实下游评测。这条判断和之前解读过的两篇论文遥相呼应——Omega-0发现离线重建质量更高的模型,换到真机反而执行更迟滞;CDLAM也得出过同样结论,像素级重建指标排不出潜在动作空间的优劣。但耐人寻味的是,这条判断和刚解读过的PointWorld直接顶牛:PointWorld特意放弃任务成功率、改用逐点L2误差当核心指标,理由正是\"成功率会掩盖系统性差异\"——一个说再精细的代理指标也靠不住,一个说换个更精细的指标就能看清差距,两边答案正相反,谁更站得住,还得看更多证据。"
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