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  "title": "Agile-WAM: Lightweight Visuotactile Joint Prediction",
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        "paper_id": "world_action_models_for_robot_learning_and_control_a_survey_2026_09",
        "title": "WAM Survey: How Prediction Serves Control",
        "url": "https://haiguangboy.com/posts/wam-survey",
        "relation": "contrast",
        "summary": "Retaining a large latent space but removing the prediction loss still leads to a drop in gains, which contradicts the idea that world prediction and action selection can be optimized separately",
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        "paper_id": "n_0_vtla_scaling_vision_tactile_language_action_model_with_latent_tactile_tokens_2026_07",
        "title": "Tactile as a Prediction Target Rather Than an Observation Input",
        "url": "https://haiguangboy.com/posts/n_0_vtla_scaling_vision_tactile_language_action_model_with_latent_tactile_tokens",
        "relation": "contrast",
        "summary": "Tactile as a Prediction Target Rather Than an Observation Input",
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      {
        "paper_id": "jepa_wam_learning_vision_language_action_policies_with_joint_embedding_world_modeling_2026_08",
        "title": "JEPA-WAM: An Innovative Method That Makes World Models Better Understand State Changes",
        "url": "https://haiguangboy.com/posts/jepa-wam",
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        "summary": "Retaining a large latent space but removing the prediction loss still leads to a drop in gains, which validates five ablation groups: joint objective 79.2 versus future-only objective 77.3, and preserving spatial structure versus iREPA convolution 74.7",
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        "title": "N0-Foundation: Opening a New Era of Touch",
        "url": "https://haiguangboy.com/posts/mathcaln_0_foundation_towards_the_age_of_tactile_intelligence",
        "relation": "same_track",
        "summary": "N0-Foundation: Opening a New Era of Touch",
        "strength": "strong"
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        "paper_id": "prism_precision_and_contact_rich_real_world_industrial_skill_dataset_with_multim_2026_08",
        "title": "PRISM: Can Scale Replace Explicit Modeling?",
        "url": "https://haiguangboy.com/posts/prism_precision_and_contact_rich_real_world_industrial_skill_dataset_with_multim",
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        "summary": "PRISM: Can Scale Replace Explicit Modeling?",
        "strength": "strong"
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        "title": "SmoothRL: Only Account for Actions That Are Actually Executed",
        "url": "https://haiguangboy.com/posts/smoothrl_online_reinforcement_learning_during_asynchronous_execution",
        "relation": "same_track",
        "summary": "SmoothRL: Only Account for Actions That Are Actually Executed",
        "strength": "strong"
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  },
  "analyst_take": {
    "type": "author_opinion",
    "text": "Viewed together with T-Rex, the difference is quite specific: T-Rex separates slow visual and fast tactile correction pathways during execution; Agile-WAM distinguishes visuotactile prediction horizons during training. Both value timescales, but \"next-step tactile prediction\" is not equivalent to a \"high-frequency tactile closed loop.\"\nWhat is worth re-examining from an engineering perspective is: after changing tasks or sampling rates, does separately setting prediction horizons still outperform a unified horizon? This determines whether it can become a reusable method, rather than just an advantage under the current configuration."
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  "published_at": "2026-09-21T17:55:00+08:00",
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