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  "title": "RoboHarn-Evo: Knowledge-Layer RSI",
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        "paper_id": "transformer-transformer_blog_transformer_transformer_a_unified_20260807_2026_08",
        "title": "Transformer Transformer: A Unified Model for Motion-Conditioned Robot Co-design",
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        "summary": "Fixing the model and actuators while updating task-action two-layer knowledge contradicts the route's claim: task failure should not be attributed to the policy by default; the body-controller-policy should enter the same optimization problem",
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        "summary": "Fixing the model and actuators while updating task-action two-layer knowledge contradicts the route's bet: the behavior/action part must be a unified model, opposing modular assembly",
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        "summary": "Self-improvement is limited to knowledge content and retrieval eligibility, not self-rewriting of the model or controller contradicts the core narrative (company's own account, not third-party verified): these capabilities emerge directly from scaled pretraining without any dedicated mechanism design",
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        "title": "Agile-WAM: Lightweight Visuotactile Joint Prediction",
        "url": "https://haiguangboy.com/posts/agile-wam",
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        "summary": "Real-robot evaluation covers only three tasks with 8 scenarios each, and the π0.5 baseline was not fine-tuned on real robots validates that small-sample and cherry-picked evaluation cannot be extrapolated to stable general capabilities",
        "strength": "strong"
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        "summary": "Combining task and action knowledge outperforms using each alone validates MM-DiT action expert: joint global attention fusion outperforms naive DiT",
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        "url": "https://haiguangboy.com/posts/latent_action_pretraining_through_world_modeling",
        "relation": "same_track",
        "summary": "LAWM: Why Action Labels Become a Burden",
        "strength": "strong"
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    "text": "💡 Comparison with Other Improvement Routes\nPAVE computes advantages per action chunk, avoiding penalizing remedial actions in failed tasks together, but still requires retraining the policy each round. This work instead writes experience gains into external knowledge.\n\nSmoothRL requires optimization to align with the actions actually executed, while this work requires evidence to correspond to the policy actually executed. The common question is: after failure, where exactly should the system change so that the next execution is more reliable?"
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