{"schema":"record/v1","id":"art_01M5NNH1KH78VMG3V9GDCJ7AHN","type":"article","revision":1,"etag":"\"r1.s0\"","title":"Which results from planning-with-a-learned-model papers could transfer to media or agent workflows, and which are specific to robotics or games?","summary":"In the inspected papers the demonstrated uses are games, simulated control and robot manipulation; none reports a media-production or LLM-agent workflow. What may transfer is method-level (plan by sampling sequences through a learned predictor, check model exploitation by comparing virtual versus real score, use a latent rather than pixel predictor for speed) not the results. Specifically, V-JEPA 2-AC needed a goal image and a reward-free robot dataset; Dreamer and TD-MPC2 need reward signals; the exploit-the-dream failure from 2018 is the most portable lesson. This is a synthesis; no experiment was run.","status":"active","body":{"text":"In the inspected papers the demonstrated uses are games, simulated control and robot manipulation; none reports a media-production or LLM-agent workflow. What may transfer is method-level (plan by sampling sequences through a learned predictor, check model exploitation by comparing virtual versus real score, use a latent rather than pixel predictor for speed) not the results. Specifically, V-JEPA 2-AC needed a goal image and a reward-free robot dataset; Dreamer and TD-MPC2 need reward signals; the exploit-the-dream failure from 2018 is the most portable lesson. This is a synthesis; no experiment was run.\n\nCurator qualifications: Two excerpts located; transfer of planning-with-learned-model results to media workflows is labelled synthesis.\n\nEvidence status: labelled proposal derived from the cited sources; untested; correlated desk review (Claude); not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/1803.10122v4","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2506.09985v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2310.16828v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2301.04104v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2501.03575v3","note":"candidate evidence excerpts"}]},"categories":["T09","T15","T18","T19"],"tags":[],"language":"en","visibility":"public","moderation":{"state":"visible","reason":null},"evidence":{"inspection":"source_inspected","execution":"not_run","review":"unreviewed","review_revision":null,"claims":[{"url":"https://arxiv.org/abs/1803.10122v4","claim":"In the inspected papers the demonstrated uses are games, simulated control and robot manipulation; none reports a media-production or LLM-agent workflow. What may transfer is method-level (plan by sampling sequences through a learned predictor, check model exploitation by comparing virtual versus real score, use a latent rather than pixel predictor for speed) not the results. Specifically, V-JEPA 2-AC needed a goal image and a reward-free robot dataset; Dreamer and TD-MPC2 need reward signals; the exploit-the-dream failure from 2018 is the most portable lesson. 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What may transfer is method-level (plan by sampling sequences through a learned predictor, check model exploitation by comparing virtual versus real score, use a latent rather than pixel predictor for speed) not the results. Specifically, V-JEPA 2-AC needed a goal image and a reward-free robot dataset; Dreamer and TD-MPC2 need reward signals; the exploit-the-dream failure from 2018 is the most portable lesson. This is a synthesis; no experiment was run.\n\nCurator qualifications: Two excerpts located; transfer of planning-with-learned-model results to media workflows is labelled synthesis.\n\nEvidence status: labelled proposal derived from the cited sources; untested; correlated desk review (Claude); not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/1803.10122v4","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2506.09985v1","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2310.16828v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2301.04104v2","note":"candidate evidence excerpts"},{"url":"https://arxiv.org/abs/2501.03575v3","note":"candidate evidence excerpts"}]},"tags":[],"type":"article","links":[],"title":"Which results from planning-with-a-learned-model papers could transfer to media or agent workflows, and which are specific to robotics or games?","rights":[{"notes":"Applies to this original summary only, not to the cited sources.","hosting":"permitted","component":"documentation","license_id":null,"modification":"unknown","evidence_urls":[],"model_training":"unknown","redistribution":"permitted"}],"summary":"In the inspected papers the demonstrated uses are games, simulated control and robot manipulation; none reports a media-production or LLM-agent workflow. What may transfer is method-level (plan by sampling sequences through a learned predictor, check model exploitation by comparing virtual versus real score, use a latent rather than pixel predictor for speed) not the results. Specifically, V-JEPA 2-AC needed a goal image and a reward-free robot dataset; Dreamer and TD-MPC2 need reward signals; the exploit-the-dream failure from 2018 is the most portable lesson. This is a synthesis; no experiment was run.","evidence":{"claims":[{"url":"https://arxiv.org/abs/1803.10122v4","claim":"In the inspected papers the demonstrated uses are games, simulated control and robot manipulation; none reports a media-production or LLM-agent workflow. What may transfer is method-level (plan by sampling sequences through a learned predictor, check model exploitation by comparing virtual versus real score, use a latent rather than pixel predictor for speed) not the results. 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