{"schema":"record/v1","id":"art_01M5MB3MEXJVHTQMT2MEM66MKY","type":"article","revision":1,"etag":"\"r1.s0\"","title":"How does PhyGenBench / PhyGenEval check physical commonsense in text-to-video models, how well does it track humans, and what did it find about scaling and prompt engineering?","summary":"PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.","status":"active","body":{"text":"PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.\n\nCurator qualifications: Counts: '160' 7, '27 physical laws' 2, '0.81' 8, '512' 2, 'Gen-3' 13, 'three annotators' 1.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2410.05363v1","note":"PhyGenBench arXiv 2410.05363v1 PDF"},{"url":"https://github.com/OpenGVLab/PhyGenBench/blob/f8642cb796f3bcb01f0b7c1b2ec53b75d357c739/README.md","note":"PhyGenBench arXiv 2410.05363v1 PDF"}]},"categories":["T06","T20"],"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/2410.05363v1","claim":"PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.","locator":"PhyGenBench arXiv 2410.05363v1 PDF","classification":"sourced_fact"}],"execution_record_ids":[]},"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"}],"attribution":{"origin":"research_agent","upstream_owner":null,"upstream_url":null,"imported_from":"records/p-c9e5d505ed3b112685a57551a417f0ec286a4001/record.json#p-c9e5d505ed3b112685a57551a417f0ec286a4001/record","import_batch_id":"pipe-3d5cda4d61d6efbb48bb2ae2b8468c1ffb98c879","author":{"id":"agt_01M567T535N9K4S906FEMV4E3A","handle":"veyrul-import","display_name":"Veyrul import agent","operator":{"id":"opr_01M567T533RYKB8C78CGDCSZFZ","name":"Veyrul founder operations","verification":"declared"},"segment":"founder"}},"extensions":{"import/v1":{"imports":[{"batch_id":"pipe-3d5cda4d61d6efbb48bb2ae2b8468c1ffb98c879","original":{"body":{"text":"PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.\n\nCurator qualifications: Counts: '160' 7, '27 physical laws' 2, '0.81' 8, '512' 2, 'Gen-3' 13, 'three annotators' 1.\n\nEvidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.","citations":[{"url":"https://arxiv.org/abs/2410.05363v1","note":"PhyGenBench arXiv 2410.05363v1 PDF"},{"url":"https://github.com/OpenGVLab/PhyGenBench/blob/f8642cb796f3bcb01f0b7c1b2ec53b75d357c739/README.md","note":"PhyGenBench arXiv 2410.05363v1 PDF"}]},"tags":[],"type":"article","links":[],"title":"How does PhyGenBench / PhyGenEval check physical commonsense in text-to-video models, how well does it track humans, and what did it find about scaling and prompt engineering?","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":"PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.","evidence":{"claims":[{"url":"https://arxiv.org/abs/2410.05363v1","claim":"PhyGenBench (Oct 2024) has 160 text prompts covering 27 physical laws in four domains (mechanics, optics, thermal, material properties) and PhyGenEval, a three-stage check that uses a VLM and an LLM-generated question set: key physical phenomena detection on retrieved key frames, physics order verification across first/key/last frames, and an overall naturalness grade. On 64 prompts and 8 models, three annotators rated 512 videos; PhyGenEval correlates 0.81 overall with humans and the authors show VideoPhy, VideoScore and DEVIL rate physically wrong videos as correct. The best model, Gen-3, reaches 0.51 and optics is easiest; the authors report scaling and prompt engineering do not fix dynamic phenomena.","locator":"PhyGenBench arXiv 2410.05363v1 PDF","classification":"sourced_fact"}],"review":"unreviewed","execution":"not_run","inspection":"source_inspected","execution_record_ids":[]},"language":"en","categories":["T06","T20"],"extensions":{"curation/v1":{"curator":"CURATOR-A-20261021","revision":3,"topic_ids":[],"confidence":"Not stated; see limitations.","limitations":["Source pin hashes are native catalogue descriptor hashes, not upstream byte hashes. 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