{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:BDLC7ME7EULMZBMMVGLL2XJRKR","short_pith_number":"pith:BDLC7ME7","canonical_record":{"source":{"id":"2501.09781","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-16T18:59:10Z","cross_cats_sorted":[],"title_canon_sha256":"2f75d56b664ed6290c94b27d6201bd6a34fea24aeb6ffe5d228937fbe427f9bc","abstract_canon_sha256":"2316b416e7d1777926e4ca685c59b7569aa2c026755213aa2c663d6a72b60aff"},"schema_version":"1.0"},"canonical_sha256":"08d62fb09f2516cc858ca996bd5d31545f31f9102791c500a49fdf3561d907b8","source":{"kind":"arxiv","id":"2501.09781","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09781","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09781v2","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09781","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"BDLC7ME7EULM","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"BDLC7ME7EULMZBMM","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"BDLC7ME7","created_at":"2026-07-05T10:24:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:BDLC7ME7EULMZBMMVGLL2XJRKR","target":"record","payload":{"canonical_record":{"source":{"id":"2501.09781","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-16T18:59:10Z","cross_cats_sorted":[],"title_canon_sha256":"2f75d56b664ed6290c94b27d6201bd6a34fea24aeb6ffe5d228937fbe427f9bc","abstract_canon_sha256":"2316b416e7d1777926e4ca685c59b7569aa2c026755213aa2c663d6a72b60aff"},"schema_version":"1.0"},"canonical_sha256":"08d62fb09f2516cc858ca996bd5d31545f31f9102791c500a49fdf3561d907b8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:24:30.091174Z","signature_b64":"PkaRTqSbC7M69efInnkYwzbZdHDV3qn9CHDSEiGHtT22EBzzc71ZGxDTg1mMseXEruQJu7HTGAl2tExlQN9gAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"08d62fb09f2516cc858ca996bd5d31545f31f9102791c500a49fdf3561d907b8","last_reissued_at":"2026-07-05T10:24:30.090746Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:24:30.090746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.09781","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YJ2AdqGvqi9BXgg1OHAAcr4krNiC7oQvinFUIQoNIAK7N7KJOMhxnA1G7LSWRfcxsVZGtPDHx6nfUqaQwDfbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:50:23.118634Z"},"content_sha256":"1d275f0de909f800264bfeed15602c1d3797b70ac06f21512d012f1f027040a2","schema_version":"1.0","event_id":"sha256:1d275f0de909f800264bfeed15602c1d3797b70ac06f21512d012f1f027040a2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:BDLC7ME7EULMZBMMVGLL2XJRKR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"VideoWorld: Exploring Knowledge Learning from Unlabeled Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bingyi Kang, Jiashi Feng, Xiaojie Jin, Xun Guo, Yao Zhao, Yunchao Wei, Zhongwei Ren","submitted_at":"2025-01-16T18:59:10Z","abstract_excerpt":"This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language models (LLMs). We develop VideoWorld, an auto-regressive video generation model trained on unlabeled video data, and test its knowledge acquisition abilities in video-based Go and robotic control tasks. Our experiments reveal two key findings: (1) video-only training provides sufficient information for learning knowledge, including rules, reasoning and planning capabilities, and (2) the representation of visual chan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09781","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.09781/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:24:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xNTeP/sUkXCj2D2SBb8XhzCC5NpNHhCqVruhtGzluGnOMSIxdoCxXv5wHvvZ26FuyDwXgIN6D4eCwbV4SSHAAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T20:50:23.119532Z"},"content_sha256":"6372e75333f719c10a540d2949682d1a9208e8ce0425bc0b281bdc8e5feeaf96","schema_version":"1.0","event_id":"sha256:6372e75333f719c10a540d2949682d1a9208e8ce0425bc0b281bdc8e5feeaf96"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BDLC7ME7EULMZBMMVGLL2XJRKR/bundle.json","state_url":"https://pith.science/pith/BDLC7ME7EULMZBMMVGLL2XJRKR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BDLC7ME7EULMZBMMVGLL2XJRKR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T20:50:23Z","links":{"resolver":"https://pith.science/pith/BDLC7ME7EULMZBMMVGLL2XJRKR","bundle":"https://pith.science/pith/BDLC7ME7EULMZBMMVGLL2XJRKR/bundle.json","state":"https://pith.science/pith/BDLC7ME7EULMZBMMVGLL2XJRKR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BDLC7ME7EULMZBMMVGLL2XJRKR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:BDLC7ME7EULMZBMMVGLL2XJRKR","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2316b416e7d1777926e4ca685c59b7569aa2c026755213aa2c663d6a72b60aff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-16T18:59:10Z","title_canon_sha256":"2f75d56b664ed6290c94b27d6201bd6a34fea24aeb6ffe5d228937fbe427f9bc"},"schema_version":"1.0","source":{"id":"2501.09781","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.09781","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"arxiv_version","alias_value":"2501.09781v2","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09781","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_12","alias_value":"BDLC7ME7EULM","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_16","alias_value":"BDLC7ME7EULMZBMM","created_at":"2026-07-05T10:24:30Z"},{"alias_kind":"pith_short_8","alias_value":"BDLC7ME7","created_at":"2026-07-05T10:24:30Z"}],"graph_snapshots":[{"event_id":"sha256:6372e75333f719c10a540d2949682d1a9208e8ce0425bc0b281bdc8e5feeaf96","target":"graph","created_at":"2026-07-05T10:24:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.09781/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work explores whether a deep generative model can learn complex knowledge solely from visual input, in contrast to the prevalent focus on text-based models like large language models (LLMs). We develop VideoWorld, an auto-regressive video generation model trained on unlabeled video data, and test its knowledge acquisition abilities in video-based Go and robotic control tasks. Our experiments reveal two key findings: (1) video-only training provides sufficient information for learning knowledge, including rules, reasoning and planning capabilities, and (2) the representation of visual chan","authors_text":"Bingyi Kang, Jiashi Feng, Xiaojie Jin, Xun Guo, Yao Zhao, Yunchao Wei, Zhongwei Ren","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-16T18:59:10Z","title":"VideoWorld: Exploring Knowledge Learning from Unlabeled Videos"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09781","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1d275f0de909f800264bfeed15602c1d3797b70ac06f21512d012f1f027040a2","target":"record","created_at":"2026-07-05T10:24:30Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2316b416e7d1777926e4ca685c59b7569aa2c026755213aa2c663d6a72b60aff","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-16T18:59:10Z","title_canon_sha256":"2f75d56b664ed6290c94b27d6201bd6a34fea24aeb6ffe5d228937fbe427f9bc"},"schema_version":"1.0","source":{"id":"2501.09781","kind":"arxiv","version":2}},"canonical_sha256":"08d62fb09f2516cc858ca996bd5d31545f31f9102791c500a49fdf3561d907b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"08d62fb09f2516cc858ca996bd5d31545f31f9102791c500a49fdf3561d907b8","first_computed_at":"2026-07-05T10:24:30.090746Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:24:30.090746Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PkaRTqSbC7M69efInnkYwzbZdHDV3qn9CHDSEiGHtT22EBzzc71ZGxDTg1mMseXEruQJu7HTGAl2tExlQN9gAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:24:30.091174Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.09781","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d275f0de909f800264bfeed15602c1d3797b70ac06f21512d012f1f027040a2","sha256:6372e75333f719c10a540d2949682d1a9208e8ce0425bc0b281bdc8e5feeaf96"],"state_sha256":"9625885181bc163f2f4d926ec4c1ae9d166543bc6ec434462587d137954d8ed8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nVOln68/eCaBZPlGl0iRxz6BFxR2ilSz6pcU4T+9zTJi/cPbNijbpJvc/WoLn5FiNva/+sNh5uH9+w4Zw0JKAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T20:50:23.126952Z","bundle_sha256":"312274188e4667fbf3e5892d2b70dd2e6720f487658b8ba71019480d46c59189"}}