{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:VEN7KABWFZS3J3TJ2MU5QPUGRD","short_pith_number":"pith:VEN7KABW","canonical_record":{"source":{"id":"2203.14101","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-26T15:38:00Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"338470e02e6aa0bf3f6345d236e17334de33a76438f8b3bb48a6dad1c19aa821","abstract_canon_sha256":"454e53a4aab30651ec1ae573d1b63f27860de59f87ccf8cd02faa31458820a37"},"schema_version":"1.0"},"canonical_sha256":"a91bf500362e65b4ee69d329d83e8688d64d60a68e48722f8002c7afdb281f57","source":{"kind":"arxiv","id":"2203.14101","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14101","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14101v4","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14101","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"pith_short_12","alias_value":"VEN7KABWFZS3","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"pith_short_16","alias_value":"VEN7KABWFZS3J3TJ","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"pith_short_8","alias_value":"VEN7KABW","created_at":"2026-07-05T04:16:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:VEN7KABWFZS3J3TJ2MU5QPUGRD","target":"record","payload":{"canonical_record":{"source":{"id":"2203.14101","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-26T15:38:00Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"338470e02e6aa0bf3f6345d236e17334de33a76438f8b3bb48a6dad1c19aa821","abstract_canon_sha256":"454e53a4aab30651ec1ae573d1b63f27860de59f87ccf8cd02faa31458820a37"},"schema_version":"1.0"},"canonical_sha256":"a91bf500362e65b4ee69d329d83e8688d64d60a68e48722f8002c7afdb281f57","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:16:18.859998Z","signature_b64":"Z3i7BB6oR7yWFwCOiihK5LgIUrWoc+pC0i0zY8kiAlC2abVi14xTJtkyL+Ntxp+7YIJ4I8IcAW35XEcxa3z8Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a91bf500362e65b4ee69d329d83e8688d64d60a68e48722f8002c7afdb281f57","last_reissued_at":"2026-07-05T04:16:18.859459Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:16:18.859459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.14101","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:16:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r3ZU2468hfSSMTcVnfj2gFwU2B1c/io7SVFUf7ooTIBHEDK+yaPO8wCjlqAPL1zCuGLTrNW11h4U1+7IZuF2Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T03:06:22.852347Z"},"content_sha256":"39f85cf583cb29a0a6bdd33ebb0b37294cd2bf9074c1d25920597a7a19594db8","schema_version":"1.0","event_id":"sha256:39f85cf583cb29a0a6bdd33ebb0b37294cd2bf9074c1d25920597a7a19594db8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:VEN7KABWFZS3J3TJ2MU5QPUGRD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Roadmap for Big Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Anwen Hu, Baobao Chang, Borui Zhang, Boxi Cao, Chaojun Xiao, Chence Shi, Chujie Zheng, Cong Fang, Fangwei Zhu, Ganqu Cui, Guoqiang Wang, Hang Su, Hanyu Zhao, Haoran Li, Hao Sun, Haoyang Li, Huawei Shen, Hui Zhang, Jiaao He, Jiahong Leng, Jiajun Zhang, Jian Tang, Jiawen Deng, Jidong Zhai, Jie Tang, Jifan Yu, Jing Zhang, Ji-Rong Wen, Jiwen Lu, Juanzi Li, Junwei Bao, Jun Zhu, Lei Hou, Liang Zhang, Lingxiao Huang, Liwei Wang, Long Zhou, Maosong Sun, Mengjie Li, Ming Ding, Minghao Xu, Mingsheng Long, Mingxuan Wang, Minlie Huang, Nanyi Fei, Ning Ding, Peng Cui, Peng Li, Qingxiu Dong, Qin Jin, Quanshi Zhang, Ruihua Song, Rui Yan, Sha Yuan, Shengding Hu, Shuai Zhao, Shulin Cao, Shuo Wang, Tianyu Pang, Weicheng Xue, Weidong Zhan, Weilin Zhao, Weinan Zhang, Weize Chen, Wenliang Zhao, Wenzhao Zheng, Xiang Pan, Xianpei Han, Xiaodong He, Xiaoyu Chu, Xiaozhi Wang, Xigang Cao, Xin Lv, Xu Han, Yang Liu, Yangxiao Liang, Yankai Lin, Yingwei Pan, Yinpeng Dong, Yisen Wang, Yizhao Gao, Yongming Rao, Yuan Yao, Yujia Qin, Zenan ling, Zhenghao Liu, Zheng Liang, Zhengyan Zhang, Zheni Zeng, Zhifang Sui, Zhiwu Lu, Zhixing Tan, Zhiyuan Liu, Zhouchen Lin, Zhou Shao, Zhou Yu, Zijun Yao, Zixuan Ma, Ziyi Wang, Zuobai Zhang","submitted_at":"2022-03-26T15:38:00Z","abstract_excerpt":"With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present, there is a lack of research work that sorts out the overall progress of BMs and guides the follow-up research. In this paper, we cover not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application. We introduce "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14101","kind":"arxiv","version":4},"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/2203.14101/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":"2026-07-05T04:16:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vGgx83yJ/FrG8RE3vuclK/0jXPg9BPDwoPN/bcyKz0+fWXdt+8IXLdlB/Xiu/dCfxUe54FuJJEHDyXREpqL2DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T03:06:22.853249Z"},"content_sha256":"e4d5f1873fa8d8e15438308f489346f5301babd3230194490c3a3a9759517659","schema_version":"1.0","event_id":"sha256:e4d5f1873fa8d8e15438308f489346f5301babd3230194490c3a3a9759517659"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VEN7KABWFZS3J3TJ2MU5QPUGRD/bundle.json","state_url":"https://pith.science/pith/VEN7KABWFZS3J3TJ2MU5QPUGRD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VEN7KABWFZS3J3TJ2MU5QPUGRD/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":"2026-08-18T03:06:22Z","links":{"resolver":"https://pith.science/pith/VEN7KABWFZS3J3TJ2MU5QPUGRD","bundle":"https://pith.science/pith/VEN7KABWFZS3J3TJ2MU5QPUGRD/bundle.json","state":"https://pith.science/pith/VEN7KABWFZS3J3TJ2MU5QPUGRD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VEN7KABWFZS3J3TJ2MU5QPUGRD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:VEN7KABWFZS3J3TJ2MU5QPUGRD","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":"454e53a4aab30651ec1ae573d1b63f27860de59f87ccf8cd02faa31458820a37","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-26T15:38:00Z","title_canon_sha256":"338470e02e6aa0bf3f6345d236e17334de33a76438f8b3bb48a6dad1c19aa821"},"schema_version":"1.0","source":{"id":"2203.14101","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14101","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14101v4","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14101","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"pith_short_12","alias_value":"VEN7KABWFZS3","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"pith_short_16","alias_value":"VEN7KABWFZS3J3TJ","created_at":"2026-07-05T04:16:18Z"},{"alias_kind":"pith_short_8","alias_value":"VEN7KABW","created_at":"2026-07-05T04:16:18Z"}],"graph_snapshots":[{"event_id":"sha256:e4d5f1873fa8d8e15438308f489346f5301babd3230194490c3a3a9759517659","target":"graph","created_at":"2026-07-05T04:16:18Z","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/2203.14101/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present, there is a lack of research work that sorts out the overall progress of BMs and guides the follow-up research. In this paper, we cover not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application. We introduce ","authors_text":"Anwen Hu, Baobao Chang, Borui Zhang, Boxi Cao, Chaojun Xiao, Chence Shi, Chujie Zheng, Cong Fang, Fangwei Zhu, Ganqu Cui, Guoqiang Wang, Hang Su, Hanyu Zhao, Haoran Li, Hao Sun, Haoyang Li, Huawei Shen, Hui Zhang, Jiaao He, Jiahong Leng, Jiajun Zhang, Jian Tang, Jiawen Deng, Jidong Zhai, Jie Tang, Jifan Yu, Jing Zhang, Ji-Rong Wen, Jiwen Lu, Juanzi Li, Junwei Bao, Jun Zhu, Lei Hou, Liang Zhang, Lingxiao Huang, Liwei Wang, Long Zhou, Maosong Sun, Mengjie Li, Ming Ding, Minghao Xu, Mingsheng Long, Mingxuan Wang, Minlie Huang, Nanyi Fei, Ning Ding, Peng Cui, Peng Li, Qingxiu Dong, Qin Jin, Quanshi Zhang, Ruihua Song, Rui Yan, Sha Yuan, Shengding Hu, Shuai Zhao, Shulin Cao, Shuo Wang, Tianyu Pang, Weicheng Xue, Weidong Zhan, Weilin Zhao, Weinan Zhang, Weize Chen, Wenliang Zhao, Wenzhao Zheng, Xiang Pan, Xianpei Han, Xiaodong He, Xiaoyu Chu, Xiaozhi Wang, Xigang Cao, Xin Lv, Xu Han, Yang Liu, Yangxiao Liang, Yankai Lin, Yingwei Pan, Yinpeng Dong, Yisen Wang, Yizhao Gao, Yongming Rao, Yuan Yao, Yujia Qin, Zenan ling, Zhenghao Liu, Zheng Liang, Zhengyan Zhang, Zheni Zeng, Zhifang Sui, Zhiwu Lu, Zhixing Tan, Zhiyuan Liu, Zhouchen Lin, Zhou Shao, Zhou Yu, Zijun Yao, Zixuan Ma, Ziyi Wang, Zuobai Zhang","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-26T15:38:00Z","title":"A Roadmap for Big Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14101","kind":"arxiv","version":4},"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":"sha256:39f85cf583cb29a0a6bdd33ebb0b37294cd2bf9074c1d25920597a7a19594db8","target":"record","created_at":"2026-07-05T04:16:18Z","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":"454e53a4aab30651ec1ae573d1b63f27860de59f87ccf8cd02faa31458820a37","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-03-26T15:38:00Z","title_canon_sha256":"338470e02e6aa0bf3f6345d236e17334de33a76438f8b3bb48a6dad1c19aa821"},"schema_version":"1.0","source":{"id":"2203.14101","kind":"arxiv","version":4}},"canonical_sha256":"a91bf500362e65b4ee69d329d83e8688d64d60a68e48722f8002c7afdb281f57","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a91bf500362e65b4ee69d329d83e8688d64d60a68e48722f8002c7afdb281f57","first_computed_at":"2026-07-05T04:16:18.859459Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:16:18.859459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z3i7BB6oR7yWFwCOiihK5LgIUrWoc+pC0i0zY8kiAlC2abVi14xTJtkyL+Ntxp+7YIJ4I8IcAW35XEcxa3z8Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:16:18.859998Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.14101","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:39f85cf583cb29a0a6bdd33ebb0b37294cd2bf9074c1d25920597a7a19594db8","sha256:e4d5f1873fa8d8e15438308f489346f5301babd3230194490c3a3a9759517659"],"state_sha256":"e42756d6d8b300cea04e242769356df34104fddcb9a61fc7bc2e8fbe3a40081d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UrQZlVQY2bChSnwXNxtDNSe3iEBsOwb7QNHD7tkiZAFBRAhsomCZac5/IK6gvZr9k9NTjA9S2c3IgOTz2zZqCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T03:06:22.859967Z","bundle_sha256":"547a0bb2f15fc69671c4541fa95a4a1a80389f46679f5a5e2070cfa228b6904c"}}