{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:YWRL2SWCTBWZVZAGRH7NL74ZBR","short_pith_number":"pith:YWRL2SWC","canonical_record":{"source":{"id":"2402.11934","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T08:22:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e20aa65b2700a564c2f201cc66ba04875c00e2ab8a915de9b9f450b578ab38e3","abstract_canon_sha256":"746f8f5571b9436444d48712fd150559b84fc764b9ead98ac79202524374f83a"},"schema_version":"1.0"},"canonical_sha256":"c5a2bd4ac2986d9ae40689fed5ff990c6ed7f8de926f1545e0dac190d6779c31","source":{"kind":"arxiv","id":"2402.11934","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.11934","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"arxiv_version","alias_value":"2402.11934v1","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11934","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_12","alias_value":"YWRL2SWCTBWZ","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_16","alias_value":"YWRL2SWCTBWZVZAG","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_8","alias_value":"YWRL2SWC","created_at":"2026-07-05T07:46:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:YWRL2SWCTBWZVZAGRH7NL74ZBR","target":"record","payload":{"canonical_record":{"source":{"id":"2402.11934","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T08:22:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e20aa65b2700a564c2f201cc66ba04875c00e2ab8a915de9b9f450b578ab38e3","abstract_canon_sha256":"746f8f5571b9436444d48712fd150559b84fc764b9ead98ac79202524374f83a"},"schema_version":"1.0"},"canonical_sha256":"c5a2bd4ac2986d9ae40689fed5ff990c6ed7f8de926f1545e0dac190d6779c31","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:46.393249Z","signature_b64":"aFm/0qxlxeZrjf+oSwmTHjsHdofpsjCCIqaloY9bsx/u7R1xTE/rqAafzcgEdAU95VqK9v9wlFUPYaKPy0T0DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5a2bd4ac2986d9ae40689fed5ff990c6ed7f8de926f1545e0dac190d6779c31","last_reissued_at":"2026-07-05T07:46:46.392874Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:46.392874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.11934","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:46:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6hBx8CAHtiyGPu1SSRGUSXK/MTo/0TNiGTtIsDy7lZyY+cht+WZdxBckmGzeX6vbwWFENTy0JCJtOpYEZzI8BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T02:54:19.209385Z"},"content_sha256":"eb6278a0366c267096679a514b7f57d67cf00b3d52eff1d5dbbbd1d76649a0c3","schema_version":"1.0","event_id":"sha256:eb6278a0366c267096679a514b7f57d67cf00b3d52eff1d5dbbbd1d76649a0c3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:YWRL2SWCTBWZVZAGRH7NL74ZBR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jianxiang Tian, Taihang Wang, Xiangrun Li, Xiaoman Xu, Ye Jiang","submitted_at":"2024-02-19T08:22:51Z","abstract_excerpt":"This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and accuracy. In the monolingual task, we evaluated traditional deep-learning methods, multiscale positive-unlabeled framework (MPU), fine-tuning, adapters and ensemble methods. Then, we selected the top-performing models based on their accuracy from the monolingual models and evaluated them in subtasks A and B. The final model construction employed a stacking ensemble that combined fine-tuning with MPU. Our system achi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11934","kind":"arxiv","version":1},"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/2402.11934/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":"2026-07-05T07:46:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J6cj9aJIjX5l/+p0ArWdon8YTQsG5k5c93fvqb1BWR2B+/a89ArpqHnpRNEF+04Mq4Fy2CQD1RkHEzUl8TCCDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T02:54:19.210291Z"},"content_sha256":"a5688f9d9abee9a76b9f6ddd2f92712b896878c52aa6539aa8ee0337e7e80d4f","schema_version":"1.0","event_id":"sha256:a5688f9d9abee9a76b9f6ddd2f92712b896878c52aa6539aa8ee0337e7e80d4f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YWRL2SWCTBWZVZAGRH7NL74ZBR/bundle.json","state_url":"https://pith.science/pith/YWRL2SWCTBWZVZAGRH7NL74ZBR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YWRL2SWCTBWZVZAGRH7NL74ZBR/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":"2026-08-08T02:54:19Z","links":{"resolver":"https://pith.science/pith/YWRL2SWCTBWZVZAGRH7NL74ZBR","bundle":"https://pith.science/pith/YWRL2SWCTBWZVZAGRH7NL74ZBR/bundle.json","state":"https://pith.science/pith/YWRL2SWCTBWZVZAGRH7NL74ZBR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YWRL2SWCTBWZVZAGRH7NL74ZBR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YWRL2SWCTBWZVZAGRH7NL74ZBR","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":"746f8f5571b9436444d48712fd150559b84fc764b9ead98ac79202524374f83a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T08:22:51Z","title_canon_sha256":"e20aa65b2700a564c2f201cc66ba04875c00e2ab8a915de9b9f450b578ab38e3"},"schema_version":"1.0","source":{"id":"2402.11934","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.11934","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"arxiv_version","alias_value":"2402.11934v1","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.11934","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_12","alias_value":"YWRL2SWCTBWZ","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_16","alias_value":"YWRL2SWCTBWZVZAG","created_at":"2026-07-05T07:46:46Z"},{"alias_kind":"pith_short_8","alias_value":"YWRL2SWC","created_at":"2026-07-05T07:46:46Z"}],"graph_snapshots":[{"event_id":"sha256:a5688f9d9abee9a76b9f6ddd2f92712b896878c52aa6539aa8ee0337e7e80d4f","target":"graph","created_at":"2026-07-05T07:46:46Z","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/2402.11934/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper presents the participation of team QUST in Task 8 SemEval 2024. We first performed data augmentation and cleaning on the dataset to enhance model training efficiency and accuracy. In the monolingual task, we evaluated traditional deep-learning methods, multiscale positive-unlabeled framework (MPU), fine-tuning, adapters and ensemble methods. Then, we selected the top-performing models based on their accuracy from the monolingual models and evaluated them in subtasks A and B. The final model construction employed a stacking ensemble that combined fine-tuning with MPU. Our system achi","authors_text":"Jianxiang Tian, Taihang Wang, Xiangrun Li, Xiaoman Xu, Ye Jiang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T08:22:51Z","title":"Team QUST at SemEval-2024 Task 8: A Comprehensive Study of Monolingual and Multilingual Approaches for Detecting AI-generated Text"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.11934","kind":"arxiv","version":1},"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:eb6278a0366c267096679a514b7f57d67cf00b3d52eff1d5dbbbd1d76649a0c3","target":"record","created_at":"2026-07-05T07:46:46Z","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":"746f8f5571b9436444d48712fd150559b84fc764b9ead98ac79202524374f83a","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-02-19T08:22:51Z","title_canon_sha256":"e20aa65b2700a564c2f201cc66ba04875c00e2ab8a915de9b9f450b578ab38e3"},"schema_version":"1.0","source":{"id":"2402.11934","kind":"arxiv","version":1}},"canonical_sha256":"c5a2bd4ac2986d9ae40689fed5ff990c6ed7f8de926f1545e0dac190d6779c31","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c5a2bd4ac2986d9ae40689fed5ff990c6ed7f8de926f1545e0dac190d6779c31","first_computed_at":"2026-07-05T07:46:46.392874Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:46:46.392874Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aFm/0qxlxeZrjf+oSwmTHjsHdofpsjCCIqaloY9bsx/u7R1xTE/rqAafzcgEdAU95VqK9v9wlFUPYaKPy0T0DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:46:46.393249Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.11934","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb6278a0366c267096679a514b7f57d67cf00b3d52eff1d5dbbbd1d76649a0c3","sha256:a5688f9d9abee9a76b9f6ddd2f92712b896878c52aa6539aa8ee0337e7e80d4f"],"state_sha256":"eed67114631e2cc77d7fce548c412a1f8e39078e184dca4b46c20d96b0f578b3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bbZsLIQeYFdQEAFh0gqjt5o5OCSGH5WHYOTDXda5CGJ3eekpeDZ8LhP6nZAQNAkVpk14hk9pnHxSAaXk1sNqBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T02:54:19.215973Z","bundle_sha256":"1c4824cd6080622d0fb6b2da315811baa3c460a9070df94b64ef672c8c788124"}}