{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:G4ATTNW5LSHMNNDQMWG4EOSQFK","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":"2c6db4aa71d650b7b7c3cb76ae018bed1fb5666150a8c1fd6479feb2c8502cfa","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-30T17:56:58Z","title_canon_sha256":"411c5c36ec103f0a9b843aac5d75953fc4368e686a18a5e5aa90cceee23ff54a"},"schema_version":"1.0","source":{"id":"2604.28177","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.28177","created_at":"2026-05-22T01:04:03Z"},{"alias_kind":"arxiv_version","alias_value":"2604.28177v2","created_at":"2026-05-22T01:04:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.28177","created_at":"2026-05-22T01:04:03Z"},{"alias_kind":"pith_short_12","alias_value":"G4ATTNW5LSHM","created_at":"2026-05-22T01:04:03Z"},{"alias_kind":"pith_short_16","alias_value":"G4ATTNW5LSHMNNDQ","created_at":"2026-05-22T01:04:03Z"},{"alias_kind":"pith_short_8","alias_value":"G4ATTNW5","created_at":"2026-05-22T01:04:03Z"}],"graph_snapshots":[{"event_id":"sha256:219b8570640aad48c661df39cb117bd1a165978eb7716a79625921ca6466965a","target":"graph","created_at":"2026-05-22T01:04:03Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"AEGIS serves as a diagnostic testbed exposing fundamental limitations in academic image forensics, with even GPT-5.1 reaching only 48.80% overall performance and expert models limited to 30.09% IoU localization accuracy."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The 39 fine-grained academic subtypes and four simulated forgery strategies using 25 generative models sufficiently represent the real-world distribution and difficulty of AI-generated academic images."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"AEGIS benchmark reveals that leading AI models achieve only 48.80% overall accuracy and low localization precision when analyzing AI-generated academic images, exposing gaps between generative and forensic capabilities."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"AEGIS benchmark reveals that even advanced models detect AI-generated academic images at only 48.80 percent overall accuracy."}],"snapshot_sha256":"c4eeda82f238ff179a642da188b5c32885c02ab08bbe6c527d2516d0b1454d57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-20T20:39:31.023454Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T18:33:07.351969Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2604.28177/integrity.json","findings":[],"snapshot_sha256":"a0c1a52141a77c30ce1e620a9dc4c3e2cbaf6e91ffa8789961570ecf4e9ba5ed","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce AEGIS, A holistic benchmark for Evaluating forensic analysis of AI-Generated academic ImageS. Compared to existing benchmarks, AEGIS features three key advances: (1) Domain-Specific Complexity: covering seven academic categories with 39 fine-grained subtypes, exposing intrinsic forensic difficulty, where even GPT-5.1 reaches 48.80% overall performance and expert models achieve only limited localization accuracy (IoU 30.09%); (2) Diverse Forgery Simulations: modeling four prevalent academic forgery strategies across 25 generative models, with 11 yielding average forensic accuracy b","authors_text":"Bo Zhang, Haihong E, Haiyang Sun, Haocheng Gao, Jiacheng Liu, Junpeng Ding, Liangjia Wang, Peilin Gao, Ronghui Xi, Tzu-Yen Ma, Yiling Huang, Yizhuo Zhao, Yuan Liu, Yuanze Li, Yujie Wang, Yuyue Zhang, Zhongjun Yang, Zichen Tang, Zijie Xi, Zirui Wang, Zixin Ding","cross_cats":["cs.CY"],"headline":"AEGIS benchmark reveals that even advanced models detect AI-generated academic images at only 48.80 percent overall accuracy.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-30T17:56:58Z","title":"AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.28177","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-07T07:30:38.284435Z","id":"163fd9c5-13d6-4b16-b3e1-cc0d068eb3b1","model_set":{"reader":"grok-4.3"},"one_line_summary":"AEGIS benchmark reveals that leading AI models achieve only 48.80% overall accuracy and low localization precision when analyzing AI-generated academic images, exposing gaps between generative and forensic capabilities.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"AEGIS benchmark reveals that even advanced models detect AI-generated academic images at only 48.80 percent overall accuracy.","strongest_claim":"AEGIS serves as a diagnostic testbed exposing fundamental limitations in academic image forensics, with even GPT-5.1 reaching only 48.80% overall performance and expert models limited to 30.09% IoU localization accuracy.","weakest_assumption":"The 39 fine-grained academic subtypes and four simulated forgery strategies using 25 generative models sufficiently represent the real-world distribution and difficulty of AI-generated academic images."}},"verdict_id":"163fd9c5-13d6-4b16-b3e1-cc0d068eb3b1"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d4bfe38ea74835493729c22ff09bc2f86103ea2c82a7150e4479ff8098d8f2e5","target":"record","created_at":"2026-05-22T01:04:03Z","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":"2c6db4aa71d650b7b7c3cb76ae018bed1fb5666150a8c1fd6479feb2c8502cfa","cross_cats_sorted":["cs.CY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-30T17:56:58Z","title_canon_sha256":"411c5c36ec103f0a9b843aac5d75953fc4368e686a18a5e5aa90cceee23ff54a"},"schema_version":"1.0","source":{"id":"2604.28177","kind":"arxiv","version":2}},"canonical_sha256":"370139b6dd5c8ec6b470658dc23a502aa63ce6c219119e0e13ed174f5210fcbb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"370139b6dd5c8ec6b470658dc23a502aa63ce6c219119e0e13ed174f5210fcbb","first_computed_at":"2026-05-22T01:04:03.724447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T01:04:03.724447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+fRiOt81Et2Fb9cNYiZTEB2dkRJSY7OxatKMMU6Rn0arBt3BCoVOU9JFViIJOyOfky3s5niw8Dk58h44xoQcAg==","signature_status":"signed_v1","signed_at":"2026-05-22T01:04:03.725341Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.28177","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4bfe38ea74835493729c22ff09bc2f86103ea2c82a7150e4479ff8098d8f2e5","sha256:219b8570640aad48c661df39cb117bd1a165978eb7716a79625921ca6466965a"],"state_sha256":"78111eaefe7d88e84df7a5466b513240c6abebe4d9ca0f34841db7a7ed5ac01f"}