{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:FNV23KQESALEVFKHJSMNHTMGUY","short_pith_number":"pith:FNV23KQE","canonical_record":{"source":{"id":"2506.07045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-08T08:47:44Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"786483e2fd68ab913f639eb08e4a5c7354b6b079491c4c54b1464807699fa071","abstract_canon_sha256":"a957f845fe085857bcc138508695aaeb44968e3fd4727cf3cf90183d6c5caa4e"},"schema_version":"1.0"},"canonical_sha256":"2b6badaa0490164a95474c98d3cd86a62db3780d9c228d251039b6c3e4f703d6","source":{"kind":"arxiv","id":"2506.07045","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07045","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07045v1","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07045","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"pith_short_12","alias_value":"FNV23KQESALE","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"pith_short_16","alias_value":"FNV23KQESALEVFKH","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"pith_short_8","alias_value":"FNV23KQE","created_at":"2026-07-05T11:18:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:FNV23KQESALEVFKHJSMNHTMGUY","target":"record","payload":{"canonical_record":{"source":{"id":"2506.07045","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-08T08:47:44Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"786483e2fd68ab913f639eb08e4a5c7354b6b079491c4c54b1464807699fa071","abstract_canon_sha256":"a957f845fe085857bcc138508695aaeb44968e3fd4727cf3cf90183d6c5caa4e"},"schema_version":"1.0"},"canonical_sha256":"2b6badaa0490164a95474c98d3cd86a62db3780d9c228d251039b6c3e4f703d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:00.142096Z","signature_b64":"Yw2TDixsyJKUQXzGCqNn+jkkQyHKoAO3poryQgnfOsTzVjj4rabib80nEOzvb/O/guDz13eWEBJw/uj/5QxFDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2b6badaa0490164a95474c98d3cd86a62db3780d9c228d251039b6c3e4f703d6","last_reissued_at":"2026-07-05T11:18:00.141685Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:00.141685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.07045","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":"2026-07-05T11:18:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2oN5BX+WcI2K+WVT8phMFAbsMfdwItTHQZ88LoFJEtMc0XbEgrJj50p+mZ5kZBpZOj2krZFAA0hBopjFa1drCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:44:51.943522Z"},"content_sha256":"260acc0e6af257dd4b96b8c3a6ee44bbe48c9ed7ae3cffa3cf29d1a465313078","schema_version":"1.0","event_id":"sha256:260acc0e6af257dd4b96b8c3a6ee44bbe48c9ed7ae3cffa3cf29d1a465313078"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:FNV23KQESALEVFKHJSMNHTMGUY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CV","authors_text":"Hong Yan, Huijia Zhu, Jianfu Zhang, Jun Lan, Liqing Zhang, Qi Fan, Weiqiang Wang, Yikun Ji","submitted_at":"2025-06-08T08:47:44Z","abstract_excerpt":"The rapid advancement of image generation technologies intensifies the demand for interpretable and robust detection methods. Although existing approaches often attain high accuracy, they typically operate as black boxes without providing human-understandable justifications. Multi-modal Large Language Models (MLLMs), while not originally intended for forgery detection, exhibit strong analytical and reasoning capabilities. When properly fine-tuned, they can effectively identify AI-generated images and offer meaningful explanations. However, existing MLLMs still struggle with hallucination and o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07045","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/2506.07045/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":"2026-07-05T11:18:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+Eh70L7OQEsPk5NfGGnb0JXScnNFWuAL6ibKdSWc5cS5JYAZ61DA+qxaU/2ydrzm3mkW3h8/00OO5wRLNMSbCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:44:51.944027Z"},"content_sha256":"cf769edcec80738f2b49584fe795af052acde86f05760d001860d486dc30731e","schema_version":"1.0","event_id":"sha256:cf769edcec80738f2b49584fe795af052acde86f05760d001860d486dc30731e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FNV23KQESALEVFKHJSMNHTMGUY/bundle.json","state_url":"https://pith.science/pith/FNV23KQESALEVFKHJSMNHTMGUY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FNV23KQESALEVFKHJSMNHTMGUY/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":"2026-08-08T04:44:51Z","links":{"resolver":"https://pith.science/pith/FNV23KQESALEVFKHJSMNHTMGUY","bundle":"https://pith.science/pith/FNV23KQESALEVFKHJSMNHTMGUY/bundle.json","state":"https://pith.science/pith/FNV23KQESALEVFKHJSMNHTMGUY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FNV23KQESALEVFKHJSMNHTMGUY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:FNV23KQESALEVFKHJSMNHTMGUY","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":"a957f845fe085857bcc138508695aaeb44968e3fd4727cf3cf90183d6c5caa4e","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-08T08:47:44Z","title_canon_sha256":"786483e2fd68ab913f639eb08e4a5c7354b6b079491c4c54b1464807699fa071"},"schema_version":"1.0","source":{"id":"2506.07045","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.07045","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"arxiv_version","alias_value":"2506.07045v1","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07045","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"pith_short_12","alias_value":"FNV23KQESALE","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"pith_short_16","alias_value":"FNV23KQESALEVFKH","created_at":"2026-07-05T11:18:00Z"},{"alias_kind":"pith_short_8","alias_value":"FNV23KQE","created_at":"2026-07-05T11:18:00Z"}],"graph_snapshots":[{"event_id":"sha256:cf769edcec80738f2b49584fe795af052acde86f05760d001860d486dc30731e","target":"graph","created_at":"2026-07-05T11:18:00Z","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/2506.07045/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid advancement of image generation technologies intensifies the demand for interpretable and robust detection methods. Although existing approaches often attain high accuracy, they typically operate as black boxes without providing human-understandable justifications. Multi-modal Large Language Models (MLLMs), while not originally intended for forgery detection, exhibit strong analytical and reasoning capabilities. When properly fine-tuned, they can effectively identify AI-generated images and offer meaningful explanations. However, existing MLLMs still struggle with hallucination and o","authors_text":"Hong Yan, Huijia Zhu, Jianfu Zhang, Jun Lan, Liqing Zhang, Qi Fan, Weiqiang Wang, Yikun Ji","cross_cats":["cs.AI","cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-08T08:47:44Z","title":"Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07045","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":"sha256:260acc0e6af257dd4b96b8c3a6ee44bbe48c9ed7ae3cffa3cf29d1a465313078","target":"record","created_at":"2026-07-05T11:18:00Z","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":"a957f845fe085857bcc138508695aaeb44968e3fd4727cf3cf90183d6c5caa4e","cross_cats_sorted":["cs.AI","cs.CL"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-08T08:47:44Z","title_canon_sha256":"786483e2fd68ab913f639eb08e4a5c7354b6b079491c4c54b1464807699fa071"},"schema_version":"1.0","source":{"id":"2506.07045","kind":"arxiv","version":1}},"canonical_sha256":"2b6badaa0490164a95474c98d3cd86a62db3780d9c228d251039b6c3e4f703d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2b6badaa0490164a95474c98d3cd86a62db3780d9c228d251039b6c3e4f703d6","first_computed_at":"2026-07-05T11:18:00.141685Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:00.141685Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yw2TDixsyJKUQXzGCqNn+jkkQyHKoAO3poryQgnfOsTzVjj4rabib80nEOzvb/O/guDz13eWEBJw/uj/5QxFDg==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:00.142096Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.07045","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:260acc0e6af257dd4b96b8c3a6ee44bbe48c9ed7ae3cffa3cf29d1a465313078","sha256:cf769edcec80738f2b49584fe795af052acde86f05760d001860d486dc30731e"],"state_sha256":"438fb7398fcd1a3221a5568a661df2c5336e21bf3b09cdd9c657f959c6aa9970"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nzCNUrtKRwXIKL2mXBTuj9YnkdDsy9sh184SA1dmZ7n1SiJMnI4PiJzky2C15L2CRjNTGDf2ekeqalSaZhHADw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T04:44:51.947769Z","bundle_sha256":"312f1d0bbc25a7ef80ca733d0f70343d6510764f5c696c01a6baee3ff116c6e7"}}