{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ECJD2SFHTF6HITKX7TRXT6PLVF","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":"1fc92f4d131bb9b5ce9c1f84ed2a5036d066c5ce1187cb8c998136d2cc45d6ec","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-24T07:07:37Z","title_canon_sha256":"d4c1e25c2a01dedaf1631f6e289f4c39f8ad18a9f2dabd3f0c23149c46f7ff15"},"schema_version":"1.0","source":{"id":"2501.14288","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.14288","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"arxiv_version","alias_value":"2501.14288v2","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.14288","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_12","alias_value":"ECJD2SFHTF6H","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_16","alias_value":"ECJD2SFHTF6HITKX","created_at":"2026-07-05T10:07:47Z"},{"alias_kind":"pith_short_8","alias_value":"ECJD2SFH","created_at":"2026-07-05T10:07:47Z"}],"graph_snapshots":[{"event_id":"sha256:fe86c1527cbcbd7dc85836038d0c4d60027fe1412338e0e6a1e59cc68bf1b530","target":"graph","created_at":"2026-07-05T10:07:47Z","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/2501.14288/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid advancement of large language models (LLMs) has made detecting AI-generated text an increasingly critical challenge. Traditional methods often fail to capture the nuanced semantic differences between human and machine-generated content. We therefore propose a novel approach based on semantic similarity analysis, leveraging a multi-layered architecture that combines a pre-trained DeBERTa-v3-large model, Bi-directional LSTMs, and linear attention pooling to capture both local and global semantic patterns. To enhance performance, we employ advanced input and output augmentation techniqu","authors_text":"Lifu Gao, Qi Zhang, Ziwei Liu","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-24T07:07:37Z","title":"A Comprehensive Framework for Semantic Similarity Analysis of Human and AI-Generated Text Using Transformer Architectures and Ensemble Techniques"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.14288","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":"sha256:80d47e0803bdbeb747541a3e331a8505839fb6bf9ae9217dc9b2a325ca1b77ab","target":"record","created_at":"2026-07-05T10:07:47Z","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":"1fc92f4d131bb9b5ce9c1f84ed2a5036d066c5ce1187cb8c998136d2cc45d6ec","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-24T07:07:37Z","title_canon_sha256":"d4c1e25c2a01dedaf1631f6e289f4c39f8ad18a9f2dabd3f0c23149c46f7ff15"},"schema_version":"1.0","source":{"id":"2501.14288","kind":"arxiv","version":2}},"canonical_sha256":"20923d48a7997c744d57fce379f9eba943171bceaba3bc60ff5290bd93808f9e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"20923d48a7997c744d57fce379f9eba943171bceaba3bc60ff5290bd93808f9e","first_computed_at":"2026-07-05T10:07:47.899908Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:07:47.899908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GJgHnR1GmYjxd9MKiP8x9+zj7DMgfndOvdq2jDltfr4UWMXA97+8OJqlgnU3cxogK1pvtNnPTWQd8fw+zGD/Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:07:47.900385Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.14288","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:80d47e0803bdbeb747541a3e331a8505839fb6bf9ae9217dc9b2a325ca1b77ab","sha256:fe86c1527cbcbd7dc85836038d0c4d60027fe1412338e0e6a1e59cc68bf1b530"],"state_sha256":"becbde5803f9d0ead6c17e524bc7b4278b9371fa57f77fefe26ea99a5fc72654"}