{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UBO46EKPQPIRUOK3Q45LDWQV4A","short_pith_number":"pith:UBO46EKP","schema_version":"1.0","canonical_sha256":"a05dcf114f83d11a395b873ab1da15e028e922130317fb2c3be756291ad6fe6c","source":{"kind":"arxiv","id":"2410.14273","version":1},"attestation_state":"computed","paper":{"title":"REEF: Representation Encoding Fingerprints for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.CL","authors_text":"Chen Qian, Dongrui Liu, Jie Zhang, Jing Shao, Linfeng Zhang, Yong Liu, Yu Qiao","submitted_at":"2024-10-18T08:27:02Z","abstract_excerpt":"Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefore, model owners and third parties need to identify whether a suspect model is a subsequent development of the victim model. To this end, we propose a training-free REEF to identify the relationship between the suspect and victim models from the perspective of LLMs' feature representations. Specifically, REEF computes and compares the centered kernel alignment similarity between the representations of a suspect model "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.14273","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-10-18T08:27:02Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"2fe3b9ee1951ee47a6220aec231799204897b4441863e90dd2a06192648c8acf","abstract_canon_sha256":"3b7e949408d8e831224bd5ddc467b449c25d58702d5d774d16db869a7ab75578"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:22:30.053963Z","signature_b64":"9UeKKFpG6Ou9k8z3d3SyFrhKHlcm3tTkX7mzvvnRW0BI6fMOI7oyLWr7ncEFDa+Piu8LeHqvXbP9EZkMRjXsBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a05dcf114f83d11a395b873ab1da15e028e922130317fb2c3be756291ad6fe6c","last_reissued_at":"2026-07-05T09:22:30.053489Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:22:30.053489Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"REEF: Representation Encoding Fingerprints for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.CL","authors_text":"Chen Qian, Dongrui Liu, Jie Zhang, Jing Shao, Linfeng Zhang, Yong Liu, Yu Qiao","submitted_at":"2024-10-18T08:27:02Z","abstract_excerpt":"Protecting the intellectual property of open-source Large Language Models (LLMs) is very important, because training LLMs costs extensive computational resources and data. Therefore, model owners and third parties need to identify whether a suspect model is a subsequent development of the victim model. To this end, we propose a training-free REEF to identify the relationship between the suspect and victim models from the perspective of LLMs' feature representations. Specifically, REEF computes and compares the centered kernel alignment similarity between the representations of a suspect model "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.14273","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/2410.14273/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.14273","created_at":"2026-07-05T09:22:30.053541+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.14273v1","created_at":"2026-07-05T09:22:30.053541+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.14273","created_at":"2026-07-05T09:22:30.053541+00:00"},{"alias_kind":"pith_short_12","alias_value":"UBO46EKPQPIR","created_at":"2026-07-05T09:22:30.053541+00:00"},{"alias_kind":"pith_short_16","alias_value":"UBO46EKPQPIRUOK3","created_at":"2026-07-05T09:22:30.053541+00:00"},{"alias_kind":"pith_short_8","alias_value":"UBO46EKP","created_at":"2026-07-05T09:22:30.053541+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01313","citing_title":"Black-Box Inference of LLM Architectural Properties with Restrictive API Access","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06342","citing_title":"Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2606.03330","citing_title":"FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2605.29245","citing_title":"Implicit Identity Technologies for LLMs: Fingerprinting and Watermarking across Datasets, Models, and Generated Content","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2508.11548","citing_title":"Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends","ref_index":177,"is_internal_anchor":false},{"citing_arxiv_id":"2509.26404","citing_title":"SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16363","citing_title":"CSF: Black-box Fingerprinting via Compositional Semantics for Text-to-Image Models","ref_index":59,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17041","citing_title":"SIF: Semantically In-Distribution Fingerprints for Large Vision-Language Models","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A","json":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A.json","graph_json":"https://pith.science/api/pith-number/UBO46EKPQPIRUOK3Q45LDWQV4A/graph.json","events_json":"https://pith.science/api/pith-number/UBO46EKPQPIRUOK3Q45LDWQV4A/events.json","paper":"https://pith.science/paper/UBO46EKP"},"agent_actions":{"view_html":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A","download_json":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A.json","view_paper":"https://pith.science/paper/UBO46EKP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.14273&json=true","fetch_graph":"https://pith.science/api/pith-number/UBO46EKPQPIRUOK3Q45LDWQV4A/graph.json","fetch_events":"https://pith.science/api/pith-number/UBO46EKPQPIRUOK3Q45LDWQV4A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A/action/storage_attestation","attest_author":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A/action/author_attestation","sign_citation":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A/action/citation_signature","submit_replication":"https://pith.science/pith/UBO46EKPQPIRUOK3Q45LDWQV4A/action/replication_record"}},"created_at":"2026-07-05T09:22:30.053541+00:00","updated_at":"2026-07-05T09:22:30.053541+00:00"}