{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VEM4BXLWI7W4JYUMPGQDRXQRBY","short_pith_number":"pith:VEM4BXLW","schema_version":"1.0","canonical_sha256":"a919c0dd7647edc4e28c79a038de110e02b762df2b93224561809d90b2210466","source":{"kind":"arxiv","id":"2404.13236","version":2},"attestation_state":"computed","paper":{"title":"LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"cs.DC","authors_text":"Micka\\\"el Coustaty, Mouhamed Amine Bouchiha, Mourad Rabah, Quentin Telnoff, Ronan Champagnat, Souhail Bakkali, Yacine Ghamri-Doudane","submitted_at":"2024-04-20T02:18:00Z","abstract_excerpt":"Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applications, such as legal assistance and medical diagnosis, where precision, reliability, and ethical con"},"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":"2404.13236","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.DC","submitted_at":"2024-04-20T02:18:00Z","cross_cats_sorted":["cs.ET"],"title_canon_sha256":"32ddec0b9e87095d22132d093d991678e89b8193df5ee75930890894b49f0a7c","abstract_canon_sha256":"efdc46210832080e965e5610e308f92e8e9bbf75a253176aa2bdc6007ba124af"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:56.000982Z","signature_b64":"VN/6raMDaV+qspVneQsc2qeGovORUv+dKAutfIjHOjiGgwqgaUWesCB1y5CjoC7eym5WrDYu4tb/4QgPz6nnBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a919c0dd7647edc4e28c79a038de110e02b762df2b93224561809d90b2210466","last_reissued_at":"2026-07-05T08:14:56.000518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:56.000518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLMChain: Blockchain-based Reputation System for Sharing and Evaluating Large Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"cs.DC","authors_text":"Micka\\\"el Coustaty, Mouhamed Amine Bouchiha, Mourad Rabah, Quentin Telnoff, Ronan Champagnat, Souhail Bakkali, Yacine Ghamri-Doudane","submitted_at":"2024-04-20T02:18:00Z","abstract_excerpt":"Large Language Models (LLMs) have witnessed rapid growth in emerging challenges and capabilities of language understanding, generation, and reasoning. Despite their remarkable performance in natural language processing-based applications, LLMs are susceptible to undesirable and erratic behaviors, including hallucinations, unreliable reasoning, and the generation of harmful content. These flawed behaviors undermine trust in LLMs and pose significant hurdles to their adoption in real-world applications, such as legal assistance and medical diagnosis, where precision, reliability, and ethical con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.13236","kind":"arxiv","version":2},"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/2404.13236/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":"2404.13236","created_at":"2026-07-05T08:14:56.000574+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.13236v2","created_at":"2026-07-05T08:14:56.000574+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.13236","created_at":"2026-07-05T08:14:56.000574+00:00"},{"alias_kind":"pith_short_12","alias_value":"VEM4BXLWI7W4","created_at":"2026-07-05T08:14:56.000574+00:00"},{"alias_kind":"pith_short_16","alias_value":"VEM4BXLWI7W4JYUM","created_at":"2026-07-05T08:14:56.000574+00:00"},{"alias_kind":"pith_short_8","alias_value":"VEM4BXLW","created_at":"2026-07-05T08:14:56.000574+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04359","citing_title":"Learning to cooperate with emergent reputation via multi-agent reinforcement learning","ref_index":62,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY","json":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY.json","graph_json":"https://pith.science/api/pith-number/VEM4BXLWI7W4JYUMPGQDRXQRBY/graph.json","events_json":"https://pith.science/api/pith-number/VEM4BXLWI7W4JYUMPGQDRXQRBY/events.json","paper":"https://pith.science/paper/VEM4BXLW"},"agent_actions":{"view_html":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY","download_json":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY.json","view_paper":"https://pith.science/paper/VEM4BXLW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.13236&json=true","fetch_graph":"https://pith.science/api/pith-number/VEM4BXLWI7W4JYUMPGQDRXQRBY/graph.json","fetch_events":"https://pith.science/api/pith-number/VEM4BXLWI7W4JYUMPGQDRXQRBY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY/action/storage_attestation","attest_author":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY/action/author_attestation","sign_citation":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY/action/citation_signature","submit_replication":"https://pith.science/pith/VEM4BXLWI7W4JYUMPGQDRXQRBY/action/replication_record"}},"created_at":"2026-07-05T08:14:56.000574+00:00","updated_at":"2026-07-05T08:14:56.000574+00:00"}