{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:A2NPDYYX4HDS4QF5H62BO3DWH3","short_pith_number":"pith:A2NPDYYX","canonical_record":{"source":{"id":"2505.09056","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-14T01:21:46Z","cross_cats_sorted":[],"title_canon_sha256":"fd229ec25b85d4fb8ea51064561edd8d3e165af9267eedd6c86337daf0e35e7d","abstract_canon_sha256":"141c59e0c1e296aebc5b28d2915411195d66b5798d9ed123e8a4200520b09271"},"schema_version":"1.0"},"canonical_sha256":"069af1e317e1c72e40bd3fb4176c763ec9932a4a75f3be5a2f04149975a1e9d8","source":{"kind":"arxiv","id":"2505.09056","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.09056","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"arxiv_version","alias_value":"2505.09056v1","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09056","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"pith_short_12","alias_value":"A2NPDYYX4HDS","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"pith_short_16","alias_value":"A2NPDYYX4HDS4QF5","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"pith_short_8","alias_value":"A2NPDYYX","created_at":"2026-07-05T11:02:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:A2NPDYYX4HDS4QF5H62BO3DWH3","target":"record","payload":{"canonical_record":{"source":{"id":"2505.09056","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-14T01:21:46Z","cross_cats_sorted":[],"title_canon_sha256":"fd229ec25b85d4fb8ea51064561edd8d3e165af9267eedd6c86337daf0e35e7d","abstract_canon_sha256":"141c59e0c1e296aebc5b28d2915411195d66b5798d9ed123e8a4200520b09271"},"schema_version":"1.0"},"canonical_sha256":"069af1e317e1c72e40bd3fb4176c763ec9932a4a75f3be5a2f04149975a1e9d8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:02:49.578137Z","signature_b64":"rmTM13FZF77xWapnvufjyJlBYBFpM7kF3nbWyIzEzBJpeYcj6Dq/Af9BhbZkfQKO1XbEd2bsVii+iKtQCZW+AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"069af1e317e1c72e40bd3fb4176c763ec9932a4a75f3be5a2f04149975a1e9d8","last_reissued_at":"2026-07-05T11:02:49.577554Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:02:49.577554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2505.09056","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":"2026-07-05T11:02:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tUVs9IvFU8ozW/uikHrrLxdWfnDr+AqHHnyXqP3AwrWQzGhc3bGRr0xJKqhwQheQT2YSepQ+0POOJN+QAsPiAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:42:59.109350Z"},"content_sha256":"8e60690288c7c0ed3a88d200a9dabf711cb78e4795c2e8cfa88d8864366399df","schema_version":"1.0","event_id":"sha256:8e60690288c7c0ed3a88d200a9dabf711cb78e4795c2e8cfa88d8864366399df"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:A2NPDYYX4HDS4QF5H62BO3DWH3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Brandon Smith, Mohamed Reda Bouadjenek, Phillip Dawson, Sunil Aryal, Tahsin Alamgir Kheya","submitted_at":"2025-05-14T01:21:46Z","abstract_excerpt":"Large Language Models (LLMs) represent a major step toward artificial general intelligence, significantly advancing our ability to interact with technology. While LLMs perform well on Natural Language Processing tasks -- such as translation, generation, code writing, and summarization -- questions remain about their output similarity, variability, and ethical implications. For instance, how similar are texts generated by the same model? How does this compare across different models? And which models best uphold ethical standards? To investigate, we used 5{,}000 prompts spanning diverse tasks l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09056","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/2505.09056/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":"2026-07-05T11:02:49Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pMmi6FYJu9kVNvQywWF+dcF6Zr+L0rac9t+KjGNjeCNOPUMGHiLC+PGY+w3j9ewcnC2HffWfk+7rrMn/d8slBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:42:59.109715Z"},"content_sha256":"5364076c5413919c20da69359022162f5ab569f9681de1c92d5dc56eb92485d7","schema_version":"1.0","event_id":"sha256:5364076c5413919c20da69359022162f5ab569f9681de1c92d5dc56eb92485d7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A2NPDYYX4HDS4QF5H62BO3DWH3/bundle.json","state_url":"https://pith.science/pith/A2NPDYYX4HDS4QF5H62BO3DWH3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A2NPDYYX4HDS4QF5H62BO3DWH3/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":"2026-07-06T20:42:59Z","links":{"resolver":"https://pith.science/pith/A2NPDYYX4HDS4QF5H62BO3DWH3","bundle":"https://pith.science/pith/A2NPDYYX4HDS4QF5H62BO3DWH3/bundle.json","state":"https://pith.science/pith/A2NPDYYX4HDS4QF5H62BO3DWH3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A2NPDYYX4HDS4QF5H62BO3DWH3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:A2NPDYYX4HDS4QF5H62BO3DWH3","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":"141c59e0c1e296aebc5b28d2915411195d66b5798d9ed123e8a4200520b09271","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-14T01:21:46Z","title_canon_sha256":"fd229ec25b85d4fb8ea51064561edd8d3e165af9267eedd6c86337daf0e35e7d"},"schema_version":"1.0","source":{"id":"2505.09056","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.09056","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"arxiv_version","alias_value":"2505.09056v1","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.09056","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"pith_short_12","alias_value":"A2NPDYYX4HDS","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"pith_short_16","alias_value":"A2NPDYYX4HDS4QF5","created_at":"2026-07-05T11:02:49Z"},{"alias_kind":"pith_short_8","alias_value":"A2NPDYYX","created_at":"2026-07-05T11:02:49Z"}],"graph_snapshots":[{"event_id":"sha256:5364076c5413919c20da69359022162f5ab569f9681de1c92d5dc56eb92485d7","target":"graph","created_at":"2026-07-05T11:02:49Z","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/2505.09056/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Language Models (LLMs) represent a major step toward artificial general intelligence, significantly advancing our ability to interact with technology. While LLMs perform well on Natural Language Processing tasks -- such as translation, generation, code writing, and summarization -- questions remain about their output similarity, variability, and ethical implications. For instance, how similar are texts generated by the same model? How does this compare across different models? And which models best uphold ethical standards? To investigate, we used 5{,}000 prompts spanning diverse tasks l","authors_text":"Brandon Smith, Mohamed Reda Bouadjenek, Phillip Dawson, Sunil Aryal, Tahsin Alamgir Kheya","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-14T01:21:46Z","title":"A Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.09056","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":"sha256:8e60690288c7c0ed3a88d200a9dabf711cb78e4795c2e8cfa88d8864366399df","target":"record","created_at":"2026-07-05T11:02:49Z","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":"141c59e0c1e296aebc5b28d2915411195d66b5798d9ed123e8a4200520b09271","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-14T01:21:46Z","title_canon_sha256":"fd229ec25b85d4fb8ea51064561edd8d3e165af9267eedd6c86337daf0e35e7d"},"schema_version":"1.0","source":{"id":"2505.09056","kind":"arxiv","version":1}},"canonical_sha256":"069af1e317e1c72e40bd3fb4176c763ec9932a4a75f3be5a2f04149975a1e9d8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"069af1e317e1c72e40bd3fb4176c763ec9932a4a75f3be5a2f04149975a1e9d8","first_computed_at":"2026-07-05T11:02:49.577554Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:02:49.577554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rmTM13FZF77xWapnvufjyJlBYBFpM7kF3nbWyIzEzBJpeYcj6Dq/Af9BhbZkfQKO1XbEd2bsVii+iKtQCZW+AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:02:49.578137Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.09056","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8e60690288c7c0ed3a88d200a9dabf711cb78e4795c2e8cfa88d8864366399df","sha256:5364076c5413919c20da69359022162f5ab569f9681de1c92d5dc56eb92485d7"],"state_sha256":"a30af223cc90b9342a1c1482d4a246e6144796fc220a330cff8a5f9cddec7656"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0j3fwwDEAK9xXg7eqQtkBGAI7YQxvw9WHPXDxfXNf7RpJD9UKRpQM7z7y/gMZwKxRgiUU78T4kYq074EbJj9Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:42:59.111554Z","bundle_sha256":"68c9927a6a0b9a4445f0f2967c8d53c15481883e00fb6c9b1fece8983da9994d"}}