{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:JX6E2YY5F5TI5QUOVVCHIZ3RTQ","short_pith_number":"pith:JX6E2YY5","canonical_record":{"source":{"id":"2411.04032","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-06T16:31:28Z","cross_cats_sorted":[],"title_canon_sha256":"a19857ba66747e3b35e21152197eba5064648ef8e50118d0bafdf77260d908ed","abstract_canon_sha256":"8c1d2358a1680c0fad9ed69d5b5124fae8a83b8c4f9b5b0054f47f2e4adc4c3d"},"schema_version":"1.0"},"canonical_sha256":"4dfc4d631d2f668ec28ead447467719c0325eea2653b4e76deb9a146355e0b51","source":{"kind":"arxiv","id":"2411.04032","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.04032","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.04032v3","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04032","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_12","alias_value":"JX6E2YY5F5TI","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"JX6E2YY5F5TI5QUO","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"JX6E2YY5","created_at":"2026-07-05T10:32:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:JX6E2YY5F5TI5QUOVVCHIZ3RTQ","target":"record","payload":{"canonical_record":{"source":{"id":"2411.04032","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-06T16:31:28Z","cross_cats_sorted":[],"title_canon_sha256":"a19857ba66747e3b35e21152197eba5064648ef8e50118d0bafdf77260d908ed","abstract_canon_sha256":"8c1d2358a1680c0fad9ed69d5b5124fae8a83b8c4f9b5b0054f47f2e4adc4c3d"},"schema_version":"1.0"},"canonical_sha256":"4dfc4d631d2f668ec28ead447467719c0325eea2653b4e76deb9a146355e0b51","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:32:33.301458Z","signature_b64":"sZNURH0duqEzj959BMZd7GyOttv0BpQMtje2n3nFw2/GUBPND3dDJ8MHC2GR2aTtlIIh8imdZwX8c/4Wgm5DCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4dfc4d631d2f668ec28ead447467719c0325eea2653b4e76deb9a146355e0b51","last_reissued_at":"2026-07-05T10:32:33.300734Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:32:33.300734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.04032","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:32:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B4rrVqXy0Z5whPxZI8NjzYGCO1MtHzRPvL5cqQbgIg1XDWjzutFaLyhiBUlirmUOmSQSexo6nANMHM2zhIkEAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T12:08:57.212518Z"},"content_sha256":"08140613f729600f4ecc43422effb77fef7e4e9fed81d1437998e193c738bf4a","schema_version":"1.0","event_id":"sha256:08140613f729600f4ecc43422effb77fef7e4e9fed81d1437998e193c738bf4a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:JX6E2YY5F5TI5QUOVVCHIZ3RTQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beemo: Benchmark of Expert-edited Machine-generated Outputs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Adaku Uchendu, Ekaterina Artemova, Jason Lucas, Jooyoung Lee, Saranya Venkatraman, Sergei Tilga, Vladislav Mikhailov","submitted_at":"2024-11-06T16:31:28Z","abstract_excerpt":"The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most existing MGT benchmarks include single-author texts (human-written and machine-generated). This conventional design fails to capture more practical multi-author scenarios, where the user refines the LLM response for natural flow, coherence, and factual correctness. Our paper introduces the Benchmark of Expert-edited Machine-generated Outputs (Beemo), which includes 6.5k texts written by humans, generated by ten instruct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04032","kind":"arxiv","version":3},"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/2411.04032/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":"2026-07-05T10:32:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vJTnjabfLGV4nE79qEOL9DEu7cwJZSBU6XBeyt2OuuqlE9xJpN9Pf/xrJIDtCLi0R8DsMIcwpP1Ztg5pf+dfCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T12:08:57.213429Z"},"content_sha256":"31a67a8c5783dcd4967721a4dc741c2b4ad9a3914fbbe99e97e5e5883e98014b","schema_version":"1.0","event_id":"sha256:31a67a8c5783dcd4967721a4dc741c2b4ad9a3914fbbe99e97e5e5883e98014b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JX6E2YY5F5TI5QUOVVCHIZ3RTQ/bundle.json","state_url":"https://pith.science/pith/JX6E2YY5F5TI5QUOVVCHIZ3RTQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JX6E2YY5F5TI5QUOVVCHIZ3RTQ/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":"2026-08-09T12:08:57Z","links":{"resolver":"https://pith.science/pith/JX6E2YY5F5TI5QUOVVCHIZ3RTQ","bundle":"https://pith.science/pith/JX6E2YY5F5TI5QUOVVCHIZ3RTQ/bundle.json","state":"https://pith.science/pith/JX6E2YY5F5TI5QUOVVCHIZ3RTQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JX6E2YY5F5TI5QUOVVCHIZ3RTQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JX6E2YY5F5TI5QUOVVCHIZ3RTQ","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":"8c1d2358a1680c0fad9ed69d5b5124fae8a83b8c4f9b5b0054f47f2e4adc4c3d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-06T16:31:28Z","title_canon_sha256":"a19857ba66747e3b35e21152197eba5064648ef8e50118d0bafdf77260d908ed"},"schema_version":"1.0","source":{"id":"2411.04032","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.04032","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"arxiv_version","alias_value":"2411.04032v3","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04032","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_12","alias_value":"JX6E2YY5F5TI","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_16","alias_value":"JX6E2YY5F5TI5QUO","created_at":"2026-07-05T10:32:33Z"},{"alias_kind":"pith_short_8","alias_value":"JX6E2YY5","created_at":"2026-07-05T10:32:33Z"}],"graph_snapshots":[{"event_id":"sha256:31a67a8c5783dcd4967721a4dc741c2b4ad9a3914fbbe99e97e5e5883e98014b","target":"graph","created_at":"2026-07-05T10:32:33Z","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/2411.04032/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most existing MGT benchmarks include single-author texts (human-written and machine-generated). This conventional design fails to capture more practical multi-author scenarios, where the user refines the LLM response for natural flow, coherence, and factual correctness. Our paper introduces the Benchmark of Expert-edited Machine-generated Outputs (Beemo), which includes 6.5k texts written by humans, generated by ten instruct","authors_text":"Adaku Uchendu, Ekaterina Artemova, Jason Lucas, Jooyoung Lee, Saranya Venkatraman, Sergei Tilga, Vladislav Mikhailov","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-06T16:31:28Z","title":"Beemo: Benchmark of Expert-edited Machine-generated Outputs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04032","kind":"arxiv","version":3},"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":"sha256:08140613f729600f4ecc43422effb77fef7e4e9fed81d1437998e193c738bf4a","target":"record","created_at":"2026-07-05T10:32:33Z","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":"8c1d2358a1680c0fad9ed69d5b5124fae8a83b8c4f9b5b0054f47f2e4adc4c3d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-06T16:31:28Z","title_canon_sha256":"a19857ba66747e3b35e21152197eba5064648ef8e50118d0bafdf77260d908ed"},"schema_version":"1.0","source":{"id":"2411.04032","kind":"arxiv","version":3}},"canonical_sha256":"4dfc4d631d2f668ec28ead447467719c0325eea2653b4e76deb9a146355e0b51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4dfc4d631d2f668ec28ead447467719c0325eea2653b4e76deb9a146355e0b51","first_computed_at":"2026-07-05T10:32:33.300734Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:32:33.300734Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sZNURH0duqEzj959BMZd7GyOttv0BpQMtje2n3nFw2/GUBPND3dDJ8MHC2GR2aTtlIIh8imdZwX8c/4Wgm5DCg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:32:33.301458Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.04032","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08140613f729600f4ecc43422effb77fef7e4e9fed81d1437998e193c738bf4a","sha256:31a67a8c5783dcd4967721a4dc741c2b4ad9a3914fbbe99e97e5e5883e98014b"],"state_sha256":"f0b4a0b9fa44150872b3bd961eb1f1997477826f97eec23487eb1e55004bce3b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"juFnQM592TfWzADLvrm45Gl5jIKQYB8YcH5ndkwKzXRdSqyd9A+w8hxCv/pga+UYMfFvyuJtpCbO3ABtSOSWCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T12:08:57.222735Z","bundle_sha256":"35ba1da05b466b4ac850c185e553a48e9fa361e72ab09f47c64d9913354cd0c4"}}