{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MQUHR6GBR3RQUSWLRX3LFKH5B2","short_pith_number":"pith:MQUHR6GB","canonical_record":{"source":{"id":"2310.00117","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-09-29T20:11:15Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ab2be3cad31c6aaadaf3d0287de6d980ffd7f57ee6143f0472791bc4aec10825","abstract_canon_sha256":"a9e89c3ca511a8cb862ce81d1e90b716f6422fcafb0a7f7b716bc2cbb95ae0da"},"schema_version":"1.0"},"canonical_sha256":"642878f8c18ee30a4acb8df6b2a8fd0eb78dd5a6177a3664b68bd021be4d251c","source":{"kind":"arxiv","id":"2310.00117","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.00117","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"arxiv_version","alias_value":"2310.00117v4","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00117","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"pith_short_12","alias_value":"MQUHR6GBR3RQ","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"pith_short_16","alias_value":"MQUHR6GBR3RQUSWL","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"pith_short_8","alias_value":"MQUHR6GB","created_at":"2026-07-05T08:01:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MQUHR6GBR3RQUSWLRX3LFKH5B2","target":"record","payload":{"canonical_record":{"source":{"id":"2310.00117","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-09-29T20:11:15Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"ab2be3cad31c6aaadaf3d0287de6d980ffd7f57ee6143f0472791bc4aec10825","abstract_canon_sha256":"a9e89c3ca511a8cb862ce81d1e90b716f6422fcafb0a7f7b716bc2cbb95ae0da"},"schema_version":"1.0"},"canonical_sha256":"642878f8c18ee30a4acb8df6b2a8fd0eb78dd5a6177a3664b68bd021be4d251c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:01:15.974228Z","signature_b64":"VB7OdqfchIVOL+5eWTj78fgjJ89g9y7d2YFaqVpigKAPn3e8QNDvwmCKkaekF/GDmXpAMgFt5pt5/0EchfJcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"642878f8c18ee30a4acb8df6b2a8fd0eb78dd5a6177a3664b68bd021be4d251c","last_reissued_at":"2026-07-05T08:01:15.973748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:01:15.973748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.00117","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:01:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RsOM+QpeDoq17NApbXA2jO8thVIplQJv0Xsjpj8818k4a4I5bKUuXZuWS4TuqE9+teXaX1Eue2fWH7EMTUA0DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T13:59:27.260221Z"},"content_sha256":"29c81f39f051e45c9a019b0256448413bacf07897a487aec64b27e649dac8a0e","schema_version":"1.0","event_id":"sha256:29c81f39f051e45c9a019b0256448413bacf07897a487aec64b27e649dac8a0e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MQUHR6GBR3RQUSWLRX3LFKH5B2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.HC","authors_text":"Anastasia Kuzminykh, Ilya Musabirov, Joseph Jay Williams, Kashish Mittal, Michael Liut, Mohi Reza, Nathan Laundry, Peter Dushniku, Tovi Grossman, Zhi Yuan \"Michael\" Yu","submitted_at":"2023-09-29T20:11:15Z","abstract_excerpt":"Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers' flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00117","kind":"arxiv","version":4},"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/2310.00117/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":"2026-07-05T08:01:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"toh0hOjbKObfTK4T+RGyx8pf4wGSRy8hE+ACroyc6dkecWo9EqBNgR//3dH878BqWRI42YtaSYrADaPe+3D7Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T13:59:27.261181Z"},"content_sha256":"70a3c4e2f055bd381eef1aa56691604046e7be66ed7eaf219cc2d19f0be683b7","schema_version":"1.0","event_id":"sha256:70a3c4e2f055bd381eef1aa56691604046e7be66ed7eaf219cc2d19f0be683b7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MQUHR6GBR3RQUSWLRX3LFKH5B2/bundle.json","state_url":"https://pith.science/pith/MQUHR6GBR3RQUSWLRX3LFKH5B2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MQUHR6GBR3RQUSWLRX3LFKH5B2/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":"2026-08-16T13:59:27Z","links":{"resolver":"https://pith.science/pith/MQUHR6GBR3RQUSWLRX3LFKH5B2","bundle":"https://pith.science/pith/MQUHR6GBR3RQUSWLRX3LFKH5B2/bundle.json","state":"https://pith.science/pith/MQUHR6GBR3RQUSWLRX3LFKH5B2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MQUHR6GBR3RQUSWLRX3LFKH5B2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MQUHR6GBR3RQUSWLRX3LFKH5B2","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":"a9e89c3ca511a8cb862ce81d1e90b716f6422fcafb0a7f7b716bc2cbb95ae0da","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-09-29T20:11:15Z","title_canon_sha256":"ab2be3cad31c6aaadaf3d0287de6d980ffd7f57ee6143f0472791bc4aec10825"},"schema_version":"1.0","source":{"id":"2310.00117","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.00117","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"arxiv_version","alias_value":"2310.00117v4","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00117","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"pith_short_12","alias_value":"MQUHR6GBR3RQ","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"pith_short_16","alias_value":"MQUHR6GBR3RQUSWL","created_at":"2026-07-05T08:01:15Z"},{"alias_kind":"pith_short_8","alias_value":"MQUHR6GB","created_at":"2026-07-05T08:01:15Z"}],"graph_snapshots":[{"event_id":"sha256:70a3c4e2f055bd381eef1aa56691604046e7be66ed7eaf219cc2d19f0be683b7","target":"graph","created_at":"2026-07-05T08:01:15Z","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/2310.00117/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However, current interfaces pose challenges for simultaneous consideration of multiple variations: creating new variations without overwriting text can be difficult, and pasting them sequentially can clutter documents, increasing workload and disrupting writers' flow. To tackle this, we present ABScribe, an interface that supports rapid, yet visually structured, exploration and organization of writing variations in human-AI c","authors_text":"Anastasia Kuzminykh, Ilya Musabirov, Joseph Jay Williams, Kashish Mittal, Michael Liut, Mohi Reza, Nathan Laundry, Peter Dushniku, Tovi Grossman, Zhi Yuan \"Michael\" Yu","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-09-29T20:11:15Z","title":"ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00117","kind":"arxiv","version":4},"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":"sha256:29c81f39f051e45c9a019b0256448413bacf07897a487aec64b27e649dac8a0e","target":"record","created_at":"2026-07-05T08:01:15Z","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":"a9e89c3ca511a8cb862ce81d1e90b716f6422fcafb0a7f7b716bc2cbb95ae0da","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2023-09-29T20:11:15Z","title_canon_sha256":"ab2be3cad31c6aaadaf3d0287de6d980ffd7f57ee6143f0472791bc4aec10825"},"schema_version":"1.0","source":{"id":"2310.00117","kind":"arxiv","version":4}},"canonical_sha256":"642878f8c18ee30a4acb8df6b2a8fd0eb78dd5a6177a3664b68bd021be4d251c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"642878f8c18ee30a4acb8df6b2a8fd0eb78dd5a6177a3664b68bd021be4d251c","first_computed_at":"2026-07-05T08:01:15.973748Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:01:15.973748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VB7OdqfchIVOL+5eWTj78fgjJ89g9y7d2YFaqVpigKAPn3e8QNDvwmCKkaekF/GDmXpAMgFt5pt5/0EchfJcBA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:01:15.974228Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.00117","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:29c81f39f051e45c9a019b0256448413bacf07897a487aec64b27e649dac8a0e","sha256:70a3c4e2f055bd381eef1aa56691604046e7be66ed7eaf219cc2d19f0be683b7"],"state_sha256":"ad923bde778d3fb6b5238f677e2bfa9e71513c8c29f72726d4ae5294efc65bd7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Mm6rtaBwyumqlqEeZqAP09hHJI417Ow27ZQ362wzXJW37DvONMYLuGIoxKXOlKcqQMQ/TWb0acGNNJ/WN3tQAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T13:59:27.266609Z","bundle_sha256":"49dc9c53aa2126ff1d91d65d9e1e3824b359382efff16fe5ff913932badedac6"}}