{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:YC2DF4WR42EPUUAYW5SKMHLLSS","short_pith_number":"pith:YC2DF4WR","canonical_record":{"source":{"id":"2605.14262","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-14T02:05:49Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"1e48279d5669a23e5640225cb25e0a60e8ad9fb010fbadd28d3208e086e7d668","abstract_canon_sha256":"e853514525568f2fac9131f95d7718417706c7bf5e8feafb8924ca7cf8c2a6a8"},"schema_version":"1.0"},"canonical_sha256":"c0b432f2d1e688fa5018b764a61d6b9490983a2520418fdc9c617314bd4baac1","source":{"kind":"arxiv","id":"2605.14262","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14262","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14262v1","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14262","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"pith_short_12","alias_value":"YC2DF4WR42EP","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"YC2DF4WR42EPUUAY","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"YC2DF4WR","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:YC2DF4WR42EPUUAYW5SKMHLLSS","target":"record","payload":{"canonical_record":{"source":{"id":"2605.14262","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-14T02:05:49Z","cross_cats_sorted":["cs.HC"],"title_canon_sha256":"1e48279d5669a23e5640225cb25e0a60e8ad9fb010fbadd28d3208e086e7d668","abstract_canon_sha256":"e853514525568f2fac9131f95d7718417706c7bf5e8feafb8924ca7cf8c2a6a8"},"schema_version":"1.0"},"canonical_sha256":"c0b432f2d1e688fa5018b764a61d6b9490983a2520418fdc9c617314bd4baac1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:10.474204Z","signature_b64":"OqgNvXby6LTzbs7Mn28rPG8iuC5c7iGKm1gcXFqJpjh5zpGpInNp1rEdaaDgeqeUiGfkmacWy6b2xHzQAsZdCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0b432f2d1e688fa5018b764a61d6b9490983a2520418fdc9c617314bd4baac1","last_reissued_at":"2026-05-17T23:39:10.473718Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:10.473718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.14262","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":"2026-05-17T23:39:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kHaQmEDdf0GjDvrov5XpvIHFXy7855jf2SGGzuuei3xFCL5az2xpjfwW76VpLy0eoLcZQFVazAmEV0phQ1ctCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T17:17:51.076095Z"},"content_sha256":"636d72236848674ae0a6ec67cfd86e62a5a75367f90da72d39b839e68afa9f7d","schema_version":"1.0","event_id":"sha256:636d72236848674ae0a6ec67cfd86e62a5a75367f90da72d39b839e68afa9f7d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:YC2DF4WR42EPUUAYW5SKMHLLSS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distill: Uncovering the True Intent behind Human-Robot Communication","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Distill refines initial robot task specifications by removing steps, generalizing meanings, and relaxing order constraints to better match users' true intent.","cross_cats":["cs.HC"],"primary_cat":"cs.RO","authors_text":"David Porfirio, Ting Li","submitted_at":"2026-05-14T02:05:49Z","abstract_excerpt":"As robots become increasingly integrated into everyday environments, intuitive communication paradigms such as natural language and end-user programming have become indispensable for specifying autonomous robot behavior. However, these mechanisms are ineffective at fully capturing user intent: natural language is imprecise and ambiguous, whereas end-user programming can be overly specific. As a result, understanding what users truly mean when they interact with robots remains a central challenge for human-AI communication systems. To address this issue, we propose the Distill approach for huma"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the three operations of removing steps, generalizing meanings, and relaxing ordering constraints accurately uncover and preserve the user's true underlying intent without introducing distortions or requiring additional user feedback.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Distill refines initial robot task specifications by removing steps, generalizing meanings, and relaxing order constraints to better match users' true intent.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3c7e1f57c4896afed24b232d24bc06a74cd82a3d4a23bfb2cfd5f2e943b9247b"},"source":{"id":"2605.14262","kind":"arxiv","version":1},"verdict":{"id":"a25cc9a5-1ff3-4f5b-afed-08cfcd02f491","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T02:42:54.220080Z","strongest_claim":"We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.","one_line_summary":"Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the three operations of removing steps, generalizing meanings, and relaxing ordering constraints accurately uncover and preserve the user's true underlying intent without introducing distortions or requiring additional user feedback.","pith_extraction_headline":"Distill refines initial robot task specifications by removing steps, generalizing meanings, and relaxing order constraints to better match users' true intent."},"references":{"count":66,"sample":[{"doi":"","year":2026,"title":"[n. d.]. LimeZu. https://limezu.itch.io/. Accessed: 2026-04-25","work_id":"1d9e6538-69c2-45b5-8947-d87ad8c7a9fb","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1145/3466819","year":2021,"title":"Gopika Ajaykumar, Maureen Steele, and Chien-Ming Huang. 2021. A survey on end-user robot programming.ACM Computing Surveys (CSUR)54, 8 (2021), 1–36. doi:10.1145/3466819","work_id":"d85ffb44-f447-40d9-b11a-364497a021d4","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1109/icra.2015.7139973","year":2015,"title":"Sonya Alexandrova, Zachary Tatlock, and Maya Cakmak. 2015. RoboFlow: A flow-based visual programming language for mobile manipulation tasks. In2015 IEEE international conference on robotics and automa","work_id":"dca3e8d2-891d-4166-b78d-967482f901f0","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1145/3290605.3300233","year":2019,"title":"Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz","work_id":"2734701f-773b-4092-8c0a-a34d39c6e99c","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Virginia Braun and Victoria Clarke. 2021. Thematic analysis: A practical guide. (2021)","work_id":"8e7fb43f-18c7-4243-abfb-26ba4021a665","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":66,"snapshot_sha256":"224b5d05de26d16a966047a0fa0b0b6f41e518db712f6afd5e73e9ab038708e3","internal_anchors":1},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c951fc5b2aa3e100b49e2157167b8c27913e507a6bc42678790c507b11966eeb"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"a25cc9a5-1ff3-4f5b-afed-08cfcd02f491"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UozY+UDzCrTB6OF8Hdq+VEQ/GQrJzISmM0rgGl+0epAFG2WAgRxwZ+7m+A4cznfODPBqqZ6pjU2S62nUntwECw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T17:17:51.077263Z"},"content_sha256":"dbb540f884af56233d40018b9a93c1fd4c120406bfa10cf1714a7bc3bd041d2f","schema_version":"1.0","event_id":"sha256:dbb540f884af56233d40018b9a93c1fd4c120406bfa10cf1714a7bc3bd041d2f"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:YC2DF4WR42EPUUAYW5SKMHLLSS","target":"integrity","payload":{"note":"Identifier '10.5555/3523760.3523788' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Andrew Schoen, Nathan White, Curt Henrichs, Amanda Siebert-Evenstone, David Shaffer, and Bilge Mutlu. 2022. CoFrame: A system for training novice cobot programmers. In2022 17th ACM/IEEE International Conference on Human-Robot Interaction (H","arxiv_id":"2605.14262","detector":"doi_compliance","evidence":{"doi":"10.5555/3523760.3523788","arxiv_id":null,"ref_index":53,"raw_excerpt":"Andrew Schoen, Nathan White, Curt Henrichs, Amanda Siebert-Evenstone, David Shaffer, and Bilge Mutlu. 2022. CoFrame: A system for training novice cobot programmers. In2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, 185–194. doi:10.5555/3523760.3523788","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":53,"audited_at":"2026-05-19T05:41:41.921369Z","event_type":"pith.integrity.v1","detected_doi":"10.5555/3523760.3523788","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"bf832b4278fcb5a109a87d7301ab1a213f60651883d67d3569138f47628c0799","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":40,"payload_sha256":"dc5e14238ecbf932d8f73446bef76f47107b47bfd7ffc0947a9e24cecaa72b52","signature_b64":"Afu5yX8Js5U8TY2N6dMdRJFuoab3W2SJTWr+8md/0RSc3Bt8atRYI8o4huV1iioWmBd8o+naJa79SyNWnH7SAQ==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T05:41:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AyAjEMpQq52GN0TVEc59rAvCQvZdrQAib6lZEbL3jy9sgzqMOBUGyDo2RcuBZsDCxIw6Ey+08FlYlf6uQgxHBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T17:17:51.078769Z"},"content_sha256":"f7b7da1277e79b2c973f234ce07e48e67368e9e217e2d7128935b8e64c9ba03e","schema_version":"1.0","event_id":"sha256:f7b7da1277e79b2c973f234ce07e48e67368e9e217e2d7128935b8e64c9ba03e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YC2DF4WR42EPUUAYW5SKMHLLSS/bundle.json","state_url":"https://pith.science/pith/YC2DF4WR42EPUUAYW5SKMHLLSS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YC2DF4WR42EPUUAYW5SKMHLLSS/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":"2026-05-22T17:17:51Z","links":{"resolver":"https://pith.science/pith/YC2DF4WR42EPUUAYW5SKMHLLSS","bundle":"https://pith.science/pith/YC2DF4WR42EPUUAYW5SKMHLLSS/bundle.json","state":"https://pith.science/pith/YC2DF4WR42EPUUAYW5SKMHLLSS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YC2DF4WR42EPUUAYW5SKMHLLSS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YC2DF4WR42EPUUAYW5SKMHLLSS","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e853514525568f2fac9131f95d7718417706c7bf5e8feafb8924ca7cf8c2a6a8","cross_cats_sorted":["cs.HC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-14T02:05:49Z","title_canon_sha256":"1e48279d5669a23e5640225cb25e0a60e8ad9fb010fbadd28d3208e086e7d668"},"schema_version":"1.0","source":{"id":"2605.14262","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14262","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14262v1","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14262","created_at":"2026-05-17T23:39:10Z"},{"alias_kind":"pith_short_12","alias_value":"YC2DF4WR42EP","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"YC2DF4WR42EPUUAY","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"YC2DF4WR","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:dbb540f884af56233d40018b9a93c1fd4c120406bfa10cf1714a7bc3bd041d2f","target":"graph","created_at":"2026-05-17T23:39:10Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the three operations of removing steps, generalizing meanings, and relaxing ordering constraints accurately uncover and preserve the user's true underlying intent without introducing distortions or requiring additional user feedback."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Distill refines initial robot task specifications by removing steps, generalizing meanings, and relaxing order constraints to better match users' true intent."}],"snapshot_sha256":"3c7e1f57c4896afed24b232d24bc06a74cd82a3d4a23bfb2cfd5f2e943b9247b"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"c951fc5b2aa3e100b49e2157167b8c27913e507a6bc42678790c507b11966eeb"},"paper":{"abstract_excerpt":"As robots become increasingly integrated into everyday environments, intuitive communication paradigms such as natural language and end-user programming have become indispensable for specifying autonomous robot behavior. However, these mechanisms are ineffective at fully capturing user intent: natural language is imprecise and ambiguous, whereas end-user programming can be overly specific. As a result, understanding what users truly mean when they interact with robots remains a central challenge for human-AI communication systems. To address this issue, we propose the Distill approach for huma","authors_text":"David Porfirio, Ting Li","cross_cats":["cs.HC"],"headline":"Distill refines initial robot task specifications by removing steps, generalizing meanings, and relaxing order constraints to better match users' true intent.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-14T02:05:49Z","title":"Distill: Uncovering the True Intent behind Human-Robot Communication"},"references":{"count":66,"internal_anchors":1,"resolved_work":66,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"[n. d.]. LimeZu. https://limezu.itch.io/. Accessed: 2026-04-25","work_id":"1d9e6538-69c2-45b5-8947-d87ad8c7a9fb","year":2026},{"cited_arxiv_id":"","doi":"10.1145/3466819","is_internal_anchor":false,"ref_index":2,"title":"Gopika Ajaykumar, Maureen Steele, and Chien-Ming Huang. 2021. A survey on end-user robot programming.ACM Computing Surveys (CSUR)54, 8 (2021), 1–36. doi:10.1145/3466819","work_id":"d85ffb44-f447-40d9-b11a-364497a021d4","year":2021},{"cited_arxiv_id":"","doi":"10.1109/icra.2015.7139973","is_internal_anchor":false,"ref_index":3,"title":"Sonya Alexandrova, Zachary Tatlock, and Maya Cakmak. 2015. RoboFlow: A flow-based visual programming language for mobile manipulation tasks. In2015 IEEE international conference on robotics and automa","work_id":"dca3e8d2-891d-4166-b78d-967482f901f0","year":2015},{"cited_arxiv_id":"","doi":"10.1145/3290605.3300233","is_internal_anchor":false,"ref_index":4,"title":"Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz","work_id":"2734701f-773b-4092-8c0a-a34d39c6e99c","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Virginia Braun and Victoria Clarke. 2021. Thematic analysis: A practical guide. (2021)","work_id":"8e7fb43f-18c7-4243-abfb-26ba4021a665","year":2021}],"snapshot_sha256":"224b5d05de26d16a966047a0fa0b0b6f41e518db712f6afd5e73e9ab038708e3"},"source":{"id":"2605.14262","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T02:42:54.220080Z","id":"a25cc9a5-1ff3-4f5b-afed-08cfcd02f491","model_set":{"reader":"grok-4.3"},"one_line_summary":"Distill refines user task specifications for robots by pruning unnecessary steps, generalizing meanings, and relaxing order constraints, as demonstrated in a crowdsourcing study on a web interface.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Distill refines initial robot task specifications by removing steps, generalizing meanings, and relaxing order constraints to better match users' true intent.","strongest_claim":"We implemented Distill on a web interface and, through a crowdsourcing study, demonstrated its ability to elicit and refine user intent from initial task specifications.","weakest_assumption":"That the three operations of removing steps, generalizing meanings, and relaxing ordering constraints accurately uncover and preserve the user's true underlying intent without introducing distortions or requiring additional user feedback."}},"verdict_id":"a25cc9a5-1ff3-4f5b-afed-08cfcd02f491"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:636d72236848674ae0a6ec67cfd86e62a5a75367f90da72d39b839e68afa9f7d","target":"record","created_at":"2026-05-17T23:39:10Z","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":"e853514525568f2fac9131f95d7718417706c7bf5e8feafb8924ca7cf8c2a6a8","cross_cats_sorted":["cs.HC"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-14T02:05:49Z","title_canon_sha256":"1e48279d5669a23e5640225cb25e0a60e8ad9fb010fbadd28d3208e086e7d668"},"schema_version":"1.0","source":{"id":"2605.14262","kind":"arxiv","version":1}},"canonical_sha256":"c0b432f2d1e688fa5018b764a61d6b9490983a2520418fdc9c617314bd4baac1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c0b432f2d1e688fa5018b764a61d6b9490983a2520418fdc9c617314bd4baac1","first_computed_at":"2026-05-17T23:39:10.473718Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:10.473718Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OqgNvXby6LTzbs7Mn28rPG8iuC5c7iGKm1gcXFqJpjh5zpGpInNp1rEdaaDgeqeUiGfkmacWy6b2xHzQAsZdCg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:10.474204Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14262","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:636d72236848674ae0a6ec67cfd86e62a5a75367f90da72d39b839e68afa9f7d","sha256:dbb540f884af56233d40018b9a93c1fd4c120406bfa10cf1714a7bc3bd041d2f","sha256:f7b7da1277e79b2c973f234ce07e48e67368e9e217e2d7128935b8e64c9ba03e"],"state_sha256":"72ed516ad7b5460713100d123c97eed6f8543f078dd4b172818c159eaab51a01"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gaEG0Za22uNcuHz5qCgwf43lpLRJ16fIKpekb4tRHe7TeBECFLUHvrer8fXfux6szSps3NRrRI3YeQqXafE+Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T17:17:51.081896Z","bundle_sha256":"a24ac9372bf44854e5284190ad4a0c715d78d210e6d44a3e80590213c2904293"}}