{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PHBNCYKKOWMK2W7G4GI3M7P65S","short_pith_number":"pith:PHBNCYKK","canonical_record":{"source":{"id":"2605.19591","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T09:35:00Z","cross_cats_sorted":[],"title_canon_sha256":"388583e04d679c7893deefff1fb1b9f1a2e89f4a5787db1758a5f36f456aae31","abstract_canon_sha256":"1d6131211fbfc6e60f97c30bc7a7dc51f34e4ad5094f3fbda8e92f6d66630230"},"schema_version":"1.0"},"canonical_sha256":"79c2d1614a7598ad5be6e191b67dfeec99604bc51587b560af8ef739ad59e9d6","source":{"kind":"arxiv","id":"2605.19591","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19591","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19591v1","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19591","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"pith_short_12","alias_value":"PHBNCYKKOWMK","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"pith_short_16","alias_value":"PHBNCYKKOWMK2W7G","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"pith_short_8","alias_value":"PHBNCYKK","created_at":"2026-05-20T01:05:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PHBNCYKKOWMK2W7G4GI3M7P65S","target":"record","payload":{"canonical_record":{"source":{"id":"2605.19591","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T09:35:00Z","cross_cats_sorted":[],"title_canon_sha256":"388583e04d679c7893deefff1fb1b9f1a2e89f4a5787db1758a5f36f456aae31","abstract_canon_sha256":"1d6131211fbfc6e60f97c30bc7a7dc51f34e4ad5094f3fbda8e92f6d66630230"},"schema_version":"1.0"},"canonical_sha256":"79c2d1614a7598ad5be6e191b67dfeec99604bc51587b560af8ef739ad59e9d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:05:53.269165Z","signature_b64":"ro0Dnn5yQcO7oYYXmqLP73l5/VjZnJJHw76wPmRt+cxIAgdoQz2Ur/D8gJhNxXAyOKcdn7R6mqx8fIBq9AabDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"79c2d1614a7598ad5be6e191b67dfeec99604bc51587b560af8ef739ad59e9d6","last_reissued_at":"2026-05-20T01:05:53.268128Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:05:53.268128Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.19591","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":"2026-05-20T01:05:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3gopGdWL1kHbVlrwIR571Qo09j4FA2Qn5RfQ1UTo26MYS1DTjAIqv4bSrLx7kvSwzargpzVTESMo0yYhpQ65Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:20:19.574436Z"},"content_sha256":"695a132abc5e747340b5cdeeffd0888c46beeca00ebb6b243a07ef6fe6b5a544","schema_version":"1.0","event_id":"sha256:695a132abc5e747340b5cdeeffd0888c46beeca00ebb6b243a07ef6fe6b5a544"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PHBNCYKKOWMK2W7G4GI3M7P65S","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Uncertainty-Aware Ideal Point Estimation via Variational EM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Johan Lim, Jong Hee Park, Kwangok Seo, Xinlei Wang, Youngjo Lee","submitted_at":"2026-05-19T09:35:00Z","abstract_excerpt":"Roll-call data analysis aims to estimate legislators' ideal points and quantify the associated uncertainty. Existing approaches either rely on Bayesian methods implemented via Markov chain Monte Carlo sampling or focus primarily on point estimation, with uncertainty typically assessed through resampling procedures such as the bootstrap. Consequently, the computational burden of these approaches can become substantial when applied to large roll-call datasets. To address this challenge, we propose a computationally efficient likelihood method for estimating ideal points and their standard errors"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19591","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/2605.19591/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":"2026-05-20T01:05:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IdYksr7Tm9+DLuoE1H7BHXRrvqHjnyf1YgMhgwnrcMyFEuaJTJAhKkxaBSgr7vDaJjrrskU6aKl1Y6MsCoSgBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T07:20:19.574822Z"},"content_sha256":"b6eab14d9a88c41e8224e6cdfdb2ac327e45e57532f60fd9e4889c17ef121df5","schema_version":"1.0","event_id":"sha256:b6eab14d9a88c41e8224e6cdfdb2ac327e45e57532f60fd9e4889c17ef121df5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PHBNCYKKOWMK2W7G4GI3M7P65S/bundle.json","state_url":"https://pith.science/pith/PHBNCYKKOWMK2W7G4GI3M7P65S/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PHBNCYKKOWMK2W7G4GI3M7P65S/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":"2026-05-30T07:20:19Z","links":{"resolver":"https://pith.science/pith/PHBNCYKKOWMK2W7G4GI3M7P65S","bundle":"https://pith.science/pith/PHBNCYKKOWMK2W7G4GI3M7P65S/bundle.json","state":"https://pith.science/pith/PHBNCYKKOWMK2W7G4GI3M7P65S/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PHBNCYKKOWMK2W7G4GI3M7P65S/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PHBNCYKKOWMK2W7G4GI3M7P65S","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":"1d6131211fbfc6e60f97c30bc7a7dc51f34e4ad5094f3fbda8e92f6d66630230","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T09:35:00Z","title_canon_sha256":"388583e04d679c7893deefff1fb1b9f1a2e89f4a5787db1758a5f36f456aae31"},"schema_version":"1.0","source":{"id":"2605.19591","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.19591","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"arxiv_version","alias_value":"2605.19591v1","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19591","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"pith_short_12","alias_value":"PHBNCYKKOWMK","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"pith_short_16","alias_value":"PHBNCYKKOWMK2W7G","created_at":"2026-05-20T01:05:53Z"},{"alias_kind":"pith_short_8","alias_value":"PHBNCYKK","created_at":"2026-05-20T01:05:53Z"}],"graph_snapshots":[{"event_id":"sha256:b6eab14d9a88c41e8224e6cdfdb2ac327e45e57532f60fd9e4889c17ef121df5","target":"graph","created_at":"2026-05-20T01:05:53Z","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/2605.19591/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Roll-call data analysis aims to estimate legislators' ideal points and quantify the associated uncertainty. Existing approaches either rely on Bayesian methods implemented via Markov chain Monte Carlo sampling or focus primarily on point estimation, with uncertainty typically assessed through resampling procedures such as the bootstrap. Consequently, the computational burden of these approaches can become substantial when applied to large roll-call datasets. To address this challenge, we propose a computationally efficient likelihood method for estimating ideal points and their standard errors","authors_text":"Johan Lim, Jong Hee Park, Kwangok Seo, Xinlei Wang, Youngjo Lee","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T09:35:00Z","title":"Uncertainty-Aware Ideal Point Estimation via Variational EM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19591","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":"sha256:695a132abc5e747340b5cdeeffd0888c46beeca00ebb6b243a07ef6fe6b5a544","target":"record","created_at":"2026-05-20T01:05:53Z","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":"1d6131211fbfc6e60f97c30bc7a7dc51f34e4ad5094f3fbda8e92f6d66630230","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-19T09:35:00Z","title_canon_sha256":"388583e04d679c7893deefff1fb1b9f1a2e89f4a5787db1758a5f36f456aae31"},"schema_version":"1.0","source":{"id":"2605.19591","kind":"arxiv","version":1}},"canonical_sha256":"79c2d1614a7598ad5be6e191b67dfeec99604bc51587b560af8ef739ad59e9d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79c2d1614a7598ad5be6e191b67dfeec99604bc51587b560af8ef739ad59e9d6","first_computed_at":"2026-05-20T01:05:53.268128Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T01:05:53.268128Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ro0Dnn5yQcO7oYYXmqLP73l5/VjZnJJHw76wPmRt+cxIAgdoQz2Ur/D8gJhNxXAyOKcdn7R6mqx8fIBq9AabDw==","signature_status":"signed_v1","signed_at":"2026-05-20T01:05:53.269165Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.19591","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:695a132abc5e747340b5cdeeffd0888c46beeca00ebb6b243a07ef6fe6b5a544","sha256:b6eab14d9a88c41e8224e6cdfdb2ac327e45e57532f60fd9e4889c17ef121df5"],"state_sha256":"908f7ba0d80592d5d3f59f5a526b70e55c1e055c0b36046e4cc084104dc24a0e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GMjtZSSKZcOVayk+/nq2xtWUTHfPUXcz0HoSr8zGR8ylYkJ4FFN1fcLskxNSWbF3Lywz6+1EXwdhN2cjHG15Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T07:20:19.576966Z","bundle_sha256":"a8f099400f750eb265e296133e263ab912c089ac5a4942e4dd28020d3acbaa6b"}}