{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:HSIG2JHW6QMDSCT657RFMQJ7XU","short_pith_number":"pith:HSIG2JHW","canonical_record":{"source":{"id":"2108.11579","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-26T05:00:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3622252bd250e8da134aa7df1115756d5f17d6273ecc1c1d2418c0e7a0340a1c","abstract_canon_sha256":"dc2a783752b44e25b0a7a83078d37f89ce11d54723fb258de905483b9573732d"},"schema_version":"1.0"},"canonical_sha256":"3c906d24f6f418390a7eefe256413fbd2ade8a66f1d89485914a17723711b0a4","source":{"kind":"arxiv","id":"2108.11579","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.11579","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"arxiv_version","alias_value":"2108.11579v2","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.11579","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"pith_short_12","alias_value":"HSIG2JHW6QMD","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"pith_short_16","alias_value":"HSIG2JHW6QMDSCT6","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"pith_short_8","alias_value":"HSIG2JHW","created_at":"2026-07-05T04:44:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:HSIG2JHW6QMDSCT657RFMQJ7XU","target":"record","payload":{"canonical_record":{"source":{"id":"2108.11579","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-26T05:00:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3622252bd250e8da134aa7df1115756d5f17d6273ecc1c1d2418c0e7a0340a1c","abstract_canon_sha256":"dc2a783752b44e25b0a7a83078d37f89ce11d54723fb258de905483b9573732d"},"schema_version":"1.0"},"canonical_sha256":"3c906d24f6f418390a7eefe256413fbd2ade8a66f1d89485914a17723711b0a4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:44:18.466876Z","signature_b64":"CuB3e0unNoJqg1ea3ChxS6+HzHBMXzVZw+572MSMGHwdcpj+7O1NRpl91etpgPrl2bSruVb7MoaeOJ+uVNEWAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3c906d24f6f418390a7eefe256413fbd2ade8a66f1d89485914a17723711b0a4","last_reissued_at":"2026-07-05T04:44:18.466397Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:44:18.466397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.11579","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:44:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ucr0+JUe30CUivkBN33wn/vUJ9d+xVdO2TiEG5PIZ2QkSm6eeUZkNE0UdU1b/CIhVpH98ZxcjoXVCn9kPegnAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:15:19.554097Z"},"content_sha256":"fa71506723d241a3ccd2e0ec8c29c8463884cb60b85bd48212830815c5a18ad0","schema_version":"1.0","event_id":"sha256:fa71506723d241a3ccd2e0ec8c29c8463884cb60b85bd48212830815c5a18ad0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:HSIG2JHW6QMDSCT657RFMQJ7XU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Modeling Item Response Theory with Stochastic Variational Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Benjamin W. Domingue, Chris Piech, Mike Wu, Noah Goodman, Richard L. Davis","submitted_at":"2021-08-26T05:00:27Z","abstract_excerpt":"Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving psychometric modeling leading to improved scientific understanding and public policy. However, while larger datasets allow for more flexible approaches, many contemporary algorithms for fitting IRT models may also have massive computational demands that forbid real-world application. To address this bottleneck, we introduce a variational Bayesian inferenc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.11579","kind":"arxiv","version":2},"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/2108.11579/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":"2026-07-05T04:44:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7sXCaR/FAWwqRv8YzH4YQA8NXT2K/edv47HXcFsF89xsRrmkdA57J4TQxULAA+Ur2mQvjv1ch4I22srIt8/DBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T18:15:19.554621Z"},"content_sha256":"68a780d236a27d36adebd6c6fcb6aeada09a5cf78de547020fa2b28bd84e460d","schema_version":"1.0","event_id":"sha256:68a780d236a27d36adebd6c6fcb6aeada09a5cf78de547020fa2b28bd84e460d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HSIG2JHW6QMDSCT657RFMQJ7XU/bundle.json","state_url":"https://pith.science/pith/HSIG2JHW6QMDSCT657RFMQJ7XU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HSIG2JHW6QMDSCT657RFMQJ7XU/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":"2026-08-09T18:15:19Z","links":{"resolver":"https://pith.science/pith/HSIG2JHW6QMDSCT657RFMQJ7XU","bundle":"https://pith.science/pith/HSIG2JHW6QMDSCT657RFMQJ7XU/bundle.json","state":"https://pith.science/pith/HSIG2JHW6QMDSCT657RFMQJ7XU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HSIG2JHW6QMDSCT657RFMQJ7XU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:HSIG2JHW6QMDSCT657RFMQJ7XU","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":"dc2a783752b44e25b0a7a83078d37f89ce11d54723fb258de905483b9573732d","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-26T05:00:27Z","title_canon_sha256":"3622252bd250e8da134aa7df1115756d5f17d6273ecc1c1d2418c0e7a0340a1c"},"schema_version":"1.0","source":{"id":"2108.11579","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.11579","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"arxiv_version","alias_value":"2108.11579v2","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.11579","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"pith_short_12","alias_value":"HSIG2JHW6QMD","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"pith_short_16","alias_value":"HSIG2JHW6QMDSCT6","created_at":"2026-07-05T04:44:18Z"},{"alias_kind":"pith_short_8","alias_value":"HSIG2JHW","created_at":"2026-07-05T04:44:18Z"}],"graph_snapshots":[{"event_id":"sha256:68a780d236a27d36adebd6c6fcb6aeada09a5cf78de547020fa2b28bd84e460d","target":"graph","created_at":"2026-07-05T04:44:18Z","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/2108.11579/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Item Response Theory (IRT) is a ubiquitous model for understanding human behaviors and attitudes based on their responses to questions. Large modern datasets offer opportunities to capture more nuances in human behavior, potentially improving psychometric modeling leading to improved scientific understanding and public policy. However, while larger datasets allow for more flexible approaches, many contemporary algorithms for fitting IRT models may also have massive computational demands that forbid real-world application. To address this bottleneck, we introduce a variational Bayesian inferenc","authors_text":"Benjamin W. Domingue, Chris Piech, Mike Wu, Noah Goodman, Richard L. Davis","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-26T05:00:27Z","title":"Modeling Item Response Theory with Stochastic Variational Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.11579","kind":"arxiv","version":2},"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:fa71506723d241a3ccd2e0ec8c29c8463884cb60b85bd48212830815c5a18ad0","target":"record","created_at":"2026-07-05T04:44:18Z","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":"dc2a783752b44e25b0a7a83078d37f89ce11d54723fb258de905483b9573732d","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-08-26T05:00:27Z","title_canon_sha256":"3622252bd250e8da134aa7df1115756d5f17d6273ecc1c1d2418c0e7a0340a1c"},"schema_version":"1.0","source":{"id":"2108.11579","kind":"arxiv","version":2}},"canonical_sha256":"3c906d24f6f418390a7eefe256413fbd2ade8a66f1d89485914a17723711b0a4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3c906d24f6f418390a7eefe256413fbd2ade8a66f1d89485914a17723711b0a4","first_computed_at":"2026-07-05T04:44:18.466397Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:44:18.466397Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CuB3e0unNoJqg1ea3ChxS6+HzHBMXzVZw+572MSMGHwdcpj+7O1NRpl91etpgPrl2bSruVb7MoaeOJ+uVNEWAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:44:18.466876Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.11579","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fa71506723d241a3ccd2e0ec8c29c8463884cb60b85bd48212830815c5a18ad0","sha256:68a780d236a27d36adebd6c6fcb6aeada09a5cf78de547020fa2b28bd84e460d"],"state_sha256":"bdb9ff96b67cd28e6be8ec3b69258091d0a6f791e644d32101b382d6b332d29d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JS2BB0JzTJhp5xzNbxhACOGYlyhJ4JwXVF1MsSmo/sOnnVuYFalr2MvtMR0wbq7q+8TI+jriE2Ufr4YgHQgWCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T18:15:19.560413Z","bundle_sha256":"049a11a97641ae4c39392142c222214dbeefd0fda87742ed1327cbc13af6869c"}}