{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XNAU2YXZR32YC5KATT76WF2NRR","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":"952d05ce4e42a55e482291ecd58721aa2ca7cd496058ef69817811a49a3e2bdc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-11T16:23:33Z","title_canon_sha256":"b83bf601007e8dda399557524fb6f17c45baafc0a5a7dba23eafc16e67750f84"},"schema_version":"1.0","source":{"id":"2406.07423","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.07423","created_at":"2026-07-05T08:30:23Z"},{"alias_kind":"arxiv_version","alias_value":"2406.07423v1","created_at":"2026-07-05T08:30:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07423","created_at":"2026-07-05T08:30:23Z"},{"alias_kind":"pith_short_12","alias_value":"XNAU2YXZR32Y","created_at":"2026-07-05T08:30:23Z"},{"alias_kind":"pith_short_16","alias_value":"XNAU2YXZR32YC5KA","created_at":"2026-07-05T08:30:23Z"},{"alias_kind":"pith_short_8","alias_value":"XNAU2YXZ","created_at":"2026-07-05T08:30:23Z"}],"graph_snapshots":[{"event_id":"sha256:a227fe8c1deb6bceeecd4492595cef565c1964959ee039d7a2ff0a3716b9240b","target":"graph","created_at":"2026-07-05T08:30:23Z","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/2406.07423/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Monte Carlo methods, Variational Inference, and their combinations play a pivotal role in sampling from intractable probability distributions. However, current studies lack a unified evaluation framework, relying on disparate performance measures and limited method comparisons across diverse tasks, complicating the assessment of progress and hindering the decision-making of practitioners. In response to these challenges, our work introduces a benchmark that evaluates sampling methods using a standardized task suite and a broad range of performance criteria. Moreover, we study existing metrics ","authors_text":"Denis Blessing, Francisco Vargas, Gerhard Neumann, Johannes Esslinger, Xiaogang Jia","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-11T16:23:33Z","title":"Beyond ELBOs: A Large-Scale Evaluation of Variational Methods for Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07423","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":"sha256:6d6792e6a960f13c35af637f874791a1e95234fd1431d1785e22f5d93de061f0","target":"record","created_at":"2026-07-05T08:30:23Z","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":"952d05ce4e42a55e482291ecd58721aa2ca7cd496058ef69817811a49a3e2bdc","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-11T16:23:33Z","title_canon_sha256":"b83bf601007e8dda399557524fb6f17c45baafc0a5a7dba23eafc16e67750f84"},"schema_version":"1.0","source":{"id":"2406.07423","kind":"arxiv","version":1}},"canonical_sha256":"bb414d62f98ef58175409cffeb174d8c6ed48cd95d556521761e4ae82c134c54","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb414d62f98ef58175409cffeb174d8c6ed48cd95d556521761e4ae82c134c54","first_computed_at":"2026-07-05T08:30:23.433657Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:30:23.433657Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"sd96yhK9VeBDboqaCnoIGicitUn6morIt9td2VOUbsZLjRstp5wTNDb2W3jCoyMByg7PY58LhwMBHeQUKEGCCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:30:23.434325Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.07423","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6d6792e6a960f13c35af637f874791a1e95234fd1431d1785e22f5d93de061f0","sha256:a227fe8c1deb6bceeecd4492595cef565c1964959ee039d7a2ff0a3716b9240b"],"state_sha256":"7c6f73225ad0c2c62e30ce3470dfabd7b05b15a290c6f839e094a85d7191020a"}