{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:Y42X5WOVQS7TEZWGADTXTVKLZI","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":"7b857c1797da22b065d00c45fb831b82274c8bff4c2a5a02ca0b11ffc52effaf","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T19:34:28Z","title_canon_sha256":"05e0ade3b1b26162d7c7587e1c63daf0caee2eb753813d92c5fb15ed7a12f760"},"schema_version":"1.0","source":{"id":"2606.00309","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.00309","created_at":"2026-06-02T01:03:51Z"},{"alias_kind":"arxiv_version","alias_value":"2606.00309v1","created_at":"2026-06-02T01:03:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00309","created_at":"2026-06-02T01:03:51Z"},{"alias_kind":"pith_short_12","alias_value":"Y42X5WOVQS7T","created_at":"2026-06-02T01:03:51Z"},{"alias_kind":"pith_short_16","alias_value":"Y42X5WOVQS7TEZWG","created_at":"2026-06-02T01:03:51Z"},{"alias_kind":"pith_short_8","alias_value":"Y42X5WOV","created_at":"2026-06-02T01:03:51Z"}],"graph_snapshots":[{"event_id":"sha256:218fb47dfce3fa06436131afb322417e30b3949caaa6ba9f98ad9766c53f44e3","target":"graph","created_at":"2026-06-02T01:03:51Z","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/2606.00309/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in latent variable models. However, it remains unclear how to tune the algorithm's hyperparameters in a principled manner to ensure the uncertainty estimates are statistically meaningful. In this work, we address this gap in tuning guidance by developing a statistical scaling limit theory for SGLD--Gibbs. We derive a joint asymptotic limit for the global parameters and latent variables under appropriate space-time rescaling. We show that global p","authors_text":"Jonathan H. Huggins, Xiaoyu Wang","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T19:34:28Z","title":"Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00309","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:c272563e1cd7eafc63c67bde9227f4ac2aed8e5559584772abafb51a6d20b83b","target":"record","created_at":"2026-06-02T01:03:51Z","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":"7b857c1797da22b065d00c45fb831b82274c8bff4c2a5a02ca0b11ffc52effaf","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T19:34:28Z","title_canon_sha256":"05e0ade3b1b26162d7c7587e1c63daf0caee2eb753813d92c5fb15ed7a12f760"},"schema_version":"1.0","source":{"id":"2606.00309","kind":"arxiv","version":1}},"canonical_sha256":"c7357ed9d584bf3266c600e779d54bca1315729d8abdd3fba1e2b9b039fe66fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c7357ed9d584bf3266c600e779d54bca1315729d8abdd3fba1e2b9b039fe66fd","first_computed_at":"2026-06-02T01:03:51.238145Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-02T01:03:51.238145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"i4m9QJRQthEPFfnAYyATX5FHdayjv1nJAUmfcfK0fVEQ5gRs7Dm5cEwJlx2NwVFKkLX8pxChCxfoXPaDvzS+Cg==","signature_status":"signed_v1","signed_at":"2026-06-02T01:03:51.238551Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.00309","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c272563e1cd7eafc63c67bde9227f4ac2aed8e5559584772abafb51a6d20b83b","sha256:218fb47dfce3fa06436131afb322417e30b3949caaa6ba9f98ad9766c53f44e3"],"state_sha256":"efc36c75c6e2e0b6125326e745ce15d9585d2396fd0e8f5d19d424e65d0e19a2"}