{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4MOV3ZZQ6N44LP4TYGDXZXCKUL","short_pith_number":"pith:4MOV3ZZQ","canonical_record":{"source":{"id":"2607.06776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-07T20:17:05Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"d95d2355bec006e6ac6ceb6b5a693de431d33ca4ff62c34f37a73334370b19cf","abstract_canon_sha256":"fa2adfe8b5f9b439ca475dd18976e09dae4af424fc0d33e3573bb896adb074e1"},"schema_version":"1.0"},"canonical_sha256":"e31d5de730f379c5bf93c1877cdc4aa2f33108c530e1f986a2331e8b1ea32440","source":{"kind":"arxiv","id":"2607.06776","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.06776","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"arxiv_version","alias_value":"2607.06776v1","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.06776","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"pith_short_12","alias_value":"4MOV3ZZQ6N44","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"pith_short_16","alias_value":"4MOV3ZZQ6N44LP4T","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"pith_short_8","alias_value":"4MOV3ZZQ","created_at":"2026-07-09T00:19:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4MOV3ZZQ6N44LP4TYGDXZXCKUL","target":"record","payload":{"canonical_record":{"source":{"id":"2607.06776","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-07T20:17:05Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"d95d2355bec006e6ac6ceb6b5a693de431d33ca4ff62c34f37a73334370b19cf","abstract_canon_sha256":"fa2adfe8b5f9b439ca475dd18976e09dae4af424fc0d33e3573bb896adb074e1"},"schema_version":"1.0"},"canonical_sha256":"e31d5de730f379c5bf93c1877cdc4aa2f33108c530e1f986a2331e8b1ea32440","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-09T00:19:26.530115Z","signature_b64":"KSjuFSqkxDDbFDZv6473vRZ0agUFZ3pQTK2gLjdD8Vm+lcWX6GqzeocXnM2yo9sDwptwfCdKwI7UY08wKuVtAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e31d5de730f379c5bf93c1877cdc4aa2f33108c530e1f986a2331e8b1ea32440","last_reissued_at":"2026-07-09T00:19:26.529494Z","signature_status":"signed_v1","first_computed_at":"2026-07-09T00:19:26.529494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.06776","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":"2026-07-09T00:19:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"42wbMFibOYKlK/vX+HUU7WWQiwn8yktXCD1XRNPKyzX7HOCnHAAbsxWp9j5bUz4XY1eIKHUcOlWwans5K+kDDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T18:01:29.481352Z"},"content_sha256":"60fbbd27673c26b0cb76643803342b959a9f342fcc8a73d88e5ead8f7b3ecf1d","schema_version":"1.0","event_id":"sha256:60fbbd27673c26b0cb76643803342b959a9f342fcc8a73d88e5ead8f7b3ecf1d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4MOV3ZZQ6N44LP4TYGDXZXCKUL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Bayesian Deep Ensembles via Analytic Predictive Inference","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"cs.LG","authors_text":"Jaesung Lee, Marie Maros, Sina Aghaee Dabaghan Fard","submitted_at":"2026-07-07T20:17:05Z","abstract_excerpt":"We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency. Our method combines the statistical rigor of Bayesian inference with the scalability of deep ensembles, providing calibrated uncertainty estimates that enable its use not only for standalone prediction but also as a component within broader learning systems. To achieve these goals, our work relies on three key design components: (i) low-dimensional ensemble representation: predictions are expres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.06776","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/2607.06776/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":"2026-07-09T00:19:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uGAIlVUNwJrvufrqi2uflG1PI4f+JYZoIMRYB4YTu3BAr3ttuyWTuxcTN3PonN7hErSexZOz//oWAwd0kRHgCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T18:01:29.481735Z"},"content_sha256":"10912dcc804828d4e82cb63dcd4957e720012cf698bc527b0827ec1690288850","schema_version":"1.0","event_id":"sha256:10912dcc804828d4e82cb63dcd4957e720012cf698bc527b0827ec1690288850"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4MOV3ZZQ6N44LP4TYGDXZXCKUL/bundle.json","state_url":"https://pith.science/pith/4MOV3ZZQ6N44LP4TYGDXZXCKUL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4MOV3ZZQ6N44LP4TYGDXZXCKUL/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":"2026-07-14T18:01:29Z","links":{"resolver":"https://pith.science/pith/4MOV3ZZQ6N44LP4TYGDXZXCKUL","bundle":"https://pith.science/pith/4MOV3ZZQ6N44LP4TYGDXZXCKUL/bundle.json","state":"https://pith.science/pith/4MOV3ZZQ6N44LP4TYGDXZXCKUL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4MOV3ZZQ6N44LP4TYGDXZXCKUL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4MOV3ZZQ6N44LP4TYGDXZXCKUL","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":"fa2adfe8b5f9b439ca475dd18976e09dae4af424fc0d33e3573bb896adb074e1","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-07T20:17:05Z","title_canon_sha256":"d95d2355bec006e6ac6ceb6b5a693de431d33ca4ff62c34f37a73334370b19cf"},"schema_version":"1.0","source":{"id":"2607.06776","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.06776","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"arxiv_version","alias_value":"2607.06776v1","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.06776","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"pith_short_12","alias_value":"4MOV3ZZQ6N44","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"pith_short_16","alias_value":"4MOV3ZZQ6N44LP4T","created_at":"2026-07-09T00:19:26Z"},{"alias_kind":"pith_short_8","alias_value":"4MOV3ZZQ","created_at":"2026-07-09T00:19:26Z"}],"graph_snapshots":[{"event_id":"sha256:10912dcc804828d4e82cb63dcd4957e720012cf698bc527b0827ec1690288850","target":"graph","created_at":"2026-07-09T00:19:26Z","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/2607.06776/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We introduce an efficient Bayesian deep ensemble method for predictive regression designed to enhance interpretability while maintaining competitive predictive performance and computational efficiency. Our method combines the statistical rigor of Bayesian inference with the scalability of deep ensembles, providing calibrated uncertainty estimates that enable its use not only for standalone prediction but also as a component within broader learning systems. To achieve these goals, our work relies on three key design components: (i) low-dimensional ensemble representation: predictions are expres","authors_text":"Jaesung Lee, Marie Maros, Sina Aghaee Dabaghan Fard","cross_cats":["stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-07T20:17:05Z","title":"Efficient Bayesian Deep Ensembles via Analytic Predictive Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.06776","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":"sha256:60fbbd27673c26b0cb76643803342b959a9f342fcc8a73d88e5ead8f7b3ecf1d","target":"record","created_at":"2026-07-09T00:19:26Z","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":"fa2adfe8b5f9b439ca475dd18976e09dae4af424fc0d33e3573bb896adb074e1","cross_cats_sorted":["stat.ME"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-07T20:17:05Z","title_canon_sha256":"d95d2355bec006e6ac6ceb6b5a693de431d33ca4ff62c34f37a73334370b19cf"},"schema_version":"1.0","source":{"id":"2607.06776","kind":"arxiv","version":1}},"canonical_sha256":"e31d5de730f379c5bf93c1877cdc4aa2f33108c530e1f986a2331e8b1ea32440","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e31d5de730f379c5bf93c1877cdc4aa2f33108c530e1f986a2331e8b1ea32440","first_computed_at":"2026-07-09T00:19:26.529494Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-09T00:19:26.529494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KSjuFSqkxDDbFDZv6473vRZ0agUFZ3pQTK2gLjdD8Vm+lcWX6GqzeocXnM2yo9sDwptwfCdKwI7UY08wKuVtAw==","signature_status":"signed_v1","signed_at":"2026-07-09T00:19:26.530115Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.06776","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:60fbbd27673c26b0cb76643803342b959a9f342fcc8a73d88e5ead8f7b3ecf1d","sha256:10912dcc804828d4e82cb63dcd4957e720012cf698bc527b0827ec1690288850"],"state_sha256":"dcb27bb5f94ec8a10a87a47f64234beb9e7c7cb3b9979267da7ea2bbad07f775"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZQ1V1v0b9cEo6aDMV5rQS4Z2xkhy0jbVltBJdI/+5unqTxA0OUMuBH1nCZJkYpKUqLwI/CCQChgMXYHMSWewBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-14T18:01:29.483865Z","bundle_sha256":"ad1dcc3af43599f779f10ca6173851c7794b5c914ffc097f0d67cce081175150"}}