{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:PDRYEL2L5JMICBRFEP7IA3DLEN","short_pith_number":"pith:PDRYEL2L","canonical_record":{"source":{"id":"2603.09793","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-10T15:25:56Z","cross_cats_sorted":[],"title_canon_sha256":"560e4eb75bbd08d321965422b63bea62bc0f4c37d6b077cb95041e124ff37e60","abstract_canon_sha256":"878a2f88056664d453db4440ff3dcaff917f82d0a07b270e1d0fc14330b6f4ab"},"schema_version":"1.0"},"canonical_sha256":"78e3822f4bea5881062523fe806c6b236129b7aabb00498374b02fea12a4ef74","source":{"kind":"arxiv","id":"2603.09793","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.09793","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"arxiv_version","alias_value":"2603.09793v2","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.09793","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"pith_short_12","alias_value":"PDRYEL2L5JMI","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"pith_short_16","alias_value":"PDRYEL2L5JMICBRF","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"pith_short_8","alias_value":"PDRYEL2L","created_at":"2026-07-22T01:23:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:PDRYEL2L5JMICBRFEP7IA3DLEN","target":"record","payload":{"canonical_record":{"source":{"id":"2603.09793","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-10T15:25:56Z","cross_cats_sorted":[],"title_canon_sha256":"560e4eb75bbd08d321965422b63bea62bc0f4c37d6b077cb95041e124ff37e60","abstract_canon_sha256":"878a2f88056664d453db4440ff3dcaff917f82d0a07b270e1d0fc14330b6f4ab"},"schema_version":"1.0"},"canonical_sha256":"78e3822f4bea5881062523fe806c6b236129b7aabb00498374b02fea12a4ef74","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-22T01:23:37.857805Z","signature_b64":"O0+B8oPChRwvHrW9VUZNbF1RF4vbPhRWS4n6Oe9UZEKX9a2X2kjCyBxInSRc5GBnHDCZ59Y33S+tDT8Sm+I+AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78e3822f4bea5881062523fe806c6b236129b7aabb00498374b02fea12a4ef74","last_reissued_at":"2026-07-22T01:23:37.856890Z","signature_status":"signed_v1","first_computed_at":"2026-07-22T01:23:37.856890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2603.09793","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":"2026-07-22T01:23:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZXJI9Ulyc2wQs+BtjHe4F/h96sh3tk93PgMY84cXZbe6YQ3LjYnyBF9IvWu3ju2zDAvsr/oCJn2Ti2YoaB4gAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:46:09.303109Z"},"content_sha256":"94a8293a25314fc035b9148b348c370a6a668cfac46d8b36edac9aaa7d697248","schema_version":"1.0","event_id":"sha256:94a8293a25314fc035b9148b348c370a6a668cfac46d8b36edac9aaa7d697248"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:PDRYEL2L5JMICBRFEP7IA3DLEN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Information Theoretic Bayesian Optimization over the Probability Simplex","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Antonio Candelieri, Federico Pavesi, No\\'emie Jaquier","submitted_at":"2026-03-10T15:25:56Z","abstract_excerpt":"Bayesian optimization is a data-efficient technique that has been shown to be extremely powerful to optimize expensive, black-box, and possibly noisy objective functions. Many applications involve optimizing probabilities and mixtures which naturally belong to the probability simplex, a constrained non-Euclidean domain defined by non-negative entries summing to one. This paper introduces $\\alpha$-GaBO, a novel family of Bayesian optimization algorithms over the probability simplex. Our approach is grounded in information geometry, a branch of Riemannian geometry which endows the simplex with a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.09793","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/2603.09793/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":"2026-07-22T01:23:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YX6INO86nfF0iNeLFxrZcxnJdsg2Giy8I2H6ZZMYYybtT1pW4tWFADYQpMZhTVm3nTo+uHHxoh6FIsfjRjOsAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T12:46:09.303658Z"},"content_sha256":"05170780975665814174df7f6b1b98cb6e9cb1a73f09ff06100b99b922ac5196","schema_version":"1.0","event_id":"sha256:05170780975665814174df7f6b1b98cb6e9cb1a73f09ff06100b99b922ac5196"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PDRYEL2L5JMICBRFEP7IA3DLEN/bundle.json","state_url":"https://pith.science/pith/PDRYEL2L5JMICBRFEP7IA3DLEN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PDRYEL2L5JMICBRFEP7IA3DLEN/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":"2026-08-05T12:46:09Z","links":{"resolver":"https://pith.science/pith/PDRYEL2L5JMICBRFEP7IA3DLEN","bundle":"https://pith.science/pith/PDRYEL2L5JMICBRFEP7IA3DLEN/bundle.json","state":"https://pith.science/pith/PDRYEL2L5JMICBRFEP7IA3DLEN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PDRYEL2L5JMICBRFEP7IA3DLEN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PDRYEL2L5JMICBRFEP7IA3DLEN","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":"878a2f88056664d453db4440ff3dcaff917f82d0a07b270e1d0fc14330b6f4ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-10T15:25:56Z","title_canon_sha256":"560e4eb75bbd08d321965422b63bea62bc0f4c37d6b077cb95041e124ff37e60"},"schema_version":"1.0","source":{"id":"2603.09793","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2603.09793","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"arxiv_version","alias_value":"2603.09793v2","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.09793","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"pith_short_12","alias_value":"PDRYEL2L5JMI","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"pith_short_16","alias_value":"PDRYEL2L5JMICBRF","created_at":"2026-07-22T01:23:37Z"},{"alias_kind":"pith_short_8","alias_value":"PDRYEL2L","created_at":"2026-07-22T01:23:37Z"}],"graph_snapshots":[{"event_id":"sha256:05170780975665814174df7f6b1b98cb6e9cb1a73f09ff06100b99b922ac5196","target":"graph","created_at":"2026-07-22T01:23:37Z","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/2603.09793/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Bayesian optimization is a data-efficient technique that has been shown to be extremely powerful to optimize expensive, black-box, and possibly noisy objective functions. Many applications involve optimizing probabilities and mixtures which naturally belong to the probability simplex, a constrained non-Euclidean domain defined by non-negative entries summing to one. This paper introduces $\\alpha$-GaBO, a novel family of Bayesian optimization algorithms over the probability simplex. Our approach is grounded in information geometry, a branch of Riemannian geometry which endows the simplex with a","authors_text":"Antonio Candelieri, Federico Pavesi, No\\'emie Jaquier","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-10T15:25:56Z","title":"Information Theoretic Bayesian Optimization over the Probability Simplex"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.09793","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":"sha256:94a8293a25314fc035b9148b348c370a6a668cfac46d8b36edac9aaa7d697248","target":"record","created_at":"2026-07-22T01:23:37Z","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":"878a2f88056664d453db4440ff3dcaff917f82d0a07b270e1d0fc14330b6f4ab","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-03-10T15:25:56Z","title_canon_sha256":"560e4eb75bbd08d321965422b63bea62bc0f4c37d6b077cb95041e124ff37e60"},"schema_version":"1.0","source":{"id":"2603.09793","kind":"arxiv","version":2}},"canonical_sha256":"78e3822f4bea5881062523fe806c6b236129b7aabb00498374b02fea12a4ef74","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78e3822f4bea5881062523fe806c6b236129b7aabb00498374b02fea12a4ef74","first_computed_at":"2026-07-22T01:23:37.856890Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-22T01:23:37.856890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O0+B8oPChRwvHrW9VUZNbF1RF4vbPhRWS4n6Oe9UZEKX9a2X2kjCyBxInSRc5GBnHDCZ59Y33S+tDT8Sm+I+AQ==","signature_status":"signed_v1","signed_at":"2026-07-22T01:23:37.857805Z","signed_message":"canonical_sha256_bytes"},"source_id":"2603.09793","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94a8293a25314fc035b9148b348c370a6a668cfac46d8b36edac9aaa7d697248","sha256:05170780975665814174df7f6b1b98cb6e9cb1a73f09ff06100b99b922ac5196"],"state_sha256":"5172d97cb7ceab461a80dc22cc2fd4cfd4587cbc5d924a652c2d367469518169"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UP2V5WEBZZ9eRGJhpp7L+Hn9BZbuJyDtseZZ/T8En+/EvJ9ckB2eJ8aKXBsRVdrVNGOwAY3j9nYQuG295XGEDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T12:46:09.310025Z","bundle_sha256":"eedbf25c1db171c236a3f2ee55d98e349c3ead87bc3de0b48bad5fb4156fc7a2"}}