{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:JALCAYJACD26HUKEMPJHURX5QZ","short_pith_number":"pith:JALCAYJA","canonical_record":{"source":{"id":"2406.04824","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1ab84099b2668a1b15f26e3995e974912ee60d5c1a856dc6f47e1c1c619b071d","abstract_canon_sha256":"a6802e5b80e96b6a4c57cd9e2bc3ba416837d20b5813fe2159cfd383a3c68f42"},"schema_version":"1.0"},"canonical_sha256":"481620612010f5e3d14463d27a46fd864579933046538750377be64fab6c7693","source":{"kind":"arxiv","id":"2406.04824","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.04824","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"arxiv_version","alias_value":"2406.04824v2","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.04824","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_12","alias_value":"JALCAYJACD26","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_16","alias_value":"JALCAYJACD26HUKE","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_8","alias_value":"JALCAYJA","created_at":"2026-07-05T08:38:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:JALCAYJACD26HUKEMPJHURX5QZ","target":"record","payload":{"canonical_record":{"source":{"id":"2406.04824","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1ab84099b2668a1b15f26e3995e974912ee60d5c1a856dc6f47e1c1c619b071d","abstract_canon_sha256":"a6802e5b80e96b6a4c57cd9e2bc3ba416837d20b5813fe2159cfd383a3c68f42"},"schema_version":"1.0"},"canonical_sha256":"481620612010f5e3d14463d27a46fd864579933046538750377be64fab6c7693","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:38.503876Z","signature_b64":"NmjFb97FLe/6HXR2tZRr4z5DE7hpjzlOAv75jx8VB9UU+nVdGhZZPifEpmpY+BvhYfNgBzT6lFw6dICpxF52AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"481620612010f5e3d14463d27a46fd864579933046538750377be64fab6c7693","last_reissued_at":"2026-07-05T08:38:38.503403Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:38.503403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.04824","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":"2026-07-05T08:38:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ii0vldyt92XRP4jXxqSvGSOoFEp0J/glYhvKD/ZcjgWuNits9Cf83Um2+rFT8Tq+s0htS5BOnTxtiBT+Nnu9CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:55:58.321498Z"},"content_sha256":"3d57d3ac66165040378c2b3e28948ee0d33a178d9afe6b7bbc4cd4cce1e787b3","schema_version":"1.0","event_id":"sha256:3d57d3ac66165040378c2b3e28948ee0d33a178d9afe6b7bbc4cd4cce1e787b3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:JALCAYJACD26HUKEMPJHURX5QZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alan Malek, Alexis Bellot, Eleni Sgouritsa, Francisco J. R. Ruiz, Ira Ktena, Jessica Schrouff, Silvia Chiappa, Virginia Aglietti","submitted_at":"2024-06-07T10:49:59Z","abstract_excerpt":"The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The best-performing AF can vary significantly across optimization problems, often requiring ad-hoc and problem-specific choices. This work tackles the challenge of designing novel AFs that perform well across a variety of experimental settings. Based on FunSearch, a recent work using Large Language Models (LLMs) for discovery in mathematical sciences, we propose FunBO, an LLM-based method that can be used to learn new AFs "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.04824","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/2406.04824/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":"2026-07-05T08:38:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9Mb0DyJmqMjaAB1fCt5aIGM5sdlnx5s8hyeAKbqsWihpAhusJ02ueud0t6JMdKGtjsYN1xF1Q0lzqKuEc+zKBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-03T20:55:58.322004Z"},"content_sha256":"17ae10a98ca0f60b7238c8dce0e5ddc3f2be777841df0e645c45103bb35d50d3","schema_version":"1.0","event_id":"sha256:17ae10a98ca0f60b7238c8dce0e5ddc3f2be777841df0e645c45103bb35d50d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JALCAYJACD26HUKEMPJHURX5QZ/bundle.json","state_url":"https://pith.science/pith/JALCAYJACD26HUKEMPJHURX5QZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JALCAYJACD26HUKEMPJHURX5QZ/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":"2026-08-03T20:55:58Z","links":{"resolver":"https://pith.science/pith/JALCAYJACD26HUKEMPJHURX5QZ","bundle":"https://pith.science/pith/JALCAYJACD26HUKEMPJHURX5QZ/bundle.json","state":"https://pith.science/pith/JALCAYJACD26HUKEMPJHURX5QZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JALCAYJACD26HUKEMPJHURX5QZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:JALCAYJACD26HUKEMPJHURX5QZ","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":"a6802e5b80e96b6a4c57cd9e2bc3ba416837d20b5813fe2159cfd383a3c68f42","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","title_canon_sha256":"1ab84099b2668a1b15f26e3995e974912ee60d5c1a856dc6f47e1c1c619b071d"},"schema_version":"1.0","source":{"id":"2406.04824","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.04824","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"arxiv_version","alias_value":"2406.04824v2","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.04824","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_12","alias_value":"JALCAYJACD26","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_16","alias_value":"JALCAYJACD26HUKE","created_at":"2026-07-05T08:38:38Z"},{"alias_kind":"pith_short_8","alias_value":"JALCAYJA","created_at":"2026-07-05T08:38:38Z"}],"graph_snapshots":[{"event_id":"sha256:17ae10a98ca0f60b7238c8dce0e5ddc3f2be777841df0e645c45103bb35d50d3","target":"graph","created_at":"2026-07-05T08:38:38Z","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/2406.04824/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The sample efficiency of Bayesian optimization algorithms depends on carefully crafted acquisition functions (AFs) guiding the sequential collection of function evaluations. The best-performing AF can vary significantly across optimization problems, often requiring ad-hoc and problem-specific choices. This work tackles the challenge of designing novel AFs that perform well across a variety of experimental settings. Based on FunSearch, a recent work using Large Language Models (LLMs) for discovery in mathematical sciences, we propose FunBO, an LLM-based method that can be used to learn new AFs ","authors_text":"Alan Malek, Alexis Bellot, Eleni Sgouritsa, Francisco J. R. Ruiz, Ira Ktena, Jessica Schrouff, Silvia Chiappa, Virginia Aglietti","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","title":"FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.04824","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":"sha256:3d57d3ac66165040378c2b3e28948ee0d33a178d9afe6b7bbc4cd4cce1e787b3","target":"record","created_at":"2026-07-05T08:38:38Z","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":"a6802e5b80e96b6a4c57cd9e2bc3ba416837d20b5813fe2159cfd383a3c68f42","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-07T10:49:59Z","title_canon_sha256":"1ab84099b2668a1b15f26e3995e974912ee60d5c1a856dc6f47e1c1c619b071d"},"schema_version":"1.0","source":{"id":"2406.04824","kind":"arxiv","version":2}},"canonical_sha256":"481620612010f5e3d14463d27a46fd864579933046538750377be64fab6c7693","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"481620612010f5e3d14463d27a46fd864579933046538750377be64fab6c7693","first_computed_at":"2026-07-05T08:38:38.503403Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:38.503403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NmjFb97FLe/6HXR2tZRr4z5DE7hpjzlOAv75jx8VB9UU+nVdGhZZPifEpmpY+BvhYfNgBzT6lFw6dICpxF52AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:38.503876Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.04824","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3d57d3ac66165040378c2b3e28948ee0d33a178d9afe6b7bbc4cd4cce1e787b3","sha256:17ae10a98ca0f60b7238c8dce0e5ddc3f2be777841df0e645c45103bb35d50d3"],"state_sha256":"25e1178ff6d5fe95d98e9b4bbcaa739e7012cfcef8cf4868e568b1b9303ab55c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eOdYud6Eruo46ASS3h66buJG7D+AvVXJkH5+w2gKcTSNfBaG8qQtUZYZLIW47dshD4zhcPZlkjvZZPa5GBFIBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-03T20:55:58.326385Z","bundle_sha256":"9823d5cf06f99d1f7e90743aa86c8bef8424b92b3f3202e5c2c7ae78e876807a"}}