{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:QEP33ICZFE3RFOJFEIZWY4ZEHN","short_pith_number":"pith:QEP33ICZ","canonical_record":{"source":{"id":"2605.17000","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T13:53:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f3a2c3e37183d47e3fe991d4b3d6ba918a7df437d722b49400b7be09599d07a8","abstract_canon_sha256":"a964e91e3a00451ef5cc7b438a185025a4363acea902531dfb9abfe24cf9a797"},"schema_version":"1.0"},"canonical_sha256":"811fbda059293712b92522336c73243b4134627b6d7f43eb1144ba1b6a8cd345","source":{"kind":"arxiv","id":"2605.17000","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17000","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17000v1","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17000","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"pith_short_12","alias_value":"QEP33ICZFE3R","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"pith_short_16","alias_value":"QEP33ICZFE3RFOJF","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"pith_short_8","alias_value":"QEP33ICZ","created_at":"2026-05-20T00:03:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:QEP33ICZFE3RFOJFEIZWY4ZEHN","target":"record","payload":{"canonical_record":{"source":{"id":"2605.17000","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T13:53:44Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f3a2c3e37183d47e3fe991d4b3d6ba918a7df437d722b49400b7be09599d07a8","abstract_canon_sha256":"a964e91e3a00451ef5cc7b438a185025a4363acea902531dfb9abfe24cf9a797"},"schema_version":"1.0"},"canonical_sha256":"811fbda059293712b92522336c73243b4134627b6d7f43eb1144ba1b6a8cd345","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:35.330534Z","signature_b64":"FqROG/pvz461dJ6XmJl+aPGIPVB2EdFJPu/XOJCqvUROBRUSx3B7MlFzYWDtZ7T3pQO5zRaG6rRFALSLdsbKBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"811fbda059293712b92522336c73243b4134627b6d7f43eb1144ba1b6a8cd345","last_reissued_at":"2026-05-20T00:03:35.329787Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:35.329787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.17000","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":"2026-05-20T00:03:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"szuJ0iVC/I2OBqiFBegsU0zEQ4kp9252aWusYHJvDuCqstQ+ODFUkM741ADRrlEj4Zv3fB0Ad3w4upOuKZJ6AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:25:24.442335Z"},"content_sha256":"b9d824376130195c1745a742044f5172a5ddf6761429cd05f76be336d4eab08a","schema_version":"1.0","event_id":"sha256:b9d824376130195c1745a742044f5172a5ddf6761429cd05f76be336d4eab08a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:QEP33ICZFE3RFOJFEIZWY4ZEHN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"BoLT supplies lightweight surrogate models from thousands of real LLM runs so black-box optimization researchers can test methods on realistic expensive tasks without prohibitive costs.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Apivich Hemachandra, Bryan Kian Hsiang Low, Ruth Wan Theng Chew, Zhiliang Chen","submitted_at":"2026-05-16T13:53:44Z","abstract_excerpt":"Optimization of LLM training and inference configurations, such as hyperparameters, data mixtures, and prompts, is critical to performance, but it is often approached heuristically in practice, leading to potentially suboptimal outcomes. By framing them as noisy, expensive, and derivative-free optimization problems, Bayesian optimization (BO) and other black-box optimization (BBO) methods offer a promising yet underexplored direction for principled, sample-efficient methods. However, LLM training and inference costs are prohibitively high for most of the BBO research community, and new methods"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"BoLT is the first LLM-centric benchmark that democratizes LLM research for the BBO community by releasing lightweight surrogate models fitted to the results of thousands of real LLM experiments, covering multi-fidelity, multi-objective, heteroscedastic noise, and high-dimensional search spaces; selected BO methods consistently outperform others across tasks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The surrogate models fitted to the real LLM experiment data accurately reproduce the optimization landscapes, noise characteristics, and relative performance ordering of methods that would be observed on the actual expensive LLM tasks.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"BoLT supplies lightweight surrogate models from thousands of real LLM runs so black-box optimization researchers can test methods on realistic expensive tasks without prohibitive costs.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"70aca01a533732907a7ad44de7eeaeaa2c1525776d6d5d4b9baa7f1cbf99a33f"},"source":{"id":"2605.17000","kind":"arxiv","version":1},"verdict":{"id":"f61b08d3-32eb-4075-aa6a-3b17e362c2f9","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T20:44:11.678175Z","strongest_claim":"BoLT is the first LLM-centric benchmark that democratizes LLM research for the BBO community by releasing lightweight surrogate models fitted to the results of thousands of real LLM experiments, covering multi-fidelity, multi-objective, heteroscedastic noise, and high-dimensional search spaces; selected BO methods consistently outperform others across tasks.","one_line_summary":"BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The surrogate models fitted to the real LLM experiment data accurately reproduce the optimization landscapes, noise characteristics, and relative performance ordering of methods that would be observed on the actual expensive LLM tasks.","pith_extraction_headline":"BoLT supplies lightweight surrogate models from thousands of real LLM runs so black-box optimization researchers can test methods on realistic expensive tasks without prohibitive costs."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.17000/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.042089Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T20:50:49.324216Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T19:49:57.217107Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T19:23:35.703275Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.199469Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.289203Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"94a1fdb50e28e9b10d753a8f660f756dca7cd805a8b8664b476947017dbbe384"},"references":{"count":83,"sample":[{"doi":"","year":2019,"title":"T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama. Optuna: A next-generation hyperparameter optimization framework. InProceedings of the 25th ACM SIGKDD international conference on knowledge discov","work_id":"d20e6819-eb1b-4930-b470-43b157b22eb5","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"A survey on data selection for language models","work_id":"465cc66d-dd4e-459f-8514-738e42d0a154","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"S. Ament, S. Daulton, D. Eriksson, M. Balandat, and E. Bakshy. Unexpected improvements to expected improvement for Bayesian optimization.Advances in Neural Information Processing Systems, 36:20577– 20","work_id":"3c6abfc3-e42b-4488-ad7d-0b9a9a0bc9eb","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"S. P. Arango, H. S. Jomaa, M. Wistuba, and J. Grabocka. Hpo-b: A large-scale reproducible benchmark for black-box hpo based on openml. InThirty-fifth Conference on Neural Information Processing System","work_id":"81743c87-76d8-432e-bfeb-4c467c5c5a63","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Program Synthesis with Large Language Models","work_id":"fd241a05-03b9-4de2-9588-9d77ce176125","ref_index":5,"cited_arxiv_id":"2108.07732","is_internal_anchor":true}],"resolved_work":83,"snapshot_sha256":"837f504f767edac88e36aab74844e06b397197f591bff9868a9d7f6283034c1f","internal_anchors":7},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6ad2bebc9596f9f11f0ded588f15cc676176669f72e3635af976307a46d1fad5"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"f61b08d3-32eb-4075-aa6a-3b17e362c2f9"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SmKA+ODjOqiaFqP0YBC8FdjPfYKTSf1EuzPH82XensVzOPfNcBad726K4TmCX6tDDaMW/rsugY7fdvYEgDfqBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:25:24.443109Z"},"content_sha256":"8b95b3cd672c6dd03f9b5fe5c73725d5b54cd953507bbe5f028cb4f97880a41f","schema_version":"1.0","event_id":"sha256:8b95b3cd672c6dd03f9b5fe5c73725d5b54cd953507bbe5f028cb4f97880a41f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QEP33ICZFE3RFOJFEIZWY4ZEHN/bundle.json","state_url":"https://pith.science/pith/QEP33ICZFE3RFOJFEIZWY4ZEHN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QEP33ICZFE3RFOJFEIZWY4ZEHN/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":"2026-05-25T21:25:24Z","links":{"resolver":"https://pith.science/pith/QEP33ICZFE3RFOJFEIZWY4ZEHN","bundle":"https://pith.science/pith/QEP33ICZFE3RFOJFEIZWY4ZEHN/bundle.json","state":"https://pith.science/pith/QEP33ICZFE3RFOJFEIZWY4ZEHN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QEP33ICZFE3RFOJFEIZWY4ZEHN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QEP33ICZFE3RFOJFEIZWY4ZEHN","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":"a964e91e3a00451ef5cc7b438a185025a4363acea902531dfb9abfe24cf9a797","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T13:53:44Z","title_canon_sha256":"f3a2c3e37183d47e3fe991d4b3d6ba918a7df437d722b49400b7be09599d07a8"},"schema_version":"1.0","source":{"id":"2605.17000","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17000","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17000v1","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17000","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"pith_short_12","alias_value":"QEP33ICZFE3R","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"pith_short_16","alias_value":"QEP33ICZFE3RFOJF","created_at":"2026-05-20T00:03:35Z"},{"alias_kind":"pith_short_8","alias_value":"QEP33ICZ","created_at":"2026-05-20T00:03:35Z"}],"graph_snapshots":[{"event_id":"sha256:8b95b3cd672c6dd03f9b5fe5c73725d5b54cd953507bbe5f028cb4f97880a41f","target":"graph","created_at":"2026-05-20T00:03:35Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"BoLT is the first LLM-centric benchmark that democratizes LLM research for the BBO community by releasing lightweight surrogate models fitted to the results of thousands of real LLM experiments, covering multi-fidelity, multi-objective, heteroscedastic noise, and high-dimensional search spaces; selected BO methods consistently outperform others across tasks."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The surrogate models fitted to the real LLM experiment data accurately reproduce the optimization landscapes, noise characteristics, and relative performance ordering of methods that would be observed on the actual expensive LLM tasks."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"BoLT supplies lightweight surrogate models from thousands of real LLM runs so black-box optimization researchers can test methods on realistic expensive tasks without prohibitive costs."}],"snapshot_sha256":"70aca01a533732907a7ad44de7eeaeaa2c1525776d6d5d4b9baa7f1cbf99a33f"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6ad2bebc9596f9f11f0ded588f15cc676176669f72e3635af976307a46d1fad5"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T21:01:19.042089Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T20:50:49.324216Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T19:49:57.217107Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T19:23:35.703275Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T18:41:56.199469Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.289203Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.17000/integrity.json","findings":[],"snapshot_sha256":"94a1fdb50e28e9b10d753a8f660f756dca7cd805a8b8664b476947017dbbe384","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Optimization of LLM training and inference configurations, such as hyperparameters, data mixtures, and prompts, is critical to performance, but it is often approached heuristically in practice, leading to potentially suboptimal outcomes. By framing them as noisy, expensive, and derivative-free optimization problems, Bayesian optimization (BO) and other black-box optimization (BBO) methods offer a promising yet underexplored direction for principled, sample-efficient methods. However, LLM training and inference costs are prohibitively high for most of the BBO research community, and new methods","authors_text":"Apivich Hemachandra, Bryan Kian Hsiang Low, Ruth Wan Theng Chew, Zhiliang Chen","cross_cats":["cs.AI"],"headline":"BoLT supplies lightweight surrogate models from thousands of real LLM runs so black-box optimization researchers can test methods on realistic expensive tasks without prohibitive costs.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T13:53:44Z","title":"BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Tasks"},"references":{"count":83,"internal_anchors":7,"resolved_work":83,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama. Optuna: A next-generation hyperparameter optimization framework. InProceedings of the 25th ACM SIGKDD international conference on knowledge discov","work_id":"d20e6819-eb1b-4930-b470-43b157b22eb5","year":2019},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"A survey on data selection for language models","work_id":"465cc66d-dd4e-459f-8514-738e42d0a154","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"S. Ament, S. Daulton, D. Eriksson, M. Balandat, and E. Bakshy. Unexpected improvements to expected improvement for Bayesian optimization.Advances in Neural Information Processing Systems, 36:20577– 20","work_id":"3c6abfc3-e42b-4488-ad7d-0b9a9a0bc9eb","year":2023},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"S. P. Arango, H. S. Jomaa, M. Wistuba, and J. Grabocka. Hpo-b: A large-scale reproducible benchmark for black-box hpo based on openml. InThirty-fifth Conference on Neural Information Processing System","work_id":"81743c87-76d8-432e-bfeb-4c467c5c5a63","year":2021},{"cited_arxiv_id":"2108.07732","doi":"","is_internal_anchor":true,"ref_index":5,"title":"Program Synthesis with Large Language Models","work_id":"fd241a05-03b9-4de2-9588-9d77ce176125","year":2021}],"snapshot_sha256":"837f504f767edac88e36aab74844e06b397197f591bff9868a9d7f6283034c1f"},"source":{"id":"2605.17000","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T20:44:11.678175Z","id":"f61b08d3-32eb-4075-aa6a-3b17e362c2f9","model_set":{"reader":"grok-4.3"},"one_line_summary":"BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"BoLT supplies lightweight surrogate models from thousands of real LLM runs so black-box optimization researchers can test methods on realistic expensive tasks without prohibitive costs.","strongest_claim":"BoLT is the first LLM-centric benchmark that democratizes LLM research for the BBO community by releasing lightweight surrogate models fitted to the results of thousands of real LLM experiments, covering multi-fidelity, multi-objective, heteroscedastic noise, and high-dimensional search spaces; selected BO methods consistently outperform others across tasks.","weakest_assumption":"The surrogate models fitted to the real LLM experiment data accurately reproduce the optimization landscapes, noise characteristics, and relative performance ordering of methods that would be observed on the actual expensive LLM tasks."}},"verdict_id":"f61b08d3-32eb-4075-aa6a-3b17e362c2f9"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b9d824376130195c1745a742044f5172a5ddf6761429cd05f76be336d4eab08a","target":"record","created_at":"2026-05-20T00:03:35Z","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":"a964e91e3a00451ef5cc7b438a185025a4363acea902531dfb9abfe24cf9a797","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-16T13:53:44Z","title_canon_sha256":"f3a2c3e37183d47e3fe991d4b3d6ba918a7df437d722b49400b7be09599d07a8"},"schema_version":"1.0","source":{"id":"2605.17000","kind":"arxiv","version":1}},"canonical_sha256":"811fbda059293712b92522336c73243b4134627b6d7f43eb1144ba1b6a8cd345","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"811fbda059293712b92522336c73243b4134627b6d7f43eb1144ba1b6a8cd345","first_computed_at":"2026-05-20T00:03:35.329787Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:35.329787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FqROG/pvz461dJ6XmJl+aPGIPVB2EdFJPu/XOJCqvUROBRUSx3B7MlFzYWDtZ7T3pQO5zRaG6rRFALSLdsbKBQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:35.330534Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17000","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9d824376130195c1745a742044f5172a5ddf6761429cd05f76be336d4eab08a","sha256:8b95b3cd672c6dd03f9b5fe5c73725d5b54cd953507bbe5f028cb4f97880a41f"],"state_sha256":"2df32972e42be970fc80c2bc6838bc36fd8f0b2c9378f1e64010595917bd1810"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7r/Du23sKhaALDN+96hn9FG3NDngnzZgD9YLO4hKn8dTuV/pH316mLuQkbtFkh71hIoWz1qWYM3XAng/jFuYBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:25:24.446200Z","bundle_sha256":"978a36ed6ee78d470bdf28cc7107841e365431236d9f1e8df1509837c4cfa8ec"}}