{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:UXKJKG3GJDEY7YBM46U5K7HRIK","short_pith_number":"pith:UXKJKG3G","canonical_record":{"source":{"id":"2605.15511","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T01:12:09Z","cross_cats_sorted":[],"title_canon_sha256":"9b61f5031366aff14187d71aa99c2eb8770718f2f89e14640db44fe252f36821","abstract_canon_sha256":"c643ffad124702754f682bfb927972900c98c8a053cb467674cf77b68c71c771"},"schema_version":"1.0"},"canonical_sha256":"a5d4951b6648c98fe02ce7a9d57cf142a8604759543f7b80f12e1f2db382f659","source":{"kind":"arxiv","id":"2605.15511","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15511","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15511v1","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15511","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_12","alias_value":"UXKJKG3GJDEY","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_16","alias_value":"UXKJKG3GJDEY7YBM","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_8","alias_value":"UXKJKG3G","created_at":"2026-05-20T00:01:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:UXKJKG3GJDEY7YBM46U5K7HRIK","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15511","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T01:12:09Z","cross_cats_sorted":[],"title_canon_sha256":"9b61f5031366aff14187d71aa99c2eb8770718f2f89e14640db44fe252f36821","abstract_canon_sha256":"c643ffad124702754f682bfb927972900c98c8a053cb467674cf77b68c71c771"},"schema_version":"1.0"},"canonical_sha256":"a5d4951b6648c98fe02ce7a9d57cf142a8604759543f7b80f12e1f2db382f659","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:02.391861Z","signature_b64":"cME0yrptYJ78LlX8DjwC3hXMQap/fHbtX9I2IDABG4j0QHW+o7Q/gP9gxoczXQX34/P9KdjXBfyQa3CMXMcVBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a5d4951b6648c98fe02ce7a9d57cf142a8604759543f7b80f12e1f2db382f659","last_reissued_at":"2026-05-20T00:01:02.390963Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:02.390963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15511","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":"2026-05-20T00:01:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9EkvlvMRaFCE8NrX5+9//lWckruTtf/cfCn049H4Z8yU3oxfytPcomoTj5tLYZ2+ZtkmmK3zRO5JJ9JY7LbACg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:00:28.629864Z"},"content_sha256":"9953fe39dd145e33634c90ebd7652c636e532de695fec1895e7f79bf86005473","schema_version":"1.0","event_id":"sha256:9953fe39dd145e33634c90ebd7652c636e532de695fec1895e7f79bf86005473"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:UXKJKG3GJDEY7YBM46U5K7HRIK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Graph neural networks often fail to outperform simple MLPs on omics data tasks with few samples and many nodes.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Guillermo Bern\\'ardez, Johan Mathe, Louisa Cornelis, Louis Van Langendonck, Nina Miolane","submitted_at":"2026-05-15T01:12:09Z","abstract_excerpt":"Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime $n \\gg p$, where the number of graphs $n$ greatly exceeds the number of nodes per graph $p$. This overlooks biological domains such as omics, which operate in the opposite $n \\ll p$ regime, characterized by large graphs of genes, transcripts, or proteins across few patient samples. This raises the question: \\textit{how do GNNs perform in this low-sample, high-node omics setting?} We introduce \\texttt{OgBench} (Omics-Graph Bench), the first benchmarking pla"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results show that widely used GNNs often do not outperform simple MLPs and classical baselines. These findings challenge the prevailing assumption that graph structure inherently adds value in this domain.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The constructed graphs from raw omics data capture biologically meaningful relationships that are relevant to the downstream prediction tasks, and the chosen datasets and tasks are representative of real omics applications.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"OgBench benchmarks GNNs on omics graphs in the n << p regime and reports that standard GNNs often fail to beat MLPs and classical baselines.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Graph neural networks often fail to outperform simple MLPs on omics data tasks with few samples and many nodes.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"7d9afa59e23bcceb8bbd89e3d0ce22d497cf1f5b04ca24597d978e9a68da4563"},"source":{"id":"2605.15511","kind":"arxiv","version":1},"verdict":{"id":"7e2af986-5c15-476d-bcc1-ab4eff4b03d5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T15:40:50.935953Z","strongest_claim":"Our results show that widely used GNNs often do not outperform simple MLPs and classical baselines. These findings challenge the prevailing assumption that graph structure inherently adds value in this domain.","one_line_summary":"OgBench benchmarks GNNs on omics graphs in the n << p regime and reports that standard GNNs often fail to beat MLPs and classical baselines.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The constructed graphs from raw omics data capture biologically meaningful relationships that are relevant to the downstream prediction tasks, and the chosen datasets and tasks are representative of real omics applications.","pith_extraction_headline":"Graph neural networks often fail to outperform simple MLPs on omics data tasks with few samples and many nodes."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15511/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T16:01:17.926200Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T15:53:35.116330Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T14:51:55.063055Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.055998Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"shingle_duplication","ran_at":"2026-05-19T13:49:41.848906Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.387835Z","status":"skipped","version":"0.1.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.635293Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"a250ed31a8a59a3b3e6af0586ceb28a904d191373c51f74fc3db9fff8d8a9b0f"},"references":{"count":80,"sample":[{"doi":"","year":2022,"title":"Francis E. Agamah, Jumamurat R. Bayjanov, Anna Niehues, Kelechi F. Njoku, Michelle Skelton, Gaston K. Mazandu, Thomas H. A. Ederveen, Nicola Mulder, Emile R. Chimusa, and Peter A. C. ’t Hoen. Computat","work_id":"074a8343-948d-46d8-abdf-2f4aba6a3970","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Elbashir, and Mohanad Mo- hammed","work_id":"46ce1f36-8754-415f-bef8-caa958bf239a","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2004,"title":"Network biology: understanding the cell’s functional organization.Nature reviews genetics, 5(2):101–113, 2004","work_id":"e481fc7a-aa10-4d3d-bd61-c0f5a5aa7f36","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, and Christopher Morris","work_id":"1d46910e-10e6-40f0-82c2-97401be52a0a","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Bench- mark of filter methods for feature selection in high-dimensional gene expression survival data","work_id":"256d5af5-a47f-4385-856b-dbc20b49110d","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":80,"snapshot_sha256":"9fae64f5da145969904811b07a13ad39e0c6dc0a89dccacc855059ba2a936d96","internal_anchors":1},"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":"7e2af986-5c15-476d-bcc1-ab4eff4b03d5"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EJuEnJ1TZWZFJyXVXlQKI7XTEOf+kIDSuN8rME3bK+RWRSkvUwQHFyps5n1qAd6X2+Qu5mLGk3UuKb8qihEOBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T21:00:28.630669Z"},"content_sha256":"bc8b6ce3b0b283483852484e15308eecd6230b18143285b49815dfdfb30622a6","schema_version":"1.0","event_id":"sha256:bc8b6ce3b0b283483852484e15308eecd6230b18143285b49815dfdfb30622a6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UXKJKG3GJDEY7YBM46U5K7HRIK/bundle.json","state_url":"https://pith.science/pith/UXKJKG3GJDEY7YBM46U5K7HRIK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UXKJKG3GJDEY7YBM46U5K7HRIK/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":"2026-05-28T21:00:28Z","links":{"resolver":"https://pith.science/pith/UXKJKG3GJDEY7YBM46U5K7HRIK","bundle":"https://pith.science/pith/UXKJKG3GJDEY7YBM46U5K7HRIK/bundle.json","state":"https://pith.science/pith/UXKJKG3GJDEY7YBM46U5K7HRIK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UXKJKG3GJDEY7YBM46U5K7HRIK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:UXKJKG3GJDEY7YBM46U5K7HRIK","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":"c643ffad124702754f682bfb927972900c98c8a053cb467674cf77b68c71c771","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T01:12:09Z","title_canon_sha256":"9b61f5031366aff14187d71aa99c2eb8770718f2f89e14640db44fe252f36821"},"schema_version":"1.0","source":{"id":"2605.15511","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15511","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15511v1","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15511","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_12","alias_value":"UXKJKG3GJDEY","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_16","alias_value":"UXKJKG3GJDEY7YBM","created_at":"2026-05-20T00:01:02Z"},{"alias_kind":"pith_short_8","alias_value":"UXKJKG3G","created_at":"2026-05-20T00:01:02Z"}],"graph_snapshots":[{"event_id":"sha256:bc8b6ce3b0b283483852484e15308eecd6230b18143285b49815dfdfb30622a6","target":"graph","created_at":"2026-05-20T00:01:02Z","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":"Our results show that widely used GNNs often do not outperform simple MLPs and classical baselines. These findings challenge the prevailing assumption that graph structure inherently adds value in this domain."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The constructed graphs from raw omics data capture biologically meaningful relationships that are relevant to the downstream prediction tasks, and the chosen datasets and tasks are representative of real omics applications."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"OgBench benchmarks GNNs on omics graphs in the n << p regime and reports that standard GNNs often fail to beat MLPs and classical baselines."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Graph neural networks often fail to outperform simple MLPs on omics data tasks with few samples and many nodes."}],"snapshot_sha256":"7d9afa59e23bcceb8bbd89e3d0ce22d497cf1f5b04ca24597d978e9a68da4563"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T16:01:17.926200Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T15:53:35.116330Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"cited_work_retraction","ran_at":"2026-05-19T14:51:55.063055Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.055998Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"shingle_duplication","ran_at":"2026-05-19T13:49:41.848906Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"citation_quote_validity","ran_at":"2026-05-19T13:49:41.387835Z","status":"skipped","version":"0.1.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.635293Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15511/integrity.json","findings":[],"snapshot_sha256":"a250ed31a8a59a3b3e6af0586ceb28a904d191373c51f74fc3db9fff8d8a9b0f","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning. Yet most benchmarks focus on the regime $n \\gg p$, where the number of graphs $n$ greatly exceeds the number of nodes per graph $p$. This overlooks biological domains such as omics, which operate in the opposite $n \\ll p$ regime, characterized by large graphs of genes, transcripts, or proteins across few patient samples. This raises the question: \\textit{how do GNNs perform in this low-sample, high-node omics setting?} We introduce \\texttt{OgBench} (Omics-Graph Bench), the first benchmarking pla","authors_text":"Guillermo Bern\\'ardez, Johan Mathe, Louisa Cornelis, Louis Van Langendonck, Nina Miolane","cross_cats":[],"headline":"Graph neural networks often fail to outperform simple MLPs on omics data tasks with few samples and many nodes.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T01:12:09Z","title":"OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data"},"references":{"count":80,"internal_anchors":1,"resolved_work":80,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Francis E. Agamah, Jumamurat R. Bayjanov, Anna Niehues, Kelechi F. Njoku, Michelle Skelton, Gaston K. Mazandu, Thomas H. A. Ederveen, Nicola Mulder, Emile R. Chimusa, and Peter A. C. ’t Hoen. Computat","work_id":"074a8343-948d-46d8-abdf-2f4aba6a3970","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Elbashir, and Mohanad Mo- hammed","work_id":"46ce1f36-8754-415f-bef8-caa958bf239a","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Network biology: understanding the cell’s functional organization.Nature reviews genetics, 5(2):101–113, 2004","work_id":"e481fc7a-aa10-4d3d-bd61-c0f5a5aa7f36","year":2004},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, and Christopher Morris","work_id":"1d46910e-10e6-40f0-82c2-97401be52a0a","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Bench- mark of filter methods for feature selection in high-dimensional gene expression survival data","work_id":"256d5af5-a47f-4385-856b-dbc20b49110d","year":2021}],"snapshot_sha256":"9fae64f5da145969904811b07a13ad39e0c6dc0a89dccacc855059ba2a936d96"},"source":{"id":"2605.15511","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T15:40:50.935953Z","id":"7e2af986-5c15-476d-bcc1-ab4eff4b03d5","model_set":{"reader":"grok-4.3"},"one_line_summary":"OgBench benchmarks GNNs on omics graphs in the n << p regime and reports that standard GNNs often fail to beat MLPs and classical baselines.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Graph neural networks often fail to outperform simple MLPs on omics data tasks with few samples and many nodes.","strongest_claim":"Our results show that widely used GNNs often do not outperform simple MLPs and classical baselines. These findings challenge the prevailing assumption that graph structure inherently adds value in this domain.","weakest_assumption":"The constructed graphs from raw omics data capture biologically meaningful relationships that are relevant to the downstream prediction tasks, and the chosen datasets and tasks are representative of real omics applications."}},"verdict_id":"7e2af986-5c15-476d-bcc1-ab4eff4b03d5"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9953fe39dd145e33634c90ebd7652c636e532de695fec1895e7f79bf86005473","target":"record","created_at":"2026-05-20T00:01:02Z","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":"c643ffad124702754f682bfb927972900c98c8a053cb467674cf77b68c71c771","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T01:12:09Z","title_canon_sha256":"9b61f5031366aff14187d71aa99c2eb8770718f2f89e14640db44fe252f36821"},"schema_version":"1.0","source":{"id":"2605.15511","kind":"arxiv","version":1}},"canonical_sha256":"a5d4951b6648c98fe02ce7a9d57cf142a8604759543f7b80f12e1f2db382f659","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a5d4951b6648c98fe02ce7a9d57cf142a8604759543f7b80f12e1f2db382f659","first_computed_at":"2026-05-20T00:01:02.390963Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:02.390963Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cME0yrptYJ78LlX8DjwC3hXMQap/fHbtX9I2IDABG4j0QHW+o7Q/gP9gxoczXQX34/P9KdjXBfyQa3CMXMcVBA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:02.391861Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15511","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9953fe39dd145e33634c90ebd7652c636e532de695fec1895e7f79bf86005473","sha256:bc8b6ce3b0b283483852484e15308eecd6230b18143285b49815dfdfb30622a6"],"state_sha256":"43884999985565188e0af4d0d54f160e41caf7f179bc192b7b6da17cd74bfc0a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8HwVq6Pcw74Ia732O6W8qOQ3YmTCTRoA+/8COtl56bQKti+6Rgqd20lIyvINDcI+fAQ+H1/rLbwliVAVzrShCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T21:00:28.633910Z","bundle_sha256":"9568304d220a1544ef5978db08de057ad9510d16c5b3a187243903da995953e6"}}