{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:TFE67VM32IMOJC2JF3LBMDWZR7","short_pith_number":"pith:TFE67VM3","canonical_record":{"source":{"id":"2605.14026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-13T18:38:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c31eda74cff35518c2b3c575cdccd4f6365eedf9fe3f61f218166ccb8bddbe9f","abstract_canon_sha256":"1df157a638430dd2d94906311f82297777152af0776ee975bb312b5ac726780d"},"schema_version":"1.0"},"canonical_sha256":"9949efd59bd218e48b492ed6160ed98fd96894e604f503eaf748c8584a1bbea3","source":{"kind":"arxiv","id":"2605.14026","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14026","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14026v1","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14026","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"pith_short_12","alias_value":"TFE67VM32IMO","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"TFE67VM32IMOJC2J","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"TFE67VM3","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:TFE67VM32IMOJC2JF3LBMDWZR7","target":"record","payload":{"canonical_record":{"source":{"id":"2605.14026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-13T18:38:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c31eda74cff35518c2b3c575cdccd4f6365eedf9fe3f61f218166ccb8bddbe9f","abstract_canon_sha256":"1df157a638430dd2d94906311f82297777152af0776ee975bb312b5ac726780d"},"schema_version":"1.0"},"canonical_sha256":"9949efd59bd218e48b492ed6160ed98fd96894e604f503eaf748c8584a1bbea3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:12.892621Z","signature_b64":"Yn/QS+kJNYqN21dj/aQkFilXpqY201puZw29xT8qmNTwBgyr3wK+G2pdJaQ3iIxWg79xnPv+N2I28Z7d940+BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9949efd59bd218e48b492ed6160ed98fd96894e604f503eaf748c8584a1bbea3","last_reissued_at":"2026-05-17T23:39:12.892050Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:12.892050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.14026","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":"2026-05-17T23:39:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"loghsn0cE+jkVJYzBAkTCGLLnOoYFmUFmYQpL80Oj8xFY9dB+zS/QHxUNcG58HiTG2lRioyh1oXvsRBa3cpgCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:52:58.049956Z"},"content_sha256":"5bedc3ce6b2e3c6029d398354f42d32e29efd4f98f2ae8876cc65e49a404b5d7","schema_version":"1.0","event_id":"sha256:5bedc3ce6b2e3c6029d398354f42d32e29efd4f98f2ae8876cc65e49a404b5d7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:TFE67VM32IMOJC2JF3LBMDWZR7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A non-centered objective in self-predictive learning resolves zero-centering conflicts to stabilize representations under intensive experience reuse.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Donghyeok Lee, Jinsik Kim, Sanghyeob Song, Sungroh Yoon","submitted_at":"2026-05-13T18:38:32Z","abstract_excerpt":"For reinforcement learning in data-scarce domains like real-world robotics, intensive data reuse enhances efficiency but induces overfitting. While prior works focus on critic bias, representation-level instability in Self-Predictive Learning (SPL) under high Update-to-Data (UTD) regimes remains underexplored. To bridge this gap, we propose Robust Representation via Redundancy Reduction (R2R2), a regularization method within SPL. We theoretically identify that standard zero-centering conflicts with SPL's spectral properties and design a non-centered objective accordingly. We verify R2R2 on SPL"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"At a UTD ratio of 20, R2R2 improves TD7 by ~22% and provides additional gains on top of SimbaV2-SPL, which itself establishes a new state-of-the-art.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the identified conflict between standard zero-centering and SPL spectral properties is the primary driver of representation instability, and that the proposed non-centered objective directly causes the observed performance gains rather than other unstated experimental factors.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"R2R2 introduces a non-centered regularization objective for SPL that addresses conflicts with spectral properties, leading to better performance on continuous control tasks at high UTD ratios.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A non-centered objective in self-predictive learning resolves zero-centering conflicts to stabilize representations under intensive experience reuse.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"aefa671ad21997ecae1d5539df86117cefa6af205f77dfabfb5f5245541e36fa"},"source":{"id":"2605.14026","kind":"arxiv","version":1},"verdict":{"id":"ab62cd44-d4b5-4592-823c-42989dfcc111","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:36:09.905668Z","strongest_claim":"At a UTD ratio of 20, R2R2 improves TD7 by ~22% and provides additional gains on top of SimbaV2-SPL, which itself establishes a new state-of-the-art.","one_line_summary":"R2R2 introduces a non-centered regularization objective for SPL that addresses conflicts with spectral properties, leading to better performance on continuous control tasks at high UTD ratios.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the identified conflict between standard zero-centering and SPL spectral properties is the primary driver of representation instability, and that the proposed non-centered objective directly causes the observed performance gains rather than other unstated experimental factors.","pith_extraction_headline":"A non-centered objective in self-predictive learning resolves zero-centering conflicts to stabilize representations under intensive experience reuse."},"references":{"count":42,"sample":[{"doi":"","year":2016,"title":"Lillicrap and Jonathan J","work_id":"b2f60bdb-e387-4502-807f-af283c830418","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"9th International Conference on Learning Representations,","work_id":"34df5ed2-87c9-4393-82d1-808761a336ba","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , booktitle =","work_id":"8231c64b-d5a3-4094-b395-b3c5345b3493","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Soft Actor-Critic Algorithms and Applications","work_id":"bb49c9fb-03b2-4226-9edb-50186b8193e4","ref_index":4,"cited_arxiv_id":"1812.05905","is_internal_anchor":true},{"doi":"","year":2025,"title":"Forty-second International Conference on Machine Learning,","work_id":"21e4bb5e-7b70-4c60-a752-713946f24abf","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":42,"snapshot_sha256":"879998858db84e2a47281d2dd79b10fe33f0a2bf720aa0e8a3e855e154720c5b","internal_anchors":5},"formal_canon":{"evidence_count":2,"snapshot_sha256":"05a0143671e977dce7717ccb14ecaad60de682e70912978fa40f6364ccf297e5"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"ab62cd44-d4b5-4592-823c-42989dfcc111"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FBJYSkc5eTNAXIXF/seVHQd2t4vPE7sJ2JFbXpnRWV2m5XRX4ysCafPe9wGVsYzCpksT+csgp/JmaICEJm+hCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:52:58.050969Z"},"content_sha256":"b9055742c718f1983b0c3b6b2124107e6104466d8bd74cb168d2925250a7f736","schema_version":"1.0","event_id":"sha256:b9055742c718f1983b0c3b6b2124107e6104466d8bd74cb168d2925250a7f736"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:TFE67VM32IMOJC2JF3LBMDWZR7","target":"integrity","payload":{"note":"Identifier '10.5555/2627435.2670313' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Nitish Srivastava and Geoffrey E. Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov , title =. J. Mach. Learn. Res. , volume =. 2014 , url =. doi:10.5555/2627435.2670313 , timestamp =","arxiv_id":"2605.14026","detector":"doi_compliance","evidence":{"doi":"10.5555/2627435.2670313","arxiv_id":null,"ref_index":37,"raw_excerpt":"Nitish Srivastava and Geoffrey E. Hinton and Alex Krizhevsky and Ilya Sutskever and Ruslan Salakhutdinov , title =. J. Mach. Learn. Res. , volume =. 2014 , url =. doi:10.5555/2627435.2670313 , timestamp =","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":37,"audited_at":"2026-05-19T05:59:04.250026Z","event_type":"pith.integrity.v1","detected_doi":"10.5555/2627435.2670313","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"db898189e990814e640598829ab8604f58dd93c1de05bcdfff491e6ae4224f9e","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":81,"payload_sha256":"e0fb65baadac64bbe0a03ae0948815ce7db8cc869bb647cda522c95a1840b319","signature_b64":"JRcumiK6Kjjy2AsMucRmBmGECi/Mw/oV0L6IG4X7gHEcd8IwkL0fDX4bSwjPBbLenmtCd0+yzn1Wa9HMsW0/Bw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-19T06:01:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OM67bf+GJ3im9+ip95ixlHUnwXWdSnKa1riIfj0rMWt/CtyoQYD8DkllxSXUWQ6MRt8L8om4TaZ6FXxInGyYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T14:52:58.052460Z"},"content_sha256":"edd72543fa695b80f9c7c0f21bdfd9f610358bfb00c4de698be33ddf0219a262","schema_version":"1.0","event_id":"sha256:edd72543fa695b80f9c7c0f21bdfd9f610358bfb00c4de698be33ddf0219a262"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TFE67VM32IMOJC2JF3LBMDWZR7/bundle.json","state_url":"https://pith.science/pith/TFE67VM32IMOJC2JF3LBMDWZR7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TFE67VM32IMOJC2JF3LBMDWZR7/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":"2026-05-21T14:52:58Z","links":{"resolver":"https://pith.science/pith/TFE67VM32IMOJC2JF3LBMDWZR7","bundle":"https://pith.science/pith/TFE67VM32IMOJC2JF3LBMDWZR7/bundle.json","state":"https://pith.science/pith/TFE67VM32IMOJC2JF3LBMDWZR7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TFE67VM32IMOJC2JF3LBMDWZR7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:TFE67VM32IMOJC2JF3LBMDWZR7","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1df157a638430dd2d94906311f82297777152af0776ee975bb312b5ac726780d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-13T18:38:32Z","title_canon_sha256":"c31eda74cff35518c2b3c575cdccd4f6365eedf9fe3f61f218166ccb8bddbe9f"},"schema_version":"1.0","source":{"id":"2605.14026","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14026","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14026v1","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14026","created_at":"2026-05-17T23:39:12Z"},{"alias_kind":"pith_short_12","alias_value":"TFE67VM32IMO","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"TFE67VM32IMOJC2J","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"TFE67VM3","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:b9055742c718f1983b0c3b6b2124107e6104466d8bd74cb168d2925250a7f736","target":"graph","created_at":"2026-05-17T23:39:12Z","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":"At a UTD ratio of 20, R2R2 improves TD7 by ~22% and provides additional gains on top of SimbaV2-SPL, which itself establishes a new state-of-the-art."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the identified conflict between standard zero-centering and SPL spectral properties is the primary driver of representation instability, and that the proposed non-centered objective directly causes the observed performance gains rather than other unstated experimental factors."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"R2R2 introduces a non-centered regularization objective for SPL that addresses conflicts with spectral properties, leading to better performance on continuous control tasks at high UTD ratios."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A non-centered objective in self-predictive learning resolves zero-centering conflicts to stabilize representations under intensive experience reuse."}],"snapshot_sha256":"aefa671ad21997ecae1d5539df86117cefa6af205f77dfabfb5f5245541e36fa"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"05a0143671e977dce7717ccb14ecaad60de682e70912978fa40f6364ccf297e5"},"paper":{"abstract_excerpt":"For reinforcement learning in data-scarce domains like real-world robotics, intensive data reuse enhances efficiency but induces overfitting. While prior works focus on critic bias, representation-level instability in Self-Predictive Learning (SPL) under high Update-to-Data (UTD) regimes remains underexplored. To bridge this gap, we propose Robust Representation via Redundancy Reduction (R2R2), a regularization method within SPL. We theoretically identify that standard zero-centering conflicts with SPL's spectral properties and design a non-centered objective accordingly. We verify R2R2 on SPL","authors_text":"Donghyeok Lee, Jinsik Kim, Sanghyeob Song, Sungroh Yoon","cross_cats":["cs.AI"],"headline":"A non-centered objective in self-predictive learning resolves zero-centering conflicts to stabilize representations under intensive experience reuse.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-13T18:38:32Z","title":"R2R2: Robust Representation for Intensive Experience Reuse via Redundancy Reduction in Self-Predictive Learning"},"references":{"count":42,"internal_anchors":5,"resolved_work":42,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Lillicrap and Jonathan J","work_id":"b2f60bdb-e387-4502-807f-af283c830418","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"9th International Conference on Learning Representations,","work_id":"34df5ed2-87c9-4393-82d1-808761a336ba","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor , booktitle =","work_id":"8231c64b-d5a3-4094-b395-b3c5345b3493","year":2018},{"cited_arxiv_id":"1812.05905","doi":"","is_internal_anchor":true,"ref_index":4,"title":"Soft Actor-Critic Algorithms and Applications","work_id":"bb49c9fb-03b2-4226-9edb-50186b8193e4","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Forty-second International Conference on Machine Learning,","work_id":"21e4bb5e-7b70-4c60-a752-713946f24abf","year":2025}],"snapshot_sha256":"879998858db84e2a47281d2dd79b10fe33f0a2bf720aa0e8a3e855e154720c5b"},"source":{"id":"2605.14026","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T05:36:09.905668Z","id":"ab62cd44-d4b5-4592-823c-42989dfcc111","model_set":{"reader":"grok-4.3"},"one_line_summary":"R2R2 introduces a non-centered regularization objective for SPL that addresses conflicts with spectral properties, leading to better performance on continuous control tasks at high UTD ratios.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A non-centered objective in self-predictive learning resolves zero-centering conflicts to stabilize representations under intensive experience reuse.","strongest_claim":"At a UTD ratio of 20, R2R2 improves TD7 by ~22% and provides additional gains on top of SimbaV2-SPL, which itself establishes a new state-of-the-art.","weakest_assumption":"That the identified conflict between standard zero-centering and SPL spectral properties is the primary driver of representation instability, and that the proposed non-centered objective directly causes the observed performance gains rather than other unstated experimental factors."}},"verdict_id":"ab62cd44-d4b5-4592-823c-42989dfcc111"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5bedc3ce6b2e3c6029d398354f42d32e29efd4f98f2ae8876cc65e49a404b5d7","target":"record","created_at":"2026-05-17T23:39:12Z","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":"1df157a638430dd2d94906311f82297777152af0776ee975bb312b5ac726780d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-13T18:38:32Z","title_canon_sha256":"c31eda74cff35518c2b3c575cdccd4f6365eedf9fe3f61f218166ccb8bddbe9f"},"schema_version":"1.0","source":{"id":"2605.14026","kind":"arxiv","version":1}},"canonical_sha256":"9949efd59bd218e48b492ed6160ed98fd96894e604f503eaf748c8584a1bbea3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9949efd59bd218e48b492ed6160ed98fd96894e604f503eaf748c8584a1bbea3","first_computed_at":"2026-05-17T23:39:12.892050Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:12.892050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Yn/QS+kJNYqN21dj/aQkFilXpqY201puZw29xT8qmNTwBgyr3wK+G2pdJaQ3iIxWg79xnPv+N2I28Z7d940+BQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:12.892621Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14026","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5bedc3ce6b2e3c6029d398354f42d32e29efd4f98f2ae8876cc65e49a404b5d7","sha256:b9055742c718f1983b0c3b6b2124107e6104466d8bd74cb168d2925250a7f736","sha256:edd72543fa695b80f9c7c0f21bdfd9f610358bfb00c4de698be33ddf0219a262"],"state_sha256":"747487c3b5156a47a1d1a7d1e97dc9602dd6960c0b3a5b0d281247c5e2a7b197"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PSDV7XOXnDJhfhQ/p+g7/e3U0MGTT0PLFKP3BUsRVDOZVjPuiR/Eu/Jn2eMSh4WBk+lFGx3APBrsqLW7iDSBAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T14:52:58.056581Z","bundle_sha256":"023ee04d3bb2c370f4aacb6a0361cc1ed2885753f0f587c5cc5b6926bea939bc"}}