{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6GXPO7TYHXCQJ3FYDSZC2RYRE5","short_pith_number":"pith:6GXPO7TY","canonical_record":{"source":{"id":"2605.15328","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:41:20Z","cross_cats_sorted":[],"title_canon_sha256":"f65c768779d6cb738d715e32f5197ef56db657971b418f670f3d5743873ce9ed","abstract_canon_sha256":"8affd006240198081e9e1e873defc1f774e44d8aa21a11cc40a8d8c39394603b"},"schema_version":"1.0"},"canonical_sha256":"f1aef77e783dc504ecb81cb22d471127656d161b32e8c45d1571795dc54fe012","source":{"kind":"arxiv","id":"2605.15328","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15328","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15328v1","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15328","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"pith_short_12","alias_value":"6GXPO7TYHXCQ","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"pith_short_16","alias_value":"6GXPO7TYHXCQJ3FY","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"pith_short_8","alias_value":"6GXPO7TY","created_at":"2026-05-20T00:00:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6GXPO7TYHXCQJ3FYDSZC2RYRE5","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15328","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:41:20Z","cross_cats_sorted":[],"title_canon_sha256":"f65c768779d6cb738d715e32f5197ef56db657971b418f670f3d5743873ce9ed","abstract_canon_sha256":"8affd006240198081e9e1e873defc1f774e44d8aa21a11cc40a8d8c39394603b"},"schema_version":"1.0"},"canonical_sha256":"f1aef77e783dc504ecb81cb22d471127656d161b32e8c45d1571795dc54fe012","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:00:52.864065Z","signature_b64":"ou/4W2339unWjg6LHW3tdqA6AYlmdE+0Jc05vc6ancVrEozWGDfD7Jg5/dDf2/NN1SAC+ND/lh0Y1du/Mc+EAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1aef77e783dc504ecb81cb22d471127656d161b32e8c45d1571795dc54fe012","last_reissued_at":"2026-05-20T00:00:52.863358Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:00:52.863358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15328","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":"2026-05-20T00:00:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yOmuzWau3HyA0IAhddCYI+gzwFX8BitUp1SLuOrg4tuObrVRTddJr9nvTdRVHvCO6M9tPpgqACMfx9fNUvT8Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T21:13:18.068561Z"},"content_sha256":"ea473b6b3c1440dc4bc410f71925c577edfdf1b18bbefe45e3c9286de357c7d0","schema_version":"1.0","event_id":"sha256:ea473b6b3c1440dc4bc410f71925c577edfdf1b18bbefe45e3c9286de357c7d0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6GXPO7TYHXCQJ3FYDSZC2RYRE5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"Perturbing weights attached to input features produces reliable attributions that avoid bias and out-of-distribution problems in occlusion methods for fully connected neural networks.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Denia Kanellopoulou, Thodoris Lymperopoulos","submitted_at":"2026-05-14T18:41:20Z","abstract_excerpt":"Fully Connected Neural Networks (FCNNs) are often regarded as simple and intuitive architectures, yet they serve as the foundation for more complex models. Nonetheless, the lack of consensus on their interpretability continues to pose challenges, underscoring the enduring relevance of simpler, attribution-based approaches for understanding even the most advanced neural architectures. In this regard, we explore a novel idea for estimating feature attribution, by applying perturbation to the features' attached weights instead of their values. This method offers a fresh perspective aimed at mitig"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Applying perturbation to the features' attached weights instead of their values leads to novel attribution methods XWP and XWP_c that mitigate common limitations in Occlusion techniques such as Added Bias and Out-of-Distribution data and achieve competitive performance in identifying image signals for simple DNNs on standard baseline metrics.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That perturbing weights attached to features produces a valid and unbiased measure of feature importance that directly addresses the added bias and out-of-distribution problems of value perturbation without introducing new artifacts or requiring additional validation on the specific network architecture.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Perturbing weights attached to input features produces reliable attributions that avoid bias and out-of-distribution problems in occlusion methods for fully connected neural networks.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d637d37ae6d2351bdad6cfc836ff772eba0258d1ec8fb5939848297b44bd5113"},"source":{"id":"2605.15328","kind":"arxiv","version":1},"verdict":{"id":"cfe6eeef-af70-4bfc-acc0-501c7ebf7770","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T16:01:48.329692Z","strongest_claim":"Applying perturbation to the features' attached weights instead of their values leads to novel attribution methods XWP and XWP_c that mitigate common limitations in Occlusion techniques such as Added Bias and Out-of-Distribution data and achieve competitive performance in identifying image signals for simple DNNs on standard baseline metrics.","one_line_summary":"XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That perturbing weights attached to features produces a valid and unbiased measure of feature importance that directly addresses the added bias and out-of-distribution problems of value perturbation without introducing new artifacts or requiring additional validation on the specific network architecture.","pith_extraction_headline":"Perturbing weights attached to input features produces reliable attributions that avoid bias and out-of-distribution problems in occlusion methods for fully connected neural networks."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15328/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.292067Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T16:16:01.101312Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:41:54.196931Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.763751Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"e506cf9997bc7b4a7b7f1c15f46b8332695a60f42d2c9d63b58834ee04428354"},"references":{"count":31,"sample":[{"doi":"10.52202/079017-2007","year":2024,"title":"Shreyash Arya, Sukrut Rao, Moritz Böhle, and Bernt Schiele. 2024. B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable. InAdvances in Neural Information Processing Systems,","work_id":"589ef093-0665-4aec-99f1-cd56d8ab729e","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Beyza Nur Aydoğan and Tevfik Aytekin. 2025. An in-depth analysis of KernelSHAP and SamplingSHAP: assessing robustness, error, and efficiency. Knowledge and Information Systems67 (2025), 10545 – 10579.","work_id":"0f1fe993-e813-41a4-8864-48d597b0b920","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1016/j.inffus.2019.12.012","year":2020,"title":"(2020).Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI","work_id":"d47d4c0c-7f58-4955-b6da-f2a5940ba35c","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"2024 , month = aug, number =","work_id":"45111204-32e8-4772-9cb5-b333c52c39e3","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"Alexander Binder, Sebastian Bach, Gregoire Montavon, Klaus-Robert Muller, and Wojciech Samek. 2016. Layer-Wise Relevance Propagation for Deep Neural Network Architectures. InInformation Science and Ap","work_id":"846eba53-8385-4a62-bd03-b830b05e8a5b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":31,"snapshot_sha256":"23d43c0fcdd213e81cc0bbe79dcf01d617fbda40021a791025a2765422e8a668","internal_anchors":4},"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":"cfe6eeef-af70-4bfc-acc0-501c7ebf7770"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:00:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ssTfJgRqtvAhiM/Az5cs5LIL+G3w/qO7qlNX/Nizdz/5/uecU18L7C7VJ9yq41zEnJT+uoQpjW/rzk7yFu1RCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T21:13:18.069323Z"},"content_sha256":"f5f8c9c37a7954e26f4fe8be5d29316b2e0a2e1496a1a4dd30ee0a3d78a7e6f2","schema_version":"1.0","event_id":"sha256:f5f8c9c37a7954e26f4fe8be5d29316b2e0a2e1496a1a4dd30ee0a3d78a7e6f2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6GXPO7TYHXCQJ3FYDSZC2RYRE5/bundle.json","state_url":"https://pith.science/pith/6GXPO7TYHXCQJ3FYDSZC2RYRE5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6GXPO7TYHXCQJ3FYDSZC2RYRE5/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":"2026-05-20T21:13:18Z","links":{"resolver":"https://pith.science/pith/6GXPO7TYHXCQJ3FYDSZC2RYRE5","bundle":"https://pith.science/pith/6GXPO7TYHXCQJ3FYDSZC2RYRE5/bundle.json","state":"https://pith.science/pith/6GXPO7TYHXCQJ3FYDSZC2RYRE5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6GXPO7TYHXCQJ3FYDSZC2RYRE5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6GXPO7TYHXCQJ3FYDSZC2RYRE5","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":"8affd006240198081e9e1e873defc1f774e44d8aa21a11cc40a8d8c39394603b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:41:20Z","title_canon_sha256":"f65c768779d6cb738d715e32f5197ef56db657971b418f670f3d5743873ce9ed"},"schema_version":"1.0","source":{"id":"2605.15328","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15328","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15328v1","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15328","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"pith_short_12","alias_value":"6GXPO7TYHXCQ","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"pith_short_16","alias_value":"6GXPO7TYHXCQJ3FY","created_at":"2026-05-20T00:00:52Z"},{"alias_kind":"pith_short_8","alias_value":"6GXPO7TY","created_at":"2026-05-20T00:00:52Z"}],"graph_snapshots":[{"event_id":"sha256:f5f8c9c37a7954e26f4fe8be5d29316b2e0a2e1496a1a4dd30ee0a3d78a7e6f2","target":"graph","created_at":"2026-05-20T00:00:52Z","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":"Applying perturbation to the features' attached weights instead of their values leads to novel attribution methods XWP and XWP_c that mitigate common limitations in Occlusion techniques such as Added Bias and Out-of-Distribution data and achieve competitive performance in identifying image signals for simple DNNs on standard baseline metrics."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That perturbing weights attached to features produces a valid and unbiased measure of feature importance that directly addresses the added bias and out-of-distribution problems of value perturbation without introducing new artifacts or requiring additional validation on the specific network architecture."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Perturbing weights attached to input features produces reliable attributions that avoid bias and out-of-distribution problems in occlusion methods for fully connected neural networks."}],"snapshot_sha256":"d637d37ae6d2351bdad6cfc836ff772eba0258d1ec8fb5939848297b44bd5113"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.292067Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T16:16:01.101312Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T14:41:54.196931Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.763751Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.15328/integrity.json","findings":[],"snapshot_sha256":"e506cf9997bc7b4a7b7f1c15f46b8332695a60f42d2c9d63b58834ee04428354","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fully Connected Neural Networks (FCNNs) are often regarded as simple and intuitive architectures, yet they serve as the foundation for more complex models. Nonetheless, the lack of consensus on their interpretability continues to pose challenges, underscoring the enduring relevance of simpler, attribution-based approaches for understanding even the most advanced neural architectures. In this regard, we explore a novel idea for estimating feature attribution, by applying perturbation to the features' attached weights instead of their values. This method offers a fresh perspective aimed at mitig","authors_text":"Denia Kanellopoulou, Thodoris Lymperopoulos","cross_cats":[],"headline":"Perturbing weights attached to input features produces reliable attributions that avoid bias and out-of-distribution problems in occlusion methods for fully connected neural networks.","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:41:20Z","title":"From Weight Perturbation to Feature Attribution for Explaining Fully Connected Neural Networks"},"references":{"count":31,"internal_anchors":4,"resolved_work":31,"sample":[{"cited_arxiv_id":"","doi":"10.52202/079017-2007","is_internal_anchor":false,"ref_index":1,"title":"Shreyash Arya, Sukrut Rao, Moritz Böhle, and Bernt Schiele. 2024. B-cosification: Transforming Deep Neural Networks to be Inherently Interpretable. InAdvances in Neural Information Processing Systems,","work_id":"589ef093-0665-4aec-99f1-cd56d8ab729e","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Beyza Nur Aydoğan and Tevfik Aytekin. 2025. An in-depth analysis of KernelSHAP and SamplingSHAP: assessing robustness, error, and efficiency. Knowledge and Information Systems67 (2025), 10545 – 10579.","work_id":"0f1fe993-e813-41a4-8864-48d597b0b920","year":2025},{"cited_arxiv_id":"","doi":"10.1016/j.inffus.2019.12.012","is_internal_anchor":false,"ref_index":3,"title":"(2020).Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI","work_id":"d47d4c0c-7f58-4955-b6da-f2a5940ba35c","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"2024 , month = aug, number =","work_id":"45111204-32e8-4772-9cb5-b333c52c39e3","year":2024},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Alexander Binder, Sebastian Bach, Gregoire Montavon, Klaus-Robert Muller, and Wojciech Samek. 2016. Layer-Wise Relevance Propagation for Deep Neural Network Architectures. InInformation Science and Ap","work_id":"846eba53-8385-4a62-bd03-b830b05e8a5b","year":2016}],"snapshot_sha256":"23d43c0fcdd213e81cc0bbe79dcf01d617fbda40021a791025a2765422e8a668"},"source":{"id":"2605.15328","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T16:01:48.329692Z","id":"cfe6eeef-af70-4bfc-acc0-501c7ebf7770","model_set":{"reader":"grok-4.3"},"one_line_summary":"XWP and XWP_c are novel attribution methods for FCNNs that estimate feature importance by perturbing attached weights to avoid added bias and out-of-distribution issues in occlusion approaches.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Perturbing weights attached to input features produces reliable attributions that avoid bias and out-of-distribution problems in occlusion methods for fully connected neural networks.","strongest_claim":"Applying perturbation to the features' attached weights instead of their values leads to novel attribution methods XWP and XWP_c that mitigate common limitations in Occlusion techniques such as Added Bias and Out-of-Distribution data and achieve competitive performance in identifying image signals for simple DNNs on standard baseline metrics.","weakest_assumption":"That perturbing weights attached to features produces a valid and unbiased measure of feature importance that directly addresses the added bias and out-of-distribution problems of value perturbation without introducing new artifacts or requiring additional validation on the specific network architecture."}},"verdict_id":"cfe6eeef-af70-4bfc-acc0-501c7ebf7770"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ea473b6b3c1440dc4bc410f71925c577edfdf1b18bbefe45e3c9286de357c7d0","target":"record","created_at":"2026-05-20T00:00:52Z","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":"8affd006240198081e9e1e873defc1f774e44d8aa21a11cc40a8d8c39394603b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-14T18:41:20Z","title_canon_sha256":"f65c768779d6cb738d715e32f5197ef56db657971b418f670f3d5743873ce9ed"},"schema_version":"1.0","source":{"id":"2605.15328","kind":"arxiv","version":1}},"canonical_sha256":"f1aef77e783dc504ecb81cb22d471127656d161b32e8c45d1571795dc54fe012","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1aef77e783dc504ecb81cb22d471127656d161b32e8c45d1571795dc54fe012","first_computed_at":"2026-05-20T00:00:52.863358Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:00:52.863358Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ou/4W2339unWjg6LHW3tdqA6AYlmdE+0Jc05vc6ancVrEozWGDfD7Jg5/dDf2/NN1SAC+ND/lh0Y1du/Mc+EAA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:00:52.864065Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15328","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ea473b6b3c1440dc4bc410f71925c577edfdf1b18bbefe45e3c9286de357c7d0","sha256:f5f8c9c37a7954e26f4fe8be5d29316b2e0a2e1496a1a4dd30ee0a3d78a7e6f2"],"state_sha256":"7cdb69a0bd466965dc36b6f1e988519efbad9d434a3732a15eafdcfab88ed547"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qMmptvq3rhqfzs4D59c7L2iOo7SLIOEt+bpg59xhHup1NZj3PAhlGagW3kUWyW8Ou4/IvS3OI3lPBL7j06A/Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T21:13:18.072117Z","bundle_sha256":"2aa660b74cb3f1c98f06d041dd0fbf36091d6b6d205f90120d4d3a7a512c5cb5"}}