{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ","short_pith_number":"pith:H5DZGZZ6","canonical_record":{"source":{"id":"2501.13273","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-22T23:32:19Z","cross_cats_sorted":[],"title_canon_sha256":"8a08dda531644b5a5c6b2f9a2f07e8b79877601ca757aa2f2b8fa1d96e654038","abstract_canon_sha256":"23c99ea1bd265ccbd375305d260a780f33e107d8f26f7b171b1dae517be022e7"},"schema_version":"1.0"},"canonical_sha256":"3f4793673e84add87b30b3f62ab8959c0cefbfdcbb9f148aab2890290044f71f","source":{"kind":"arxiv","id":"2501.13273","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13273","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13273v1","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13273","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"H5DZGZZ6QSW5","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"pith_short_16","alias_value":"H5DZGZZ6QSW5Q6ZQ","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"pith_short_8","alias_value":"H5DZGZZ6","created_at":"2026-07-05T10:04:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ","target":"record","payload":{"canonical_record":{"source":{"id":"2501.13273","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-22T23:32:19Z","cross_cats_sorted":[],"title_canon_sha256":"8a08dda531644b5a5c6b2f9a2f07e8b79877601ca757aa2f2b8fa1d96e654038","abstract_canon_sha256":"23c99ea1bd265ccbd375305d260a780f33e107d8f26f7b171b1dae517be022e7"},"schema_version":"1.0"},"canonical_sha256":"3f4793673e84add87b30b3f62ab8959c0cefbfdcbb9f148aab2890290044f71f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:04:26.023001Z","signature_b64":"rquVwP8JRPnm2DasgtNhpAK765SrOIcZ+pRgu4hzyN+noKEF6MByc7AhQ5cAuZx0mrsyUT/r7Q7DxyiSd0FNDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3f4793673e84add87b30b3f62ab8959c0cefbfdcbb9f148aab2890290044f71f","last_reissued_at":"2026-07-05T10:04:26.022595Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:04:26.022595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.13273","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":"2026-07-05T10:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JKztKYFT7TNGPXSVL8s4AlKDjS4jcINT74VfXp0usf0w99R3gF5TDJPqysZhAm4/ZIC1RBcj0rSjiHl+OCuCBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T19:03:34.749769Z"},"content_sha256":"4a46c72da28ea3665689c803427d261a110a9b9d8f711e437fc3fe8d201a4122","schema_version":"1.0","event_id":"sha256:4a46c72da28ea3665689c803427d261a110a9b9d8f711e437fc3fe8d201a4122"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Robust Fairness via Confusional Spectral Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Gaojie Jin, Jiaxu Liu, Ronghui Mu, Sihao Wu, Tianjin Huang","submitted_at":"2025-01-22T23:32:19Z","abstract_excerpt":"Recent research has highlighted a critical issue known as ``robust fairness\", where robust accuracy varies significantly across different classes, undermining the reliability of deep neural networks (DNNs). A common approach to address this has been to dynamically reweight classes during training, giving more weight to those with lower empirical robust performance. However, we find there is a divergence of class-wise robust performance between training set and testing set, which limits the effectiveness of these explicit reweighting methods, indicating the need for a principled alternative. In"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13273","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.13273/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:04:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1rrDyK0z5NKuwV3paPyGGEU8g8oFznWoka4C3ERRC/iTd8pVHx2P0wWIAD2MhcA5YqV/UW71bDXgNfB2E4FGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T19:03:34.750748Z"},"content_sha256":"50b083869470df4830fd3035f7a7e889553684f7842af1d8f85764df009905a7","schema_version":"1.0","event_id":"sha256:50b083869470df4830fd3035f7a7e889553684f7842af1d8f85764df009905a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ/bundle.json","state_url":"https://pith.science/pith/H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-15T19:03:34Z","links":{"resolver":"https://pith.science/pith/H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ","bundle":"https://pith.science/pith/H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ/bundle.json","state":"https://pith.science/pith/H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:H5DZGZZ6QSW5Q6ZQWP3CVOEVTQ","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":"23c99ea1bd265ccbd375305d260a780f33e107d8f26f7b171b1dae517be022e7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-22T23:32:19Z","title_canon_sha256":"8a08dda531644b5a5c6b2f9a2f07e8b79877601ca757aa2f2b8fa1d96e654038"},"schema_version":"1.0","source":{"id":"2501.13273","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13273","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13273v1","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13273","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"pith_short_12","alias_value":"H5DZGZZ6QSW5","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"pith_short_16","alias_value":"H5DZGZZ6QSW5Q6ZQ","created_at":"2026-07-05T10:04:26Z"},{"alias_kind":"pith_short_8","alias_value":"H5DZGZZ6","created_at":"2026-07-05T10:04:26Z"}],"graph_snapshots":[{"event_id":"sha256:50b083869470df4830fd3035f7a7e889553684f7842af1d8f85764df009905a7","target":"graph","created_at":"2026-07-05T10:04:26Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.13273/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent research has highlighted a critical issue known as ``robust fairness\", where robust accuracy varies significantly across different classes, undermining the reliability of deep neural networks (DNNs). A common approach to address this has been to dynamically reweight classes during training, giving more weight to those with lower empirical robust performance. However, we find there is a divergence of class-wise robust performance between training set and testing set, which limits the effectiveness of these explicit reweighting methods, indicating the need for a principled alternative. In","authors_text":"Gaojie Jin, Jiaxu Liu, Ronghui Mu, Sihao Wu, Tianjin Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-22T23:32:19Z","title":"Enhancing Robust Fairness via Confusional Spectral Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13273","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4a46c72da28ea3665689c803427d261a110a9b9d8f711e437fc3fe8d201a4122","target":"record","created_at":"2026-07-05T10:04:26Z","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":"23c99ea1bd265ccbd375305d260a780f33e107d8f26f7b171b1dae517be022e7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-22T23:32:19Z","title_canon_sha256":"8a08dda531644b5a5c6b2f9a2f07e8b79877601ca757aa2f2b8fa1d96e654038"},"schema_version":"1.0","source":{"id":"2501.13273","kind":"arxiv","version":1}},"canonical_sha256":"3f4793673e84add87b30b3f62ab8959c0cefbfdcbb9f148aab2890290044f71f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3f4793673e84add87b30b3f62ab8959c0cefbfdcbb9f148aab2890290044f71f","first_computed_at":"2026-07-05T10:04:26.022595Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:04:26.022595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rquVwP8JRPnm2DasgtNhpAK765SrOIcZ+pRgu4hzyN+noKEF6MByc7AhQ5cAuZx0mrsyUT/r7Q7DxyiSd0FNDA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:04:26.023001Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.13273","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4a46c72da28ea3665689c803427d261a110a9b9d8f711e437fc3fe8d201a4122","sha256:50b083869470df4830fd3035f7a7e889553684f7842af1d8f85764df009905a7"],"state_sha256":"b4c3b40f9e8dc75949fa522145fa7c697cc0e654d71dda78a3e38b2372ee25fa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bsht6iJcPfELUr9OzGV/2jIgsjcYc9g1VhFUHo9GdALkDfGZ9XbtF2vNE6tIPkE9+a2fnpcS1t2Oaft8mSqVCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T19:03:34.764211Z","bundle_sha256":"5d8e0dca55081748ba1ff7b019727ac569341b49196eb5d355d3fd502dce05d8"}}