{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WRDQNBLTN2RZAWVZ7XG67PRSS6","short_pith_number":"pith:WRDQNBLT","canonical_record":{"source":{"id":"2606.26973","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T12:45:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d297973d2f3e38dc1dc25e2dc66a0f89f9620c359c64578705bcfaa08ecbb7d1","abstract_canon_sha256":"933c0d59b41029dd32a24c9ba9c1160794c89d7526ea5aef8f1dfc4eae66a923"},"schema_version":"1.0"},"canonical_sha256":"b4470685736ea3905ab9fdcdefbe32979b7809676bc00f40bbcd737ac97159e2","source":{"kind":"arxiv","id":"2606.26973","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.26973","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"arxiv_version","alias_value":"2606.26973v1","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26973","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"pith_short_12","alias_value":"WRDQNBLTN2RZ","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"pith_short_16","alias_value":"WRDQNBLTN2RZAWVZ","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"pith_short_8","alias_value":"WRDQNBLT","created_at":"2026-06-26T01:16:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WRDQNBLTN2RZAWVZ7XG67PRSS6","target":"record","payload":{"canonical_record":{"source":{"id":"2606.26973","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T12:45:42Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d297973d2f3e38dc1dc25e2dc66a0f89f9620c359c64578705bcfaa08ecbb7d1","abstract_canon_sha256":"933c0d59b41029dd32a24c9ba9c1160794c89d7526ea5aef8f1dfc4eae66a923"},"schema_version":"1.0"},"canonical_sha256":"b4470685736ea3905ab9fdcdefbe32979b7809676bc00f40bbcd737ac97159e2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:05.637809Z","signature_b64":"ej5Be9mcUQ6LXJ0naIsY7PD10WBkFjv2IoeH2TMGBpS6LGshBWI6iGZUrHuZEk4a1RXRobtKzspc37zlK807BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4470685736ea3905ab9fdcdefbe32979b7809676bc00f40bbcd737ac97159e2","last_reissued_at":"2026-06-26T01:16:05.637429Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:05.637429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.26973","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":"2026-06-26T01:16:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bur9mz8wrSTqzdf2yCnktXJHpjgrzXzdCsU/r1ylqioOBMfyBU5KHsUnQvTkdHe42V+uJEnua52wi1+ZvqCxBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T06:57:10.878967Z"},"content_sha256":"e0118d8542ae3b441f59d1d07304963dd6f186facc4165c0a307c75ac1a4b7eb","schema_version":"1.0","event_id":"sha256:e0118d8542ae3b441f59d1d07304963dd6f186facc4165c0a307c75ac1a4b7eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WRDQNBLTN2RZAWVZ7XG67PRSS6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hongxia Xu, Jiahe Chen, Jian Wu, Jiaying He, Jintai Chen, Qian Shao, Qiyuan Chen","submitted_at":"2026-06-25T12:45:42Z","abstract_excerpt":"Open-set semi-supervised learning aims to leverage unlabeled data that may contain out-of-distribution outliers while maintaining performance on in-distribution classes. Existing methods mainly follow two paradigms: filtering suspicious samples or incorporating unlabeled objectives with soft weighting. We argue that both face a common trade-off: aggressive filtering can discard informative but hard ID samples, whereas utilization can introduce auxiliary gradients that conflict with supervised learning when pseudo labels are wrong. We therefore shift the focus from sample selection to gradient-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26973","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/2606.26973/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":"2026-06-26T01:16:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jG/jw4K9nHLD+RxSBQ4mg5F7oACymP/P+RG/4W3wAT/KJghWQbijPL/uD+eWu+zFTvTcHg2x1syDXeObGpWrDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T06:57:10.879344Z"},"content_sha256":"0bb8570a664bd4b9193d2c64648835b54dc979c715bd79306a942d99999285c5","schema_version":"1.0","event_id":"sha256:0bb8570a664bd4b9193d2c64648835b54dc979c715bd79306a942d99999285c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WRDQNBLTN2RZAWVZ7XG67PRSS6/bundle.json","state_url":"https://pith.science/pith/WRDQNBLTN2RZAWVZ7XG67PRSS6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WRDQNBLTN2RZAWVZ7XG67PRSS6/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":"2026-07-05T06:57:10Z","links":{"resolver":"https://pith.science/pith/WRDQNBLTN2RZAWVZ7XG67PRSS6","bundle":"https://pith.science/pith/WRDQNBLTN2RZAWVZ7XG67PRSS6/bundle.json","state":"https://pith.science/pith/WRDQNBLTN2RZAWVZ7XG67PRSS6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WRDQNBLTN2RZAWVZ7XG67PRSS6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WRDQNBLTN2RZAWVZ7XG67PRSS6","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":"933c0d59b41029dd32a24c9ba9c1160794c89d7526ea5aef8f1dfc4eae66a923","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T12:45:42Z","title_canon_sha256":"d297973d2f3e38dc1dc25e2dc66a0f89f9620c359c64578705bcfaa08ecbb7d1"},"schema_version":"1.0","source":{"id":"2606.26973","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.26973","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"arxiv_version","alias_value":"2606.26973v1","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26973","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"pith_short_12","alias_value":"WRDQNBLTN2RZ","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"pith_short_16","alias_value":"WRDQNBLTN2RZAWVZ","created_at":"2026-06-26T01:16:05Z"},{"alias_kind":"pith_short_8","alias_value":"WRDQNBLT","created_at":"2026-06-26T01:16:05Z"}],"graph_snapshots":[{"event_id":"sha256:0bb8570a664bd4b9193d2c64648835b54dc979c715bd79306a942d99999285c5","target":"graph","created_at":"2026-06-26T01:16:05Z","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/2606.26973/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Open-set semi-supervised learning aims to leverage unlabeled data that may contain out-of-distribution outliers while maintaining performance on in-distribution classes. Existing methods mainly follow two paradigms: filtering suspicious samples or incorporating unlabeled objectives with soft weighting. We argue that both face a common trade-off: aggressive filtering can discard informative but hard ID samples, whereas utilization can introduce auxiliary gradients that conflict with supervised learning when pseudo labels are wrong. We therefore shift the focus from sample selection to gradient-","authors_text":"Hongxia Xu, Jiahe Chen, Jian Wu, Jiaying He, Jintai Chen, Qian Shao, Qiyuan Chen","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T12:45:42Z","title":"Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26973","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:e0118d8542ae3b441f59d1d07304963dd6f186facc4165c0a307c75ac1a4b7eb","target":"record","created_at":"2026-06-26T01:16:05Z","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":"933c0d59b41029dd32a24c9ba9c1160794c89d7526ea5aef8f1dfc4eae66a923","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-25T12:45:42Z","title_canon_sha256":"d297973d2f3e38dc1dc25e2dc66a0f89f9620c359c64578705bcfaa08ecbb7d1"},"schema_version":"1.0","source":{"id":"2606.26973","kind":"arxiv","version":1}},"canonical_sha256":"b4470685736ea3905ab9fdcdefbe32979b7809676bc00f40bbcd737ac97159e2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b4470685736ea3905ab9fdcdefbe32979b7809676bc00f40bbcd737ac97159e2","first_computed_at":"2026-06-26T01:16:05.637429Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-26T01:16:05.637429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ej5Be9mcUQ6LXJ0naIsY7PD10WBkFjv2IoeH2TMGBpS6LGshBWI6iGZUrHuZEk4a1RXRobtKzspc37zlK807BA==","signature_status":"signed_v1","signed_at":"2026-06-26T01:16:05.637809Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.26973","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e0118d8542ae3b441f59d1d07304963dd6f186facc4165c0a307c75ac1a4b7eb","sha256:0bb8570a664bd4b9193d2c64648835b54dc979c715bd79306a942d99999285c5"],"state_sha256":"87eeff30e1abffe9fb77cd87284d2bace3a64bec4ffdd31bb46ea9cd8400c60a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZEu7n3wnMexZ76EGXj1q4eS2bgEfCs8WHdKFpW/H8Nbo1XKHQGxL3XE0FV9HeQ1CzQnAUWwWa7LZRigrhQLGBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T06:57:10.881238Z","bundle_sha256":"f0011b7193d3ee141b9ca63522f1552026c07487c367b288743466e5a48b0d7a"}}