{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:CRJJEB5TIDAY5KNKB37ROLHRMK","short_pith_number":"pith:CRJJEB5T","canonical_record":{"source":{"id":"2506.06965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-08T02:01:49Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"70c608cc46455c1ca0949a35efb818f939e57e192527ff1241685c97d71a100b","abstract_canon_sha256":"4d9ecf56745c66f7348a8b7df3e8b74cf5a0be7e433ed940ef334512c756c917"},"schema_version":"1.0"},"canonical_sha256":"14529207b340c18ea9aa0eff172cf162b40a81b65909f5113da7204060f30419","source":{"kind":"arxiv","id":"2506.06965","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06965","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06965v1","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06965","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"pith_short_12","alias_value":"CRJJEB5TIDAY","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"pith_short_16","alias_value":"CRJJEB5TIDAY5KNK","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"pith_short_8","alias_value":"CRJJEB5T","created_at":"2026-07-05T11:45:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:CRJJEB5TIDAY5KNKB37ROLHRMK","target":"record","payload":{"canonical_record":{"source":{"id":"2506.06965","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-08T02:01:49Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"70c608cc46455c1ca0949a35efb818f939e57e192527ff1241685c97d71a100b","abstract_canon_sha256":"4d9ecf56745c66f7348a8b7df3e8b74cf5a0be7e433ed940ef334512c756c917"},"schema_version":"1.0"},"canonical_sha256":"14529207b340c18ea9aa0eff172cf162b40a81b65909f5113da7204060f30419","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:36.292546Z","signature_b64":"WRcqBsOP0hDB+i1cTGnkQ7/7y2fMFzfTiV/UlN7zPQAOqbUqgRNdhrA0qmPtOFb4fVG2dyk5rZCP5NuTYKbZAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14529207b340c18ea9aa0eff172cf162b40a81b65909f5113da7204060f30419","last_reissued_at":"2026-07-05T11:45:36.292036Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:36.292036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.06965","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":"2026-07-05T11:45:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jOeWxxR5groHivoOeJS3349zf/Y2yNmYzGEDUzzIuZ0Q3bD1nYSziXn6x8aSOWnAf+bS5Ywoq0BW2iperqpECw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:52:36.238678Z"},"content_sha256":"0d0fa48efb00f1a45d0d65832128751dfd0236320c84d09b4c8677f6c258ccd6","schema_version":"1.0","event_id":"sha256:0d0fa48efb00f1a45d0d65832128751dfd0236320c84d09b4c8677f6c258ccd6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:CRJJEB5TIDAY5KNKB37ROLHRMK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Long-Tailed Learning for Generalized Category Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.AI","authors_text":"Cuong Manh Hoang","submitted_at":"2025-06-08T02:01:49Z","abstract_excerpt":"Generalized Category Discovery (GCD) utilizes labeled samples of known classes to discover novel classes in unlabeled samples. Existing methods show effective performance on artificial datasets with balanced distributions. However, real-world datasets are always imbalanced, significantly affecting the effectiveness of these methods. To solve this problem, we propose a novel framework that performs generalized category discovery in long-tailed distributions. We first present a self-guided labeling technique that uses a learnable distribution to generate pseudo-labels, resulting in less biased c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06965","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/2506.06965/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":"2026-07-05T11:45:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eFBIaL+s7dUW98lVK1DRNxF+4z47W4+zzR+Gi2RuWfHsVwm+M16SeGPrQZKC2ftiEirwZTraaHPXvmPvZiaoCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T08:52:36.239593Z"},"content_sha256":"7acc3aaff07b0cfdd52379da2ade52a0aa0e3e12e5dd589cf26ef99ea12e76ff","schema_version":"1.0","event_id":"sha256:7acc3aaff07b0cfdd52379da2ade52a0aa0e3e12e5dd589cf26ef99ea12e76ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CRJJEB5TIDAY5KNKB37ROLHRMK/bundle.json","state_url":"https://pith.science/pith/CRJJEB5TIDAY5KNKB37ROLHRMK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CRJJEB5TIDAY5KNKB37ROLHRMK/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":"2026-08-08T08:52:36Z","links":{"resolver":"https://pith.science/pith/CRJJEB5TIDAY5KNKB37ROLHRMK","bundle":"https://pith.science/pith/CRJJEB5TIDAY5KNKB37ROLHRMK/bundle.json","state":"https://pith.science/pith/CRJJEB5TIDAY5KNKB37ROLHRMK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CRJJEB5TIDAY5KNKB37ROLHRMK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:CRJJEB5TIDAY5KNKB37ROLHRMK","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":"4d9ecf56745c66f7348a8b7df3e8b74cf5a0be7e433ed940ef334512c756c917","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-08T02:01:49Z","title_canon_sha256":"70c608cc46455c1ca0949a35efb818f939e57e192527ff1241685c97d71a100b"},"schema_version":"1.0","source":{"id":"2506.06965","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.06965","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"arxiv_version","alias_value":"2506.06965v1","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06965","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"pith_short_12","alias_value":"CRJJEB5TIDAY","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"pith_short_16","alias_value":"CRJJEB5TIDAY5KNK","created_at":"2026-07-05T11:45:36Z"},{"alias_kind":"pith_short_8","alias_value":"CRJJEB5T","created_at":"2026-07-05T11:45:36Z"}],"graph_snapshots":[{"event_id":"sha256:7acc3aaff07b0cfdd52379da2ade52a0aa0e3e12e5dd589cf26ef99ea12e76ff","target":"graph","created_at":"2026-07-05T11:45:36Z","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/2506.06965/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Generalized Category Discovery (GCD) utilizes labeled samples of known classes to discover novel classes in unlabeled samples. Existing methods show effective performance on artificial datasets with balanced distributions. However, real-world datasets are always imbalanced, significantly affecting the effectiveness of these methods. To solve this problem, we propose a novel framework that performs generalized category discovery in long-tailed distributions. We first present a self-guided labeling technique that uses a learnable distribution to generate pseudo-labels, resulting in less biased c","authors_text":"Cuong Manh Hoang","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-08T02:01:49Z","title":"Long-Tailed Learning for Generalized Category Discovery"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06965","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":"sha256:0d0fa48efb00f1a45d0d65832128751dfd0236320c84d09b4c8677f6c258ccd6","target":"record","created_at":"2026-07-05T11:45:36Z","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":"4d9ecf56745c66f7348a8b7df3e8b74cf5a0be7e433ed940ef334512c756c917","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-08T02:01:49Z","title_canon_sha256":"70c608cc46455c1ca0949a35efb818f939e57e192527ff1241685c97d71a100b"},"schema_version":"1.0","source":{"id":"2506.06965","kind":"arxiv","version":1}},"canonical_sha256":"14529207b340c18ea9aa0eff172cf162b40a81b65909f5113da7204060f30419","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14529207b340c18ea9aa0eff172cf162b40a81b65909f5113da7204060f30419","first_computed_at":"2026-07-05T11:45:36.292036Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:45:36.292036Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WRcqBsOP0hDB+i1cTGnkQ7/7y2fMFzfTiV/UlN7zPQAOqbUqgRNdhrA0qmPtOFb4fVG2dyk5rZCP5NuTYKbZAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:45:36.292546Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.06965","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0d0fa48efb00f1a45d0d65832128751dfd0236320c84d09b4c8677f6c258ccd6","sha256:7acc3aaff07b0cfdd52379da2ade52a0aa0e3e12e5dd589cf26ef99ea12e76ff"],"state_sha256":"a618d6b15995c64e2d9f960540bbb296463562f6dc33f4294ccb3ce2b35a8f7a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"e6Lp5W3szGeBCHid86zqs0kvMof4p2w/a2+k7SDe6nvUCKI7iYIUWu5cfKywpE0x+tuUMcLhZO2Yf0Ps+SdtBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T08:52:36.244363Z","bundle_sha256":"fd88ad523938a5eba7c02d06ba499f118b7ef68dfd0cdd84fb0fe6674fd0c50f"}}