{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ATYAQD2WV6US4SKNFFR4CWBKDT","short_pith_number":"pith:ATYAQD2W","canonical_record":{"source":{"id":"2502.18923","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-26T08:19:55Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"60ecf842a3a170c158441dc1103cebd1d4f69ec522e99cee28d822eadd3623b9","abstract_canon_sha256":"8442423b9eb646cbb560cfb54aa88636e4449edc2a07a67c260d02658aa6374c"},"schema_version":"1.0"},"canonical_sha256":"04f0080f56afa92e494d2963c1582a1cc2deb3ee175fe1af6b8e0aafe65527ee","source":{"kind":"arxiv","id":"2502.18923","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.18923","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"arxiv_version","alias_value":"2502.18923v1","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.18923","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"pith_short_12","alias_value":"ATYAQD2WV6US","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"pith_short_16","alias_value":"ATYAQD2WV6US4SKN","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"pith_short_8","alias_value":"ATYAQD2W","created_at":"2026-07-05T10:20:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ATYAQD2WV6US4SKNFFR4CWBKDT","target":"record","payload":{"canonical_record":{"source":{"id":"2502.18923","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-26T08:19:55Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"60ecf842a3a170c158441dc1103cebd1d4f69ec522e99cee28d822eadd3623b9","abstract_canon_sha256":"8442423b9eb646cbb560cfb54aa88636e4449edc2a07a67c260d02658aa6374c"},"schema_version":"1.0"},"canonical_sha256":"04f0080f56afa92e494d2963c1582a1cc2deb3ee175fe1af6b8e0aafe65527ee","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:20:23.340390Z","signature_b64":"n4jrA7LBSpXvq3Jjs0hCmZbl0gtgZjm+Kx7+xyntyzks6bJewY3aM/tem+fWlg+FOXvHYr+Hh2sdU2mEa8j6CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"04f0080f56afa92e494d2963c1582a1cc2deb3ee175fe1af6b8e0aafe65527ee","last_reissued_at":"2026-07-05T10:20:23.339807Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:20:23.339807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.18923","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":"2026-07-05T10:20:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5mJIlPB2Wddas7GOgOje70QpmBujpKjto06tzockbIWr/FMgJWLBOTQsCqu46scjYqzyid4HaDFqT8dt9ryMAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T03:04:17.610458Z"},"content_sha256":"93d598edc7564f7a3ca58e2f6343c7b6276e37585b6914d7838277397b616a44","schema_version":"1.0","event_id":"sha256:93d598edc7564f7a3ca58e2f6343c7b6276e37585b6914d7838277397b616a44"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ATYAQD2WV6US4SKNFFR4CWBKDT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Brain-inspired analogical mixture prototypes for few-shot class-incremental learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Peng Wang, Wanyi Li, Wei Wei, Yongkang Luo","submitted_at":"2025-02-26T08:19:55Z","abstract_excerpt":"Few-shot class-incremental learning (FSCIL) poses significant challenges for artificial neural networks due to the need to efficiently learn from limited data while retaining knowledge of previously learned tasks. Inspired by the brain's mechanisms for categorization and analogical learning, we propose a novel approach called Brain-inspired Analogical Mixture Prototypes (BAMP). BAMP has three components: mixed prototypical feature learning, statistical analogy, and soft voting. Starting from a pre-trained Vision Transformer (ViT), mixed prototypical feature learning represents each class using"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.18923","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/2502.18923/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":"2026-07-05T10:20:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6XZ6U95ZPoc4ZB7ZMcIpi8gRqRXh1Bpfji4KR2/7LiUWNswOp/zs0k1JUynMmomXCK07IEJ3IF5+czPUEky+Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T03:04:17.610846Z"},"content_sha256":"db8300e4971eee0a2b19603eab079087f0d7d343305a96469bc8b646407cfbd7","schema_version":"1.0","event_id":"sha256:db8300e4971eee0a2b19603eab079087f0d7d343305a96469bc8b646407cfbd7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ATYAQD2WV6US4SKNFFR4CWBKDT/bundle.json","state_url":"https://pith.science/pith/ATYAQD2WV6US4SKNFFR4CWBKDT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ATYAQD2WV6US4SKNFFR4CWBKDT/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":"2026-08-11T03:04:17Z","links":{"resolver":"https://pith.science/pith/ATYAQD2WV6US4SKNFFR4CWBKDT","bundle":"https://pith.science/pith/ATYAQD2WV6US4SKNFFR4CWBKDT/bundle.json","state":"https://pith.science/pith/ATYAQD2WV6US4SKNFFR4CWBKDT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ATYAQD2WV6US4SKNFFR4CWBKDT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ATYAQD2WV6US4SKNFFR4CWBKDT","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":"8442423b9eb646cbb560cfb54aa88636e4449edc2a07a67c260d02658aa6374c","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-26T08:19:55Z","title_canon_sha256":"60ecf842a3a170c158441dc1103cebd1d4f69ec522e99cee28d822eadd3623b9"},"schema_version":"1.0","source":{"id":"2502.18923","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.18923","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"arxiv_version","alias_value":"2502.18923v1","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.18923","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"pith_short_12","alias_value":"ATYAQD2WV6US","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"pith_short_16","alias_value":"ATYAQD2WV6US4SKN","created_at":"2026-07-05T10:20:23Z"},{"alias_kind":"pith_short_8","alias_value":"ATYAQD2W","created_at":"2026-07-05T10:20:23Z"}],"graph_snapshots":[{"event_id":"sha256:db8300e4971eee0a2b19603eab079087f0d7d343305a96469bc8b646407cfbd7","target":"graph","created_at":"2026-07-05T10:20:23Z","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/2502.18923/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Few-shot class-incremental learning (FSCIL) poses significant challenges for artificial neural networks due to the need to efficiently learn from limited data while retaining knowledge of previously learned tasks. Inspired by the brain's mechanisms for categorization and analogical learning, we propose a novel approach called Brain-inspired Analogical Mixture Prototypes (BAMP). BAMP has three components: mixed prototypical feature learning, statistical analogy, and soft voting. Starting from a pre-trained Vision Transformer (ViT), mixed prototypical feature learning represents each class using","authors_text":"Peng Wang, Wanyi Li, Wei Wei, Yongkang Luo","cross_cats":["cs.LG","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-26T08:19:55Z","title":"Brain-inspired analogical mixture prototypes for few-shot class-incremental learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.18923","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":"sha256:93d598edc7564f7a3ca58e2f6343c7b6276e37585b6914d7838277397b616a44","target":"record","created_at":"2026-07-05T10:20:23Z","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":"8442423b9eb646cbb560cfb54aa88636e4449edc2a07a67c260d02658aa6374c","cross_cats_sorted":["cs.LG","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-26T08:19:55Z","title_canon_sha256":"60ecf842a3a170c158441dc1103cebd1d4f69ec522e99cee28d822eadd3623b9"},"schema_version":"1.0","source":{"id":"2502.18923","kind":"arxiv","version":1}},"canonical_sha256":"04f0080f56afa92e494d2963c1582a1cc2deb3ee175fe1af6b8e0aafe65527ee","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"04f0080f56afa92e494d2963c1582a1cc2deb3ee175fe1af6b8e0aafe65527ee","first_computed_at":"2026-07-05T10:20:23.339807Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:20:23.339807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"n4jrA7LBSpXvq3Jjs0hCmZbl0gtgZjm+Kx7+xyntyzks6bJewY3aM/tem+fWlg+FOXvHYr+Hh2sdU2mEa8j6CA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:20:23.340390Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.18923","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93d598edc7564f7a3ca58e2f6343c7b6276e37585b6914d7838277397b616a44","sha256:db8300e4971eee0a2b19603eab079087f0d7d343305a96469bc8b646407cfbd7"],"state_sha256":"11915797ef3931a143b56f7a3fbee42128f9671871a4e8ded5b2c7959e2cefd1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RbLOMFOwj149ieyN1XY2fjfWKIS9ndT9j1wS+ZZt4bs6mSD4cMUt4SG/73a+4ywC/hFasgX6vXA+xIvypyglCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T03:04:17.613182Z","bundle_sha256":"1b9b14db08c7b8520ede63e021821b3b17ec0f316eb43abf6d77f6ddb4122d03"}}