{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MSYIJ2YQKBKQFSAAY7BLTBMNQX","short_pith_number":"pith:MSYIJ2YQ","canonical_record":{"source":{"id":"2605.23231","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T04:48:14Z","cross_cats_sorted":[],"title_canon_sha256":"e9439799f1811b33de1d81eebd6f95ec9b8a2a91d775e36f81dd6133c58e06a7","abstract_canon_sha256":"af2e881219d0ef295dfc9872fd6896104aeb7d2fa8f7215e242c9fa48f1a4336"},"schema_version":"1.0"},"canonical_sha256":"64b084eb10505502c800c7c2b9858d85cdec5cacb6518a8ce3fc35867ed733a6","source":{"kind":"arxiv","id":"2605.23231","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.23231","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"arxiv_version","alias_value":"2605.23231v1","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23231","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"pith_short_12","alias_value":"MSYIJ2YQKBKQ","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"pith_short_16","alias_value":"MSYIJ2YQKBKQFSAA","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"pith_short_8","alias_value":"MSYIJ2YQ","created_at":"2026-05-25T02:01:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MSYIJ2YQKBKQFSAAY7BLTBMNQX","target":"record","payload":{"canonical_record":{"source":{"id":"2605.23231","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T04:48:14Z","cross_cats_sorted":[],"title_canon_sha256":"e9439799f1811b33de1d81eebd6f95ec9b8a2a91d775e36f81dd6133c58e06a7","abstract_canon_sha256":"af2e881219d0ef295dfc9872fd6896104aeb7d2fa8f7215e242c9fa48f1a4336"},"schema_version":"1.0"},"canonical_sha256":"64b084eb10505502c800c7c2b9858d85cdec5cacb6518a8ce3fc35867ed733a6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:44.749992Z","signature_b64":"e+5aeLksfWztrIO34nW5vVycXjvnZksvKa7cvwVK3IUGH1xgJRc08lsIOBlZ8Zw87nGqT7FkzJPIUnaC1Jw8DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64b084eb10505502c800c7c2b9858d85cdec5cacb6518a8ce3fc35867ed733a6","last_reissued_at":"2026-05-25T02:01:44.749426Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:44.749426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.23231","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":"2026-05-25T02:01:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6YxRUMHBrgfYc0Nv13XNoDog0za7C6wOXC6rmHooDbciDzEg11MTXR4COKZ5kGwifDXz8G3S05vGNIdVOXHVDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T19:07:40.348421Z"},"content_sha256":"c320b772fbdef3b52a71071e6edaad1f5f0b110cfd08cb64e81b70a128cc4e00","schema_version":"1.0","event_id":"sha256:c320b772fbdef3b52a71071e6edaad1f5f0b110cfd08cb64e81b70a128cc4e00"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MSYIJ2YQKBKQFSAAY7BLTBMNQX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Normal References: Discriminative Few-Shot Anomaly Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guansong Pang, Huan Wang, Jun Shen, Jun Yan","submitted_at":"2026-05-22T04:48:14Z","abstract_excerpt":"This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both references can overfit to the seen anomalies. We introduce IDEAL, an intrinsic deviation learning framework that leverages both reference types to learn intrinsic deviation patterns characterizing generalizable abnormalit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23231","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/2605.23231/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":"2026-05-25T02:01:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TrB/KlTKs3JulU7kbrysjPgCtvWnLnT628a998lfV8kd3ic3NgIr0I3KIWhfnBZVF92S3sTvLLdNSMX/oXu9Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T19:07:40.349132Z"},"content_sha256":"2b32097d3a06e802b90c76ecf06b92000fe72e27646a8c37fc433adcf8637ad2","schema_version":"1.0","event_id":"sha256:2b32097d3a06e802b90c76ecf06b92000fe72e27646a8c37fc433adcf8637ad2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MSYIJ2YQKBKQFSAAY7BLTBMNQX/bundle.json","state_url":"https://pith.science/pith/MSYIJ2YQKBKQFSAAY7BLTBMNQX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MSYIJ2YQKBKQFSAAY7BLTBMNQX/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":"2026-05-26T19:07:40Z","links":{"resolver":"https://pith.science/pith/MSYIJ2YQKBKQFSAAY7BLTBMNQX","bundle":"https://pith.science/pith/MSYIJ2YQKBKQFSAAY7BLTBMNQX/bundle.json","state":"https://pith.science/pith/MSYIJ2YQKBKQFSAAY7BLTBMNQX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MSYIJ2YQKBKQFSAAY7BLTBMNQX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MSYIJ2YQKBKQFSAAY7BLTBMNQX","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":"af2e881219d0ef295dfc9872fd6896104aeb7d2fa8f7215e242c9fa48f1a4336","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T04:48:14Z","title_canon_sha256":"e9439799f1811b33de1d81eebd6f95ec9b8a2a91d775e36f81dd6133c58e06a7"},"schema_version":"1.0","source":{"id":"2605.23231","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.23231","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"arxiv_version","alias_value":"2605.23231v1","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.23231","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"pith_short_12","alias_value":"MSYIJ2YQKBKQ","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"pith_short_16","alias_value":"MSYIJ2YQKBKQFSAA","created_at":"2026-05-25T02:01:44Z"},{"alias_kind":"pith_short_8","alias_value":"MSYIJ2YQ","created_at":"2026-05-25T02:01:44Z"}],"graph_snapshots":[{"event_id":"sha256:2b32097d3a06e802b90c76ecf06b92000fe72e27646a8c37fc433adcf8637ad2","target":"graph","created_at":"2026-05-25T02:01:44Z","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/2605.23231/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper considers a practical few-shot anomaly detection (FSAD) setting, termed discriminative FSAD, where a limited number of both normal and anomalous examples are available as references during inference. Existing FSAD methods rely on normal-only references through normality matching, ignoring the discriminative clues in anomalous references, while directly fitting both references can overfit to the seen anomalies. We introduce IDEAL, an intrinsic deviation learning framework that leverages both reference types to learn intrinsic deviation patterns characterizing generalizable abnormalit","authors_text":"Guansong Pang, Huan Wang, Jun Shen, Jun Yan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T04:48:14Z","title":"Beyond Normal References: Discriminative Few-Shot Anomaly Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.23231","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:c320b772fbdef3b52a71071e6edaad1f5f0b110cfd08cb64e81b70a128cc4e00","target":"record","created_at":"2026-05-25T02:01:44Z","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":"af2e881219d0ef295dfc9872fd6896104aeb7d2fa8f7215e242c9fa48f1a4336","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-22T04:48:14Z","title_canon_sha256":"e9439799f1811b33de1d81eebd6f95ec9b8a2a91d775e36f81dd6133c58e06a7"},"schema_version":"1.0","source":{"id":"2605.23231","kind":"arxiv","version":1}},"canonical_sha256":"64b084eb10505502c800c7c2b9858d85cdec5cacb6518a8ce3fc35867ed733a6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"64b084eb10505502c800c7c2b9858d85cdec5cacb6518a8ce3fc35867ed733a6","first_computed_at":"2026-05-25T02:01:44.749426Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:01:44.749426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e+5aeLksfWztrIO34nW5vVycXjvnZksvKa7cvwVK3IUGH1xgJRc08lsIOBlZ8Zw87nGqT7FkzJPIUnaC1Jw8DA==","signature_status":"signed_v1","signed_at":"2026-05-25T02:01:44.749992Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.23231","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c320b772fbdef3b52a71071e6edaad1f5f0b110cfd08cb64e81b70a128cc4e00","sha256:2b32097d3a06e802b90c76ecf06b92000fe72e27646a8c37fc433adcf8637ad2"],"state_sha256":"9e53d1c7bf9cabea9e9789d14e89aca9df2a08e63244830fc00fa9744fafe632"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7n12c/Riix72IBWEnsicVh23cEFiyMLbiAT5NAUTcGbyCvKrPh3gGUTesw0TNm2UnAZGpYmkOhSjwtrkrmJ/DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T19:07:40.352210Z","bundle_sha256":"d9e583b32bbfa1dab45f00be69ebd65e25d847a8d29987f07858ca15b08403d2"}}