{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:O3UOEVWYQTW2FU3X6VTUM5DGFQ","short_pith_number":"pith:O3UOEVWY","schema_version":"1.0","canonical_sha256":"76e8e256d884eda2d377f5674674662c19e94d5c70e85b637ddade5dfc05d794","source":{"kind":"arxiv","id":"2605.30745","version":1},"attestation_state":"computed","paper":{"title":"Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Wanlong Fang, Wei Ji, Xiang Fang","submitted_at":"2026-05-29T02:22:01Z","abstract_excerpt":"Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. However, this semantic abstraction creates a critical vulnerability in open-world deployment: the ``Hubris of Semantics'', where models force-fit unknown anomalies into known categories with high confidence due to the lack of explicit negative knowledge. To address this \\textit{Open-World Trustworthiness Paradox}, we propose \\textbf{Immuno-VLM}, a bio-inspired framework that adapts the biological principle of \\textbf{Immunological Negative Selectio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.30745","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-29T02:22:01Z","cross_cats_sorted":[],"title_canon_sha256":"a3bc8677007ba3b5fff76e7153c15157f4603938bee39d4f3361cc74dcece356","abstract_canon_sha256":"49a630daf8c7d3da74e032a8556e4b6c4d7ec60da7289669dfa2446190482f30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:03:13.848191Z","signature_b64":"dZvjFpsjbPeclZAbf/GZvry2TaP1mbVS104lsvVr74QQZBURppq9axKOWFzP0RlpZDzxiFUImShiweZxZiAXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"76e8e256d884eda2d377f5674674662c19e94d5c70e85b637ddade5dfc05d794","last_reissued_at":"2026-06-01T01:03:13.847284Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:03:13.847284Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Immuno-VLM: Immunizing Large Vision-Language Models via Generative Semantic Antibodies for Open-World Trustworthiness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Wanlong Fang, Wei Ji, Xiang Fang","submitted_at":"2026-05-29T02:22:01Z","abstract_excerpt":"Large Vision-Language Models have achieved unprecedented success in zero-shot recognition by aligning visual features with broad semantic concepts. However, this semantic abstraction creates a critical vulnerability in open-world deployment: the ``Hubris of Semantics'', where models force-fit unknown anomalies into known categories with high confidence due to the lack of explicit negative knowledge. To address this \\textit{Open-World Trustworthiness Paradox}, we propose \\textbf{Immuno-VLM}, a bio-inspired framework that adapts the biological principle of \\textbf{Immunological Negative Selectio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30745","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/2605.30745/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.30745","created_at":"2026-06-01T01:03:13.847416+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.30745v1","created_at":"2026-06-01T01:03:13.847416+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30745","created_at":"2026-06-01T01:03:13.847416+00:00"},{"alias_kind":"pith_short_12","alias_value":"O3UOEVWYQTW2","created_at":"2026-06-01T01:03:13.847416+00:00"},{"alias_kind":"pith_short_16","alias_value":"O3UOEVWYQTW2FU3X","created_at":"2026-06-01T01:03:13.847416+00:00"},{"alias_kind":"pith_short_8","alias_value":"O3UOEVWY","created_at":"2026-06-01T01:03:13.847416+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ","json":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ.json","graph_json":"https://pith.science/api/pith-number/O3UOEVWYQTW2FU3X6VTUM5DGFQ/graph.json","events_json":"https://pith.science/api/pith-number/O3UOEVWYQTW2FU3X6VTUM5DGFQ/events.json","paper":"https://pith.science/paper/O3UOEVWY"},"agent_actions":{"view_html":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ","download_json":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ.json","view_paper":"https://pith.science/paper/O3UOEVWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.30745&json=true","fetch_graph":"https://pith.science/api/pith-number/O3UOEVWYQTW2FU3X6VTUM5DGFQ/graph.json","fetch_events":"https://pith.science/api/pith-number/O3UOEVWYQTW2FU3X6VTUM5DGFQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ/action/storage_attestation","attest_author":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ/action/author_attestation","sign_citation":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ/action/citation_signature","submit_replication":"https://pith.science/pith/O3UOEVWYQTW2FU3X6VTUM5DGFQ/action/replication_record"}},"created_at":"2026-06-01T01:03:13.847416+00:00","updated_at":"2026-06-01T01:03:13.847416+00:00"}