{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OU334ZYPGDWY7PC37TGUQH5LMG","short_pith_number":"pith:OU334ZYP","schema_version":"1.0","canonical_sha256":"7537be670f30ed8fbc5bfccd481fab618195078b91d8cf861b58913e948f81ba","source":{"kind":"arxiv","id":"2501.12553","version":2},"attestation_state":"computed","paper":{"title":"ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Maria Gorlatova, Tim Scargill, Yanming Xiu","submitted_at":"2025-01-22T00:17:08Z","abstract_excerpt":"In Augmented Reality (AR), virtual content enhances user experience by providing additional information. However, improperly positioned or designed virtual content can be detrimental to task performance, as it can impair users' ability to accurately interpret real-world information. In this paper we examine two types of task-detrimental virtual content: obstruction attacks, in which virtual content prevents users from seeing real-world objects, and information manipulation attacks, in which virtual content interferes with users' ability to accurately interpret real-world information. We provid"},"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":"2501.12553","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-01-22T00:17:08Z","cross_cats_sorted":[],"title_canon_sha256":"b6784f2505b143dd397099979a33625919260a0cc8909be4cb0c0f8a6688cef1","abstract_canon_sha256":"df91f86b4b9adb41a592d4e048887e594b37430933cefa55a38f783783e7cb2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:45.153152Z","signature_b64":"+pCitRn/XhhC7SvNWubWOydBXTp3kTx6jHMVIFwSKOGQJLn87dtxVeg+xQG/t5pigIGfAh2Gn/kJ3HUGu10SDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7537be670f30ed8fbc5bfccd481fab618195078b91d8cf861b58913e948f81ba","last_reissued_at":"2026-07-05T12:03:45.152651Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:45.152651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ViDDAR: Vision Language Model-Based Task-Detrimental Content Detection for Augmented Reality","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Maria Gorlatova, Tim Scargill, Yanming Xiu","submitted_at":"2025-01-22T00:17:08Z","abstract_excerpt":"In Augmented Reality (AR), virtual content enhances user experience by providing additional information. However, improperly positioned or designed virtual content can be detrimental to task performance, as it can impair users' ability to accurately interpret real-world information. In this paper we examine two types of task-detrimental virtual content: obstruction attacks, in which virtual content prevents users from seeing real-world objects, and information manipulation attacks, in which virtual content interferes with users' ability to accurately interpret real-world information. We provid"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.12553","kind":"arxiv","version":2},"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/2501.12553/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":"2501.12553","created_at":"2026-07-05T12:03:45.152709+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.12553v2","created_at":"2026-07-05T12:03:45.152709+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.12553","created_at":"2026-07-05T12:03:45.152709+00:00"},{"alias_kind":"pith_short_12","alias_value":"OU334ZYPGDWY","created_at":"2026-07-05T12:03:45.152709+00:00"},{"alias_kind":"pith_short_16","alias_value":"OU334ZYPGDWY7PC3","created_at":"2026-07-05T12:03:45.152709+00:00"},{"alias_kind":"pith_short_8","alias_value":"OU334ZYP","created_at":"2026-07-05T12:03:45.152709+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.10154","citing_title":"PRISM-XR: Empowering Privacy-Aware XR Collaboration with Multimodal Large Language Models","ref_index":57,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG","json":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG.json","graph_json":"https://pith.science/api/pith-number/OU334ZYPGDWY7PC37TGUQH5LMG/graph.json","events_json":"https://pith.science/api/pith-number/OU334ZYPGDWY7PC37TGUQH5LMG/events.json","paper":"https://pith.science/paper/OU334ZYP"},"agent_actions":{"view_html":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG","download_json":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG.json","view_paper":"https://pith.science/paper/OU334ZYP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.12553&json=true","fetch_graph":"https://pith.science/api/pith-number/OU334ZYPGDWY7PC37TGUQH5LMG/graph.json","fetch_events":"https://pith.science/api/pith-number/OU334ZYPGDWY7PC37TGUQH5LMG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG/action/storage_attestation","attest_author":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG/action/author_attestation","sign_citation":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG/action/citation_signature","submit_replication":"https://pith.science/pith/OU334ZYPGDWY7PC37TGUQH5LMG/action/replication_record"}},"created_at":"2026-07-05T12:03:45.152709+00:00","updated_at":"2026-07-05T12:03:45.152709+00:00"}