{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AG5A5WYW37PIFTUD4RMU45DAVR","short_pith_number":"pith:AG5A5WYW","schema_version":"1.0","canonical_sha256":"01ba0edb16dfde82ce83e4594e7460ac7d6a273a073af793ddc4332adf494859","source":{"kind":"arxiv","id":"2607.11063","version":1},"attestation_state":"computed","paper":{"title":"AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Changhao Chen, Chenyang Li, Kaige Li, Zeyu Jiang","submitted_at":"2026-07-13T04:03:32Z","abstract_excerpt":"Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances. Most existing methods rely on white-box access to target model gradients, which is often unrealistic for real-world deployed systems and computationally exhaustive due to recursive backpropagation for optimization, limiting their applicability. While previous black-box methods predominantly target single-step, instantaneous decision tasks, they struggle to handle the task complexities and temporal dependencies. This highlights the need for a gradient-free attack method "},"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":"2607.11063","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-13T04:03:32Z","cross_cats_sorted":[],"title_canon_sha256":"e0a8a7bdf8e7b494d9f4db94da9df5e9d0e364830a578da5eb09f35f3f28e9b6","abstract_canon_sha256":"b7d1ae3bacc595df94322b364f6ab98fc4e16f9ab6866ecc0cfae318603a6ed4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:22:14.299203Z","signature_b64":"fbRLIoiGUP5a9v3GXNVAh0lZPmL6b7nH0aLq9wj7z08IOsaAe7e4DIZfSobdDmOdnb+O2hIgK0MZSGDjLRXiCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01ba0edb16dfde82ce83e4594e7460ac7d6a273a073af793ddc4332adf494859","last_reissued_at":"2026-07-14T01:22:14.298377Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:22:14.298377Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AdvNav: Behavior-Guided Black-Box Adversarial Attacks on Vision-Language Navigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Changhao Chen, Chenyang Li, Kaige Li, Zeyu Jiang","submitted_at":"2026-07-13T04:03:32Z","abstract_excerpt":"Despite progress in Embodied AI, Vision-and-Language Navigation systems remain vulnerable to adversarial visual disturbances. Most existing methods rely on white-box access to target model gradients, which is often unrealistic for real-world deployed systems and computationally exhaustive due to recursive backpropagation for optimization, limiting their applicability. While previous black-box methods predominantly target single-step, instantaneous decision tasks, they struggle to handle the task complexities and temporal dependencies. This highlights the need for a gradient-free attack method "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11063","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/2607.11063/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":"2607.11063","created_at":"2026-07-14T01:22:14.298804+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.11063v1","created_at":"2026-07-14T01:22:14.298804+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11063","created_at":"2026-07-14T01:22:14.298804+00:00"},{"alias_kind":"pith_short_12","alias_value":"AG5A5WYW37PI","created_at":"2026-07-14T01:22:14.298804+00:00"},{"alias_kind":"pith_short_16","alias_value":"AG5A5WYW37PIFTUD","created_at":"2026-07-14T01:22:14.298804+00:00"},{"alias_kind":"pith_short_8","alias_value":"AG5A5WYW","created_at":"2026-07-14T01:22:14.298804+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/AG5A5WYW37PIFTUD4RMU45DAVR","json":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR.json","graph_json":"https://pith.science/api/pith-number/AG5A5WYW37PIFTUD4RMU45DAVR/graph.json","events_json":"https://pith.science/api/pith-number/AG5A5WYW37PIFTUD4RMU45DAVR/events.json","paper":"https://pith.science/paper/AG5A5WYW"},"agent_actions":{"view_html":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR","download_json":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR.json","view_paper":"https://pith.science/paper/AG5A5WYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.11063&json=true","fetch_graph":"https://pith.science/api/pith-number/AG5A5WYW37PIFTUD4RMU45DAVR/graph.json","fetch_events":"https://pith.science/api/pith-number/AG5A5WYW37PIFTUD4RMU45DAVR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR/action/storage_attestation","attest_author":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR/action/author_attestation","sign_citation":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR/action/citation_signature","submit_replication":"https://pith.science/pith/AG5A5WYW37PIFTUD4RMU45DAVR/action/replication_record"}},"created_at":"2026-07-14T01:22:14.298804+00:00","updated_at":"2026-07-14T01:22:14.298804+00:00"}