{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BBZ3PFBRJSWG7Y3MPXPJX5UIND","short_pith_number":"pith:BBZ3PFBR","schema_version":"1.0","canonical_sha256":"0873b794314cac6fe36c7dde9bf68868d9507b55d05bc24739d10579b01eb696","source":{"kind":"arxiv","id":"2608.09467","version":1},"attestation_state":"computed","paper":{"title":"RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Boxiong Wang, Chao Yu, Daxin Tian, Geng Sun, Hui Kang, Jiahui Li","submitted_at":"2026-08-10T11:37:46Z","abstract_excerpt":"Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments. Although recent end-to-end UAV vision-language-action (UAV-VLA) policies reduce reliance on separately designed perception, planning, and control modules, their behavior-cloning objectives provide limited corrective supervision for interactive closed-loop execution. Reinforcement learning (RL) offers a promising solution, while its effectiveness is constrained by inefficient use of samples, long-tailed scen"},"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":"2608.09467","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-10T11:37:46Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"35d04445b143dd60c8c18728e01f518b6c4e971fe0f0d6d37f823bcbccd75867","abstract_canon_sha256":"91fd307d481abbf15d3e67125af55c46826368746bebc103ca604fb279cccdd1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T02:24:17.028347Z","signature_b64":"B2Ot4aTF3+tfBqU5w9mPmxTPeIHnOM7aCaW2Rl0uYg8INt0O9KrQHhaAlJlExRC3+SRRzj6vXIkPhy1xwIOcCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0873b794314cac6fe36c7dde9bf68868d9507b55d05bc24739d10579b01eb696","last_reissued_at":"2026-08-11T02:24:17.026725Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T02:24:17.026725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RecoverFly: A Failure-Aware Reinforcement Learning Post-Training Framework for Aerial Vision-Language Navigation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Boxiong Wang, Chao Yu, Daxin Tian, Geng Sun, Hui Kang, Jiahui Li","submitted_at":"2026-08-10T11:37:46Z","abstract_excerpt":"Unmanned aerial vehicle vision-language navigation (UAV-VLN) requires agents to translate visual observations and language instructions into reliable flight actions in complex environments. Although recent end-to-end UAV vision-language-action (UAV-VLA) policies reduce reliance on separately designed perception, planning, and control modules, their behavior-cloning objectives provide limited corrective supervision for interactive closed-loop execution. Reinforcement learning (RL) offers a promising solution, while its effectiveness is constrained by inefficient use of samples, long-tailed scen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.09467","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/2608.09467/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":"2608.09467","created_at":"2026-08-11T02:24:17.027428+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.09467v1","created_at":"2026-08-11T02:24:17.027428+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.09467","created_at":"2026-08-11T02:24:17.027428+00:00"},{"alias_kind":"pith_short_12","alias_value":"BBZ3PFBRJSWG","created_at":"2026-08-11T02:24:17.027428+00:00"},{"alias_kind":"pith_short_16","alias_value":"BBZ3PFBRJSWG7Y3M","created_at":"2026-08-11T02:24:17.027428+00:00"},{"alias_kind":"pith_short_8","alias_value":"BBZ3PFBR","created_at":"2026-08-11T02:24:17.027428+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/BBZ3PFBRJSWG7Y3MPXPJX5UIND","json":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND.json","graph_json":"https://pith.science/api/pith-number/BBZ3PFBRJSWG7Y3MPXPJX5UIND/graph.json","events_json":"https://pith.science/api/pith-number/BBZ3PFBRJSWG7Y3MPXPJX5UIND/events.json","paper":"https://pith.science/paper/BBZ3PFBR"},"agent_actions":{"view_html":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND","download_json":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND.json","view_paper":"https://pith.science/paper/BBZ3PFBR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.09467&json=true","fetch_graph":"https://pith.science/api/pith-number/BBZ3PFBRJSWG7Y3MPXPJX5UIND/graph.json","fetch_events":"https://pith.science/api/pith-number/BBZ3PFBRJSWG7Y3MPXPJX5UIND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND/action/storage_attestation","attest_author":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND/action/author_attestation","sign_citation":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND/action/citation_signature","submit_replication":"https://pith.science/pith/BBZ3PFBRJSWG7Y3MPXPJX5UIND/action/replication_record"}},"created_at":"2026-08-11T02:24:17.027428+00:00","updated_at":"2026-08-11T02:24:17.027428+00:00"}