{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WFM45NYLQ6Y5P4BCAMD5FBVQX6","short_pith_number":"pith:WFM45NYL","schema_version":"1.0","canonical_sha256":"b159ceb70b87b1d7f0220307d286b0bf908ad42c83a61618857be9ff397cdd8b","source":{"kind":"arxiv","id":"2607.10120","version":1},"attestation_state":"computed","paper":{"title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changhua Pei, Hao Liu, Jianhui Li, Shujun Wang, Xianzhi Ma, Xiaohan Li","submitted_at":"2026-07-11T04:56:27Z","abstract_excerpt":"Ultra-High-Resolution (UHR) remote sensing image understanding requires Vision-Language Models (VLMs) to capture both the global scene layout and sparse yet task-critical local details under limited computational budgets. Existing methods mainly follow two paradigms. One is passive perception, which relies on resolution expansion or token compression and may therefore discard fine-grained details. The other is active perception, which depends on multi-round zooming and search, but suffers from high latency, contextual fragmentation, and error accumulation. We argue that a more effective path t"},"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":"2607.10120","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-11T04:56:27Z","cross_cats_sorted":[],"title_canon_sha256":"673e584804b502137707e9f80ae63eec4755a8ad12786a902f9e196b0faeb238","abstract_canon_sha256":"716bda0af5c145ee69ce5f452e3fd623abda304357627961f3c52701c8bcf4e8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T01:20:26.212122Z","signature_b64":"uyDixw6Ym8H35kz5P4G0xyvRNpZQYScLnZsY421HCZXRXNBGJWtCI8WOmeqVh255bTf3YFvJJ0w3M6/uqgX4Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b159ceb70b87b1d7f0220307d286b0bf908ad42c83a61618857be9ff397cdd8b","last_reissued_at":"2026-07-14T01:20:26.211262Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T01:20:26.211262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Changhua Pei, Hao Liu, Jianhui Li, Shujun Wang, Xianzhi Ma, Xiaohan Li","submitted_at":"2026-07-11T04:56:27Z","abstract_excerpt":"Ultra-High-Resolution (UHR) remote sensing image understanding requires Vision-Language Models (VLMs) to capture both the global scene layout and sparse yet task-critical local details under limited computational budgets. Existing methods mainly follow two paradigms. One is passive perception, which relies on resolution expansion or token compression and may therefore discard fine-grained details. The other is active perception, which depends on multi-round zooming and search, but suffers from high latency, contextual fragmentation, and error accumulation. We argue that a more effective path t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.10120","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/2607.10120/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":"2607.10120","created_at":"2026-07-14T01:20:26.211716+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.10120v1","created_at":"2026-07-14T01:20:26.211716+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.10120","created_at":"2026-07-14T01:20:26.211716+00:00"},{"alias_kind":"pith_short_12","alias_value":"WFM45NYLQ6Y5","created_at":"2026-07-14T01:20:26.211716+00:00"},{"alias_kind":"pith_short_16","alias_value":"WFM45NYLQ6Y5P4BC","created_at":"2026-07-14T01:20:26.211716+00:00"},{"alias_kind":"pith_short_8","alias_value":"WFM45NYL","created_at":"2026-07-14T01:20:26.211716+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/WFM45NYLQ6Y5P4BCAMD5FBVQX6","json":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6.json","graph_json":"https://pith.science/api/pith-number/WFM45NYLQ6Y5P4BCAMD5FBVQX6/graph.json","events_json":"https://pith.science/api/pith-number/WFM45NYLQ6Y5P4BCAMD5FBVQX6/events.json","paper":"https://pith.science/paper/WFM45NYL"},"agent_actions":{"view_html":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6","download_json":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6.json","view_paper":"https://pith.science/paper/WFM45NYL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.10120&json=true","fetch_graph":"https://pith.science/api/pith-number/WFM45NYLQ6Y5P4BCAMD5FBVQX6/graph.json","fetch_events":"https://pith.science/api/pith-number/WFM45NYLQ6Y5P4BCAMD5FBVQX6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6/action/storage_attestation","attest_author":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6/action/author_attestation","sign_citation":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6/action/citation_signature","submit_replication":"https://pith.science/pith/WFM45NYLQ6Y5P4BCAMD5FBVQX6/action/replication_record"}},"created_at":"2026-07-14T01:20:26.211716+00:00","updated_at":"2026-07-14T01:20:26.211716+00:00"}