{"as_of":"2026-08-17T01:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1abb50592039ae2637085ff84bbf0d99e60a30685f82d1357ee85a77f76bc678","coverage":[{"denominator":71,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":71,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-14T14:09:30.395518Z","state":"measured"},{"denominator":71,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":71,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.10120/citation-record","integrity":"/paper/2607.10120/integrity","json":"/paper/2607.10120/citation-record.json","paper":"/paper/2607.10120"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":"2025.Claude 3.7 Sonnet and Claude Code","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:794954a2c834e539591fea24dc2c649a2f8186d758f71e0f41b2a966a35c1767","observation_id":"59f7e08c-a79c-415c-ba7f-777085d2c32c","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.21631","last_updated":"2025-11-27T12:16:54Z","snapshot_observed_at":"2026-08-11T14:42:37.584016Z","submitted_at":"2025-11-26T17:59:08Z","title":"Qwen3-VL Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.21631","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2511.21631","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:d33bc773f01f0e3b286b2fb1b528959cf60780d26f5b580d7c869d450b485cd6","observation_id":"2dbd7b9d-471a-4cdf-8a6e-f140143e83ee","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.13923","last_updated":"2025-02-19T18:00:14Z","snapshot_observed_at":"2026-08-14T04:17:22.593941Z","submitted_at":"2025-02-19T18:00:14Z","title":"Qwen2.5-VL Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.13923","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2502.13923","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:58fdcc971ea9d108b5d46de6a5a590410b5dea6eb3c6b57abca1c17aa9537b0e","observation_id":"2db374fd-cfbf-484b-9b61-456e6e004349","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:c68aa99f436f26c6a13ae672623cc79e26f080c2152fe3e697454e32d219a0ce","observation_id":"ab9122a9-089d-4109-9ffb-cdc75f0f4c6c","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.11310","last_updated":"2026-04-10T18:50:16Z","snapshot_observed_at":"2026-08-15T05:39:28.042444Z","submitted_at":"2026-01-16T14:06:46Z","title":"Context-Aware Semantic Segmentation via Stage-Wise Attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.11310","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2601.11310","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:74f300b35a09c9b863bbaa7a5a81ea4f0043151f81b7f8fcbe8322078b0bf187","observation_id":"f82a103b-b458-4e63-a1a2-a89b7b3e7a1d","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07847","last_updated":"2025-06-09T15:09:49Z","snapshot_observed_at":"2026-08-14T13:40:28.642788Z","submitted_at":"2025-06-09T15:09:49Z","title":"F2Net: A Frequency-Fused Network for Ultra-High Resolution Remote Sensing Segmentation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07847","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2506.07847","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:b050a3a9c8d8d2371dd942ad34f484d75a82eaaf39d988b7eba76f716f49e28d","observation_id":"28cd82e5-b6df-4e03-a3c9-37727bbf2c83","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:6e3617d0b418f1b28478307fd7fa4071147f28da4ce01e857a1d1513a6bd0e3c","observation_id":"baa33ae6-0bda-4423-bb4e-fb1604578df6","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:4839971ec56d94d1df313372cffb633899d8f76a38d955f0f53fcfa0701d3cfa","observation_id":"78c7a61b-1e9f-468c-83b1-a7e44d5577ac","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:5481c84fc5f8b8e8a60884fb29c020a1733cd7f96cc5fbc05e1732e049d778b0","observation_id":"f902a8eb-3382-4266-90bb-cf20d3ea30a3","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.16500","last_updated":"2024-08-29T12:59:12Z","snapshot_observed_at":"2026-08-11T07:53:33.804680Z","submitted_at":"2024-08-29T12:59:12Z","title":"CogVLM2: Visual Language Models for Image and Video Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.16500","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2408.16500","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:76392230ca32b5ac493cd8820382670dccdc34393b69652fa00d6b25f3cc6377","observation_id":"0e3f8377-2e3d-4766-97cc-b1d086c7d0c0","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15266","last_updated":"2023-07-28T02:23:35Z","snapshot_observed_at":"2026-08-16T15:12:38.693695Z","submitted_at":"2023-07-28T02:23:35Z","title":"RSGPT: A Remote Sensing Vision Language Model and Benchmark","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15266","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2307.15266","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:a9106a2b649ba2d083f93f1eacb4dcd92f4c8abece7cfcb965f4266e40935a23","observation_id":"702be6a7-ebeb-4edb-9787-cc5e61a184f9","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:4e10df91d3979b451579304396099844ac9e370437c142ee28cfcf7e99407309","observation_id":"ab7de4ba-0fa6-4cbf-b471-76149cb1f6f4","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":"InProceedings of the 32nd ACM International Conference on Multimedia (ACM MM)","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:61d0b8b1cd7f1ab1ce489665b635c2e5985c74060523713a860e0d65c235e458","observation_id":"4c099494-ee6c-482a-8074-2c5c63adda2e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:c463460e1ee813cb424ee2aa78431596bef7bf1648b9bec4a93b365b160dbf4e","observation_id":"f4875e30-b956-4d62-b8f6-36cd3f18541e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:ef7e68b6554e863ab3dd220b2b69a9c2e65cf699fb1700a6f5d0056233715451","observation_id":"5fd23513-e502-4364-a8b9-593c8ee2ba93","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:e43a96f59f934176aee7205052337b8f5c96b195db823c10055a956221579c86","observation_id":"9e243c1e-ff3c-48f3-86ac-7fa37877778e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.03326","last_updated":"2024-10-26T16:35:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-08-06T17:59:44Z","title":"LLaVA-OneVision: Easy Visual Task Transfer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.03326","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2408.03326","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:c75199a43c3578e9541e06acef64ad2d790a02cc1736c43cd10148014f9e754f","observation_id":"8d598f3e-4b6b-456a-b9bb-bd6194417fa6","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:830960c50b64f3a0c73cc5674e25623fd1797cf4cf38fbad4033740b91fbc181","observation_id":"a3a566f8-cc48-4e41-be88-c02549555662","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:2f799f2612ff1abf7621c753e1c090469eff1bf9ae2ab621962b10d60813b685","observation_id":"16caf778-6078-4d9a-a51c-b056a807c90c","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:0b81b8b03a6ffc2a21d68e09e40fcc0bc6756adb619923ad2837bf91e98d1204","observation_id":"59f39403-f086-4a27-b99c-676f2b078591","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:0fdbaae2b0efcd6449c128dd61342ac439d1fdc5b34c8b46729eeac5dddd12ab","observation_id":"1d6840f0-2d77-4bce-b06c-937094505431","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:5ed293bf44e88e03a0025bade4350f0430d999a3b42d2cfc3cb92bd50bda2128","observation_id":"e101a9c9-0463-48d0-a262-035a35379d7f","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:dba1eeca21ba3935c932f0ebf9435995b74ddf87913638298309dac9e3a9a1f6","observation_id":"9c76a625-9090-4429-8f69-0fda029c224e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:ca9438c6e7f7bf9cb36930f68836cb943ff65c2ff6366d5da8c91f6abb93dfcc","observation_id":"6c8642e1-db14-45ad-9ad8-3ace042973cd","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":"2024.LLaV A-NeXT: Improved reasoning, OCR, and world knowledge","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:124ab001b7016bd1084d6a9152c4ce449a633594226088b2f4154f1d640f23e0","observation_id":"9c934673-2ea7-4372-ac39-4d436e22df83","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:dc4c77747dc516ab4f8f7af774b8ce9b8da7bd48ee4a8d35499add7795456a30","observation_id":"dfb6c88d-bbfc-4165-84b3-25129a9c305e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:ef48aa21f8ac65ac87c73cdc4fa7ba1572b413266777965e0c9ce80924477efd","observation_id":"5b065fbf-6860-4db2-b407-deb01e2dfc3f","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:af6e73048513602979762da1169a945e720471cb38b76cec50c4fc06278e6586","observation_id":"5e9ec869-27bf-4463-a5ee-ca6a3b945ca5","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:c507348547a447dbd8b44e5e161b27f87003d80553a58664faa96842c897874b","observation_id":"1c6db654-e5dc-4d0f-a055-29731d6f73a8","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:a06f3e7f4a570d98c83e7f242abefb4c1c25edbe92687cd8612166e13f9526f7","observation_id":"48b773db-7e52-467a-b879-324ba8eae6cd","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.05679","last_updated":"2024-12-10T02:23:30Z","snapshot_observed_at":"2026-08-15T18:52:04.714439Z","submitted_at":"2024-12-07T15:11:21Z","title":"RSUniVLM: A Unified Vision Language Model for Remote Sensing via Granularity-oriented Mixture of Experts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.05679","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2412.05679","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:b99e89ca738b86690b3ada162f37b5ec321746fc3e1da59af808f6b29723614a","observation_id":"a4009fd8-4c32-408a-933c-a8c1654a3083","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:15293d492d30f76649312ce67f6ffd97bd649fd9a95a08ab1c20ca43430bc94a","observation_id":"4407ad47-b71b-44a4-875d-e39183c6e8a0","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.07588","last_updated":"2025-07-24T07:44:45Z","snapshot_observed_at":"2026-08-16T12:51:24.763215Z","submitted_at":"2025-03-10T17:51:16Z","title":"When Large Vision-Language Model Meets Large Remote Sensing Imagery: Coarse-to-Fine Text-Guided Token Pruning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.07588","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2503.07588","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:3a2143c036ae1d2e7d23709d63432fbc93424341d974ddd26c386164006bb615","observation_id":"4c4dfee1-47cf-4cfc-aeda-075996c8f3bb","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:176156278f1a6dba470420e9185ebf146e9f47b427d295e65ac7d97fdf08125e","observation_id":"b3981780-c0ea-48ce-9323-558a579a16ad","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:4e233da101b29cf5dfbfc1347f9341599d69da2c21a1d1abbf63d1ba4be57938","observation_id":"c85a6a29-f921-4aaf-bef9-fc50ba0a2467","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":"2024.GPT-4o mini: advancing cost-efficient intelligence","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:344b8292837773c297452499c823e2f61eb829ecf2da4a02708ac40edf30591e","observation_id":"c062d659-1be7-41e7-8640-c7debeea0188","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":"2024.Hello GPT -4o","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:0649d3aeecc043bd7930dc0fcb081a4bffe886e777a03ba7173befeaec12b446","observation_id":"1385d3d2-b6c0-4dc3-8c74-727007053558","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.06828","last_updated":"2025-03-13T08:16:01Z","snapshot_observed_at":"2026-08-15T13:54:43.023268Z","submitted_at":"2025-01-12T14:45:27Z","title":"GeoPix: Multi-Modal Large Language Model for Pixel-level Image Understanding in Remote Sensing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.06828","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2501.06828","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:1fa4ab7a47debafb9d0b135271f6829706ad771286a07b2e3df81c0328ec56d2","observation_id":"1b903af9-48f8-45b4-8a74-25804925781b","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:8a2abc531ca1342d87965bdb6a3db190024b1876c06ce8854f9583a271d54d28","observation_id":"40e24159-cd35-419a-976e-e969d42ca3cf","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:640d91a11a36a1370fb0f830a89442aa756d53cc465f22833a3ad26527f48307","observation_id":"d441a222-1911-4267-af1a-7c0a139a82c4","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.13925","last_updated":"2025-01-23T18:59:30Z","snapshot_observed_at":"2026-08-15T03:53:36.276667Z","submitted_at":"2025-01-23T18:59:30Z","title":"GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.13925","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":"Khan, and Salman Khan","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2501.13925","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:4d1b5c68bc6163af6db83f3881f1c1ed6549ad160d95928692416d604cde8be4","observation_id":"3d82469c-2594-4623-9476-ad8c23d7a288","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:6be74e03efdd63ed71842e3f6364b5a2c20917bf9c85b80e47edcf4c0786438f","observation_id":"9baa0608-8ea7-4cf3-8033-cc8bff7e4f3d","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2511.22396","last_updated":"2026-04-08T01:53:03Z","snapshot_observed_at":"2026-08-12T23:54:47.053124Z","submitted_at":"2025-11-27T12:19:37Z","title":"Asking like Socrates: Socrates helps VLMs understand remote sensing images","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2511.22396","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2511.22396","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:b2c961b9e87dbfac11b0bed58ed71a2b7f76d599271aeb79ca425193e69551df","observation_id":"8632ae4a-e47f-4dc1-b93c-70b999c8902b","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.16044","last_updated":"2025-09-01T02:39:51Z","snapshot_observed_at":"2026-08-15T05:00:23.895770Z","submitted_at":"2024-11-25T02:15:30Z","title":"ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.16044","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2411.16044","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:82e8c77a5fc62fd384d4eb85cad32cc91ba1424402422b0d579cfcd7b97c92fe","observation_id":"e912b465-8b2e-418b-a622-9c4be1ac5a3d","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.06475","last_updated":"2024-02-09T15:31:01Z","snapshot_observed_at":"2026-08-16T14:19:51.857539Z","submitted_at":"2024-02-09T15:31:01Z","title":"Large Language Models for Captioning and Retrieving Remote Sensing Images","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.06475","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2402.06475","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:979230cf892975c1e11931bef18e7b727ae702083b8b8812d31eb5a1c632929e","observation_id":"9055eae1-c298-4208-b2ac-8dd227ea2830","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:b6867fc9ee75672ec4d8d4b26e1373478c9489f08c5b75220fc18b44d3083c40","observation_id":"2312c5a7-c591-4092-a85b-73e16d762018","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:ae945de30d8ac668a00841b8d2f20d5ecbd9208b3ecf01daa643fefde78044b9","observation_id":"a604fac5-b479-45e4-98fa-17c5502ffb23","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:deab11151de928165bd18fcddc9202247aaaf02a35f298f1849f1296c7de8497","observation_id":"02de2905-825b-4177-8cbf-a22ba0470ae1","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:a00d86175f070a6bc1226eb6079df72d01bf28ffce6cf1de2a79b005aca67d44","observation_id":"e3793497-f7dc-4971-a5df-8d656696534b","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:77de5796f873ba6e4ee5cf2515abbc47ab5799809a1162c15d0352faa73f3ed5","observation_id":"73567441-f554-45e1-96f8-2c8e1cb05e71","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:ef43ecdc206a171e8ed9f3ab6bf0cb543402f986d711467e711647e86dff34ee","observation_id":"7d85db21-4a09-49f9-b675-bb0ecdf9cac6","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:dfdbf4b4f1fc7db7898712113215a0934eec2ee9a6bed47c002b9697d044d593","observation_id":"336dfaf6-bef7-49b8-91b7-f0291d0663a4","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:147e43b6b2fbda1cc6aef0ac3513179831e25ad846568a4a19c6a1f2cac60231","observation_id":"b3c5b54d-d985-4cbf-a6c3-52dff3314fe1","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:1780229d50b753213d052d2589b08005c000f9edafd71451f9b47e0e82c93c94","observation_id":"ffc7ba69-e343-4229-b620-4d6fd5e4b629","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.04451","last_updated":"2026-08-07T02:21:45Z","snapshot_observed_at":"2026-08-13T14:56:42.822561Z","submitted_at":"2026-05-06T03:25:41Z","title":"RemoteZero: Geospatial Reasoning with Zero Labels","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.04451","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2605.04451","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:0f0012509e49d88cd56fd4d038ea4b0f5509dda12dc1a92163f2414c589942de","observation_id":"257aab3f-e3d8-43a8-b14f-e74b6d2a7110","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.07765","last_updated":"2026-04-12T05:49:10Z","snapshot_observed_at":"2026-08-14T11:13:27.928398Z","submitted_at":"2026-04-09T03:40:46Z","title":"RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.07765","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2604.07765","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:5cfd0af02acf48571df7b16f22cf70d8dafa029eb75c410d0d2156e245984e29","observation_id":"f005c587-d693-436b-9849-285692d85aa1","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:d5c1be059f758caaa7cbb589749233c729d2d4edc7a854080c72fa66e158ba4a","observation_id":"4ff6cba4-5e67-444e-9ea6-e64e91a8791e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:16be766b7de50008659b170ada10a6c89f005fd80eeb05b33d7fab3e64c980e5","observation_id":"e7050ac9-a06f-4ea1-bf7b-ebff9e127514","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.03320","last_updated":"2024-07-03T17:59:21Z","snapshot_observed_at":"2026-08-14T16:53:05.474750Z","submitted_at":"2024-07-03T17:59:21Z","title":"InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.03320","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2407.03320","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:9239f53d69f584f6e6fd573696ef6463762d1268e2c235951c458fa94ee407ce","observation_id":"8e167f3e-02fe-4f42-871f-3923a118714e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:8fd36c4e3d729fb1031e9641a437b36890f1dcebbfd560415607b72d559ce3ab","observation_id":"cc0ce847-2512-466d-a411-aa1a5b0909da","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:e14059ec251404fb4a4c67b1a274e6c3713a78e3c98262086ac1d02065ce701c","observation_id":"b4ad68c6-0e4d-4c3b-80fc-30887963948a","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:74570bd0a18ad0be587d5d3d5bca87905fedf42ab54a0e6df3deb529f48412d7","observation_id":"e74b4dd7-d35c-4a52-9a27-46348664a88d","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2512.19302","last_updated":"2026-04-21T02:16:23Z","snapshot_observed_at":"2026-08-15T04:45:08.201421Z","submitted_at":"2025-12-22T11:46:42Z","title":"Bridging Semantics and Geometry: A Decoupled LVLM-SAM Framework for Reasoning Segmentation in Optical Remote Sensing","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2512.19302","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2512.19302","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:5c5b884982b8b1d9484208ec23ecdc12a784b3daa6941d2b892c0ab5db172607","observation_id":"ebcbd825-0e8b-4a7e-b1ec-ee21ced0ed41","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.13257","last_updated":"2025-02-05T08:44:02Z","snapshot_observed_at":"2026-08-13T00:35:03.634903Z","submitted_at":"2024-08-23T17:59:51Z","title":"MME-RealWorld: Could Your Multimodal LLM Challenge High-Resolution Real-World Scenarios that are Difficult for Humans?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.13257","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2408.13257","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:b6ce082a0de04781a9c0bee3d706f706ec6f958b08a4782bed9cae210676bf51","observation_id":"289129be-1ae5-4590-8b6c-4bcd44eca537","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2509.21976","last_updated":"2026-04-23T16:06:02Z","snapshot_observed_at":"2026-08-05T07:42:29.124541Z","submitted_at":"2025-09-26T07:01:12Z","title":"Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2509.21976","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2509.21976","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:22fdd3e33126c28e34c474cd844dade0a62745af414b3efcc72352e9d3e6acc9","observation_id":"f62214d7-32a7-42d4-9cc1-10cec4990bfa","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:3b47d3f1aaf87c6f520428167e0fee60935876c9ab90dbfe5641ee828fa0a8d3","observation_id":"5440a71b-9aaf-42ed-8a93-47bdca596967","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:10a1d0ab523b7513578984ea234987ff1683a2698042b43313011fd53f290c25","observation_id":"47e2fb85-77a2-48d0-8958-dff8f30fbff6","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:69ff71384179ff3815814659bf84d83362d965b9a5e422e2787a49f6b853b681","observation_id":"cf57de2b-3eb9-45a3-b19f-f89e0dffdf3f","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:c07b557b5a0804062cced59b5232e7377282feb055bbd9cc12b9fdb11bbc35cb","observation_id":"56b6163b-73cb-4fa8-bca7-08ad93fd5616","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:34dc6719c7ce47857fe0ca908d17b700183bb62ba478b3d6df1cfd381341123d","observation_id":"bf695797-2c95-46ed-b52d-f489ea90b09e","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.10479","last_updated":"2025-04-19T03:47:21Z","snapshot_observed_at":"2026-08-16T11:21:33.397874Z","submitted_at":"2025-04-14T17:59:25Z","title":"InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.10479","snapshot_observed_at":"2026-07-14T14:09:30.395518Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-07-14T14:09:30.395518Z"},"links":{"cited_paper":"/paper/2504.10479","citing_paper":"/paper/2607.10120"},"observation_digest":"sha256:09779abb399cd6755afb3b87bae2432b1ce147ab611d6c3204acf362c598e936","observation_id":"ebca0a8b-9c4f-46d3-952e-96939de8b3de","resolution":{"observed_at":"2026-07-14T14:09:30.395518Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.10120","last_updated":"2026-07-11T04:56:27Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-14T16:05:42.515947Z","submitted_at":"2026-07-11T04:56:27Z","title":"WeaveEarth: Structured Evidence Construction and Reasoning for Training-Free UHR Remote Sensing Understanding"},"reference_resolution":{"displayed":71,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":71,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":71},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2607.10120."}