{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:O4NFWJLN6SFRYPFXQ3FDACS7RH","short_pith_number":"pith:O4NFWJLN","canonical_record":{"source":{"id":"2607.25993","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-28T17:09:16Z","cross_cats_sorted":[],"title_canon_sha256":"e8af1cecfe38519295c10018dc7088b8bab608da9972ae405463c023c230bcdd","abstract_canon_sha256":"12d920a953d233c8bc2e365b99537f94ef995ab9710f725fcba13c599177531b"},"schema_version":"1.0"},"canonical_sha256":"771a5b256df48b1c3cb786ca300a5f89da3bcd0a41f0720daa1c8e5668cd3258","source":{"kind":"arxiv","id":"2607.25993","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.25993","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"arxiv_version","alias_value":"2607.25993v1","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25993","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"pith_short_12","alias_value":"O4NFWJLN6SFR","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"O4NFWJLN6SFRYPFX","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"O4NFWJLN","created_at":"2026-07-29T01:26:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:O4NFWJLN6SFRYPFXQ3FDACS7RH","target":"record","payload":{"canonical_record":{"source":{"id":"2607.25993","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-28T17:09:16Z","cross_cats_sorted":[],"title_canon_sha256":"e8af1cecfe38519295c10018dc7088b8bab608da9972ae405463c023c230bcdd","abstract_canon_sha256":"12d920a953d233c8bc2e365b99537f94ef995ab9710f725fcba13c599177531b"},"schema_version":"1.0"},"canonical_sha256":"771a5b256df48b1c3cb786ca300a5f89da3bcd0a41f0720daa1c8e5668cd3258","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:26:18.020642Z","signature_b64":"BFexLZoXjOK1OH5wjuFKd4FtdI3S0Yk/BTenmJYpFIB2UUAxfvGWft3DgNTxaJfU7tx3aR/K+bPfr/inqUlkDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"771a5b256df48b1c3cb786ca300a5f89da3bcd0a41f0720daa1c8e5668cd3258","last_reissued_at":"2026-07-29T01:26:18.019779Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:26:18.019779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.25993","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T01:26:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mBrUgd4imklxCXGPjV4rqS2ly44s+L6bb60eV8kTfyCpKgIRiccuKL1iS6N0mua2hjoopl46t7nEhvWaxi/oAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:31:15.783960Z"},"content_sha256":"11bb09af0c78cf377f78476b94d4cdb38434415c70bf2131a1a3e03f578cab21","schema_version":"1.0","event_id":"sha256:11bb09af0c78cf377f78476b94d4cdb38434415c70bf2131a1a3e03f578cab21"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:O4NFWJLN6SFRYPFXQ3FDACS7RH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Zooming: Learning Multi-Tool Visual Reasoning for Ultra-High-Resolution Remote Sensing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fengxiang Wang, Haiyan Zhao, Haoyu Wang, Jiangnan Huang, Jing Zhang, Junwei Luo, Mingshuo Chen, Wenjing Yang, Yang Shi, Yansheng Li, Yueying Li","submitted_at":"2026-07-28T17:09:16Z","abstract_excerpt":"Ultra-high-resolution (UHR) remote-sensing (RS) imagery provides fine-grained Earth-observation evidence over city-scale scenes, but poses a fundamental challenge for multimodal large language models (MLLMs): task-relevant evidence is often sparse, local, and spatially dispersed across extremely large visual contexts. A natural solution is to equip MLLMs with zoom-in tools for active local inspection. However, through a pilot study on XLRS-Bench, we find that zoom-in is only partially effective: it resolves easy and medium-level tasks with locally recoverable evidence, but saturates on hard ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25993","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/2607.25993/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-29T01:26:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mZW1h2JbbEAL0DDwwPMenSgnxXE9+1MK2cpr9wXeAR/YWxGFcUsDBIgb9wBybA+rJt77baYQyGtzs58JBiHoBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T08:31:15.784885Z"},"content_sha256":"eae9d2ada03e08aae65f2658ec7737a4ded13e5efa1206804644e7296135f901","schema_version":"1.0","event_id":"sha256:eae9d2ada03e08aae65f2658ec7737a4ded13e5efa1206804644e7296135f901"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O4NFWJLN6SFRYPFXQ3FDACS7RH/bundle.json","state_url":"https://pith.science/pith/O4NFWJLN6SFRYPFXQ3FDACS7RH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O4NFWJLN6SFRYPFXQ3FDACS7RH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-04T08:31:15Z","links":{"resolver":"https://pith.science/pith/O4NFWJLN6SFRYPFXQ3FDACS7RH","bundle":"https://pith.science/pith/O4NFWJLN6SFRYPFXQ3FDACS7RH/bundle.json","state":"https://pith.science/pith/O4NFWJLN6SFRYPFXQ3FDACS7RH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O4NFWJLN6SFRYPFXQ3FDACS7RH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:O4NFWJLN6SFRYPFXQ3FDACS7RH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"12d920a953d233c8bc2e365b99537f94ef995ab9710f725fcba13c599177531b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-28T17:09:16Z","title_canon_sha256":"e8af1cecfe38519295c10018dc7088b8bab608da9972ae405463c023c230bcdd"},"schema_version":"1.0","source":{"id":"2607.25993","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.25993","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"arxiv_version","alias_value":"2607.25993v1","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25993","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"pith_short_12","alias_value":"O4NFWJLN6SFR","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"pith_short_16","alias_value":"O4NFWJLN6SFRYPFX","created_at":"2026-07-29T01:26:18Z"},{"alias_kind":"pith_short_8","alias_value":"O4NFWJLN","created_at":"2026-07-29T01:26:18Z"}],"graph_snapshots":[{"event_id":"sha256:eae9d2ada03e08aae65f2658ec7737a4ded13e5efa1206804644e7296135f901","target":"graph","created_at":"2026-07-29T01:26:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2607.25993/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Ultra-high-resolution (UHR) remote-sensing (RS) imagery provides fine-grained Earth-observation evidence over city-scale scenes, but poses a fundamental challenge for multimodal large language models (MLLMs): task-relevant evidence is often sparse, local, and spatially dispersed across extremely large visual contexts. A natural solution is to equip MLLMs with zoom-in tools for active local inspection. However, through a pilot study on XLRS-Bench, we find that zoom-in is only partially effective: it resolves easy and medium-level tasks with locally recoverable evidence, but saturates on hard ca","authors_text":"Fengxiang Wang, Haiyan Zhao, Haoyu Wang, Jiangnan Huang, Jing Zhang, Junwei Luo, Mingshuo Chen, Wenjing Yang, Yang Shi, Yansheng Li, Yueying Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-28T17:09:16Z","title":"Beyond Zooming: Learning Multi-Tool Visual Reasoning for Ultra-High-Resolution Remote Sensing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25993","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:11bb09af0c78cf377f78476b94d4cdb38434415c70bf2131a1a3e03f578cab21","target":"record","created_at":"2026-07-29T01:26:18Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"12d920a953d233c8bc2e365b99537f94ef995ab9710f725fcba13c599177531b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-28T17:09:16Z","title_canon_sha256":"e8af1cecfe38519295c10018dc7088b8bab608da9972ae405463c023c230bcdd"},"schema_version":"1.0","source":{"id":"2607.25993","kind":"arxiv","version":1}},"canonical_sha256":"771a5b256df48b1c3cb786ca300a5f89da3bcd0a41f0720daa1c8e5668cd3258","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"771a5b256df48b1c3cb786ca300a5f89da3bcd0a41f0720daa1c8e5668cd3258","first_computed_at":"2026-07-29T01:26:18.019779Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-29T01:26:18.019779Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BFexLZoXjOK1OH5wjuFKd4FtdI3S0Yk/BTenmJYpFIB2UUAxfvGWft3DgNTxaJfU7tx3aR/K+bPfr/inqUlkDw==","signature_status":"signed_v1","signed_at":"2026-07-29T01:26:18.020642Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.25993","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:11bb09af0c78cf377f78476b94d4cdb38434415c70bf2131a1a3e03f578cab21","sha256:eae9d2ada03e08aae65f2658ec7737a4ded13e5efa1206804644e7296135f901"],"state_sha256":"e5303058ef8789dcc558162493b16497156578fc23ecdb274d6d14988b1baf34"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NMz6GyOM0fRtjyEpGdl9s/OuhUJKY4o6feTE8Cm3Pzxog/xZ7vmPnQQKK/sOymdLodfbSJrNVUl+W05gZwVKCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T08:31:15.792226Z","bundle_sha256":"a28cfd05c88a8d36252cc1d8b5f91be63523315f03355591a1196361aaf45546"}}