{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MKPF3B3AQHIJRZQZTTMTM325DI","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":"842433fc9a7b7a57cede9979b1b3942b3c8e76f315210fbb2cbcddb14a8428e7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-20T16:45:48Z","title_canon_sha256":"df66f493002f31798f1317e126149ea4e0786fe74b817b7ff3e21a889ab89465"},"schema_version":"1.0","source":{"id":"2503.16326","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.16326","created_at":"2026-07-05T10:36:17Z"},{"alias_kind":"arxiv_version","alias_value":"2503.16326v1","created_at":"2026-07-05T10:36:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.16326","created_at":"2026-07-05T10:36:17Z"},{"alias_kind":"pith_short_12","alias_value":"MKPF3B3AQHIJ","created_at":"2026-07-05T10:36:17Z"},{"alias_kind":"pith_short_16","alias_value":"MKPF3B3AQHIJRZQZ","created_at":"2026-07-05T10:36:17Z"},{"alias_kind":"pith_short_8","alias_value":"MKPF3B3A","created_at":"2026-07-05T10:36:17Z"}],"graph_snapshots":[{"event_id":"sha256:09bfe620611e1e57a35d65c245faf47c61749391170a7306c08dcd01d947bf2f","target":"graph","created_at":"2026-07-05T10:36:17Z","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/2503.16326/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types such as text, images, and spatial information. In this paper, we explore the potential of multimodal LLMs (MLLM) for geospatial artificial intelligence (GeoAI), a field that leverages spatial data to address challenges in domains including Geospatial Semantics, Health Geography, Urban Geography, Urban Perception, and Remote Sensing. We propose a MLLM (OmniGeo) tailored to geospatial applications, capable of processing a","authors_text":"Fengran Mo, Jian-Yun Nie, Jinan Xu, Kaiyu Huang, Long Yuan, Wangyuxuan Zhai, Wenjie Wang, Xiaoyu Zhu, You Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-20T16:45:48Z","title":"OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.16326","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:df919123f4317f129e71299975015c40423b6245fcfc879ed6d91ca424c3c3ea","target":"record","created_at":"2026-07-05T10:36:17Z","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":"842433fc9a7b7a57cede9979b1b3942b3c8e76f315210fbb2cbcddb14a8428e7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-03-20T16:45:48Z","title_canon_sha256":"df66f493002f31798f1317e126149ea4e0786fe74b817b7ff3e21a889ab89465"},"schema_version":"1.0","source":{"id":"2503.16326","kind":"arxiv","version":1}},"canonical_sha256":"629e5d876081d098e6199cd9366f5d1a05d85f6ddab164718c6892cade12dab0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"629e5d876081d098e6199cd9366f5d1a05d85f6ddab164718c6892cade12dab0","first_computed_at":"2026-07-05T10:36:17.094673Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:36:17.094673Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6QwAIm0cS8mEXmY+om7vtIOVfItbYvagCkGcqPxmKTwzbiWB9Y4Ke0KZrNh23CTFIzBQU1IJhjuO8fVHBgH5Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:36:17.095202Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.16326","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:df919123f4317f129e71299975015c40423b6245fcfc879ed6d91ca424c3c3ea","sha256:09bfe620611e1e57a35d65c245faf47c61749391170a7306c08dcd01d947bf2f"],"state_sha256":"46858c16669da483b6b697702fc38fdec9a5dc70d3afa885cc54ecb9fee1fe80"}