{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:AK2SCD367I4AB22UYEV2L3IJH5","short_pith_number":"pith:AK2SCD36","canonical_record":{"source":{"id":"2406.13948","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-06-20T02:32:16Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"f4f9714c0cd66545e55bd51a323ac3e4f2c4a7f660e637f376212b4d71f623ca","abstract_canon_sha256":"09f6d950957811f4bbb99c5af27a14ff32075d1a6b2696c962b15cc72dfdd7d0"},"schema_version":"1.0"},"canonical_sha256":"02b5210f7efa3800eb54c12ba5ed093f7a30d0ae610fa71274a2a1afd887104b","source":{"kind":"arxiv","id":"2406.13948","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.13948","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"arxiv_version","alias_value":"2406.13948v2","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13948","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"pith_short_12","alias_value":"AK2SCD367I4A","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"pith_short_16","alias_value":"AK2SCD367I4AB22U","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"pith_short_8","alias_value":"AK2SCD36","created_at":"2026-07-05T11:13:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:AK2SCD367I4AB22UYEV2L3IJH5","target":"record","payload":{"canonical_record":{"source":{"id":"2406.13948","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-06-20T02:32:16Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"f4f9714c0cd66545e55bd51a323ac3e4f2c4a7f660e637f376212b4d71f623ca","abstract_canon_sha256":"09f6d950957811f4bbb99c5af27a14ff32075d1a6b2696c962b15cc72dfdd7d0"},"schema_version":"1.0"},"canonical_sha256":"02b5210f7efa3800eb54c12ba5ed093f7a30d0ae610fa71274a2a1afd887104b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:10.812067Z","signature_b64":"ui77S2UyqC3SDVbieiVwu68arG3weSJS+6y4X16e3Z5WeS/9SBJDaOT2o1R+SC2biLc0PjO/qLw6XxO3odkUAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02b5210f7efa3800eb54c12ba5ed093f7a30d0ae610fa71274a2a1afd887104b","last_reissued_at":"2026-07-05T11:13:10.811516Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:10.811516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.13948","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:13:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yUxY2mgbespGvtZjW9zuxT2oXKOxeDdpNOQjePj8hDAwf8zVgCqloSUKO51GCCuG+vDg0/iuADEBbeEH4nqaDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T06:28:59.823567Z"},"content_sha256":"46b83fa5fb09aa5411046c115c0e899f007259a5a1cfb5138e76f36685d6ea6b","schema_version":"1.0","event_id":"sha256:46b83fa5fb09aa5411046c115c0e899f007259a5a1cfb5138e76f36685d6ea6b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:AK2SCD367I4AB22UYEV2L3IJH5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CityGPT: Empowering Urban Spatial Cognition of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"cs.AI","authors_text":"Jie Feng, Siqi Guo, Tianhui Liu, Yong Li, Yuming Lin, Yuwei Du","submitted_at":"2024-06-20T02:32:16Z","abstract_excerpt":"Large language models(LLMs), with their powerful language generation and reasoning capabilities, have already achieved notable success in many domains, e.g., math and code generation. However, they often fall short when tackling real-life geospatial tasks within urban environments. This limitation stems from a lack of physical world knowledge and relevant data during training. To address this gap, we propose \\textit{CityGPT}, a systematic framework designed to enhance LLMs' understanding of urban space and improve their ability to solve the related urban tasks by integrating a city-scale `worl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13948","kind":"arxiv","version":2},"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/2406.13948/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":"2026-07-05T11:13:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IirWtRRNC7LA3jb4f+cugHSK+9qqxEUYGmcrXUQSwigEBf+RjJk8e5YcxzbvuQvf3U4YDLUNuXNUx4/BeomBAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T06:28:59.824521Z"},"content_sha256":"9d3c3f070a0f3aacb1fe35b88da673a761afe6e9283d36003aff47f163e40017","schema_version":"1.0","event_id":"sha256:9d3c3f070a0f3aacb1fe35b88da673a761afe6e9283d36003aff47f163e40017"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AK2SCD367I4AB22UYEV2L3IJH5/bundle.json","state_url":"https://pith.science/pith/AK2SCD367I4AB22UYEV2L3IJH5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AK2SCD367I4AB22UYEV2L3IJH5/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":"2026-08-09T06:28:59Z","links":{"resolver":"https://pith.science/pith/AK2SCD367I4AB22UYEV2L3IJH5","bundle":"https://pith.science/pith/AK2SCD367I4AB22UYEV2L3IJH5/bundle.json","state":"https://pith.science/pith/AK2SCD367I4AB22UYEV2L3IJH5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AK2SCD367I4AB22UYEV2L3IJH5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:AK2SCD367I4AB22UYEV2L3IJH5","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":"09f6d950957811f4bbb99c5af27a14ff32075d1a6b2696c962b15cc72dfdd7d0","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-06-20T02:32:16Z","title_canon_sha256":"f4f9714c0cd66545e55bd51a323ac3e4f2c4a7f660e637f376212b4d71f623ca"},"schema_version":"1.0","source":{"id":"2406.13948","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.13948","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"arxiv_version","alias_value":"2406.13948v2","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13948","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"pith_short_12","alias_value":"AK2SCD367I4A","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"pith_short_16","alias_value":"AK2SCD367I4AB22U","created_at":"2026-07-05T11:13:10Z"},{"alias_kind":"pith_short_8","alias_value":"AK2SCD36","created_at":"2026-07-05T11:13:10Z"}],"graph_snapshots":[{"event_id":"sha256:9d3c3f070a0f3aacb1fe35b88da673a761afe6e9283d36003aff47f163e40017","target":"graph","created_at":"2026-07-05T11:13:10Z","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/2406.13948/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models(LLMs), with their powerful language generation and reasoning capabilities, have already achieved notable success in many domains, e.g., math and code generation. However, they often fall short when tackling real-life geospatial tasks within urban environments. This limitation stems from a lack of physical world knowledge and relevant data during training. To address this gap, we propose \\textit{CityGPT}, a systematic framework designed to enhance LLMs' understanding of urban space and improve their ability to solve the related urban tasks by integrating a city-scale `worl","authors_text":"Jie Feng, Siqi Guo, Tianhui Liu, Yong Li, Yuming Lin, Yuwei Du","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-06-20T02:32:16Z","title":"CityGPT: Empowering Urban Spatial Cognition of Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13948","kind":"arxiv","version":2},"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":"sha256:46b83fa5fb09aa5411046c115c0e899f007259a5a1cfb5138e76f36685d6ea6b","target":"record","created_at":"2026-07-05T11:13:10Z","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":"09f6d950957811f4bbb99c5af27a14ff32075d1a6b2696c962b15cc72dfdd7d0","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-06-20T02:32:16Z","title_canon_sha256":"f4f9714c0cd66545e55bd51a323ac3e4f2c4a7f660e637f376212b4d71f623ca"},"schema_version":"1.0","source":{"id":"2406.13948","kind":"arxiv","version":2}},"canonical_sha256":"02b5210f7efa3800eb54c12ba5ed093f7a30d0ae610fa71274a2a1afd887104b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"02b5210f7efa3800eb54c12ba5ed093f7a30d0ae610fa71274a2a1afd887104b","first_computed_at":"2026-07-05T11:13:10.811516Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:13:10.811516Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ui77S2UyqC3SDVbieiVwu68arG3weSJS+6y4X16e3Z5WeS/9SBJDaOT2o1R+SC2biLc0PjO/qLw6XxO3odkUAA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:13:10.812067Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.13948","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:46b83fa5fb09aa5411046c115c0e899f007259a5a1cfb5138e76f36685d6ea6b","sha256:9d3c3f070a0f3aacb1fe35b88da673a761afe6e9283d36003aff47f163e40017"],"state_sha256":"cefa6783d8e21a8ee85587666cf15a2e737f957fe6bf9c32e74ac9d2f90a4cbe"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XqfRADRJxaCdfMdYvPwtX62y8lhguGbtjdvP8FfYb0ezUm+/mJgX71l+hXdGc+86AjNL1W4EawKbIkIxl2aVCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T06:28:59.830358Z","bundle_sha256":"01a80632311144585f69f747a92c4f1016cf302671c95287fcfc0de58fdfb56e"}}