{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:4IRA72SI6RHGILOXVDGQOE4M3A","short_pith_number":"pith:4IRA72SI","canonical_record":{"source":{"id":"2506.05981","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-06T11:01:21Z","cross_cats_sorted":[],"title_canon_sha256":"d46920a2e5413f4a85a0b59fa4c0e881c6e23597286e2867522bf6b060d11bd9","abstract_canon_sha256":"b5a810ea0e28b6050e560a30dd5885ad89eb90560961f646dfed5cc3962ce417"},"schema_version":"1.0"},"canonical_sha256":"e2220fea48f44e642dd7a8cd07138cd806a4fb11c2c749516d94642ee10ab224","source":{"kind":"arxiv","id":"2506.05981","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05981","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05981v2","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05981","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"pith_short_12","alias_value":"4IRA72SI6RHG","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"pith_short_16","alias_value":"4IRA72SI6RHGILOX","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"pith_short_8","alias_value":"4IRA72SI","created_at":"2026-07-05T11:18:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:4IRA72SI6RHGILOXVDGQOE4M3A","target":"record","payload":{"canonical_record":{"source":{"id":"2506.05981","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-06T11:01:21Z","cross_cats_sorted":[],"title_canon_sha256":"d46920a2e5413f4a85a0b59fa4c0e881c6e23597286e2867522bf6b060d11bd9","abstract_canon_sha256":"b5a810ea0e28b6050e560a30dd5885ad89eb90560961f646dfed5cc3962ce417"},"schema_version":"1.0"},"canonical_sha256":"e2220fea48f44e642dd7a8cd07138cd806a4fb11c2c749516d94642ee10ab224","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:43.584911Z","signature_b64":"aJYwnel1mBbY1V9wgoAb8G40V1+4nMD6cdtounEWZzYVZ7k6w5o9G+w7joBKLs92/K+S9A8+ePwEA3m++OK9Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e2220fea48f44e642dd7a8cd07138cd806a4fb11c2c749516d94642ee10ab224","last_reissued_at":"2026-07-05T11:18:43.584491Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:43.584491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.05981","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":"2026-07-05T11:18:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n70Peaw1D/5ilIfRsDYGbBoxylNwOrTCxyB11vo1dWBHujObnb5ngcI1rJJDj1/pSgXm9kx9yYc0gvw8DzWkBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:50:30.528462Z"},"content_sha256":"08926e44975771c4f6af1406a1878c242f7ab4f5674888b84f6601e7fe2bdb51","schema_version":"1.0","event_id":"sha256:08926e44975771c4f6af1406a1878c242f7ab4f5674888b84f6601e7fe2bdb51"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:4IRA72SI6RHGILOXVDGQOE4M3A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agents","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Fengli Xu, Haoyang Li, Jinzhu Mao, Qingbin Zeng, Ruotong Zhao, Yong Li","submitted_at":"2025-06-06T11:01:21Z","abstract_excerpt":"Modeling urban crime is an important yet challenging task that requires understanding the subtle visual, social, and cultural cues embedded in urban environments. Previous work has mainly focused on rule-based agent-based modeling (ABM) and deep learning methods. ABMs offer interpretability of internal mechanisms but exhibit limited predictive accuracy. In contrast, deep learning methods are often effective in prediction but are less interpretable and require extensive training data. Moreover, both lines of work lack the cognitive flexibility to adapt to changing environments. Leveraging the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05981","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/2506.05981/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":"2026-07-05T11:18:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tqqgnMYxpjDq26cAwbiUWppUgN1XH4QLebkPk0q0BRjlfY1ANqYf/oV/cjaNGq1qFfUgsFmRpuSy6FTFF+9aDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T07:50:30.529366Z"},"content_sha256":"7f960b740d29c68e4cdcbda2d0ac40d5f77ca687e29f99ca7d4163bd4c758a60","schema_version":"1.0","event_id":"sha256:7f960b740d29c68e4cdcbda2d0ac40d5f77ca687e29f99ca7d4163bd4c758a60"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4IRA72SI6RHGILOXVDGQOE4M3A/bundle.json","state_url":"https://pith.science/pith/4IRA72SI6RHGILOXVDGQOE4M3A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4IRA72SI6RHGILOXVDGQOE4M3A/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":"2026-08-09T07:50:30Z","links":{"resolver":"https://pith.science/pith/4IRA72SI6RHGILOXVDGQOE4M3A","bundle":"https://pith.science/pith/4IRA72SI6RHGILOXVDGQOE4M3A/bundle.json","state":"https://pith.science/pith/4IRA72SI6RHGILOXVDGQOE4M3A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4IRA72SI6RHGILOXVDGQOE4M3A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:4IRA72SI6RHGILOXVDGQOE4M3A","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":"b5a810ea0e28b6050e560a30dd5885ad89eb90560961f646dfed5cc3962ce417","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-06T11:01:21Z","title_canon_sha256":"d46920a2e5413f4a85a0b59fa4c0e881c6e23597286e2867522bf6b060d11bd9"},"schema_version":"1.0","source":{"id":"2506.05981","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05981","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05981v2","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05981","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"pith_short_12","alias_value":"4IRA72SI6RHG","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"pith_short_16","alias_value":"4IRA72SI6RHGILOX","created_at":"2026-07-05T11:18:43Z"},{"alias_kind":"pith_short_8","alias_value":"4IRA72SI","created_at":"2026-07-05T11:18:43Z"}],"graph_snapshots":[{"event_id":"sha256:7f960b740d29c68e4cdcbda2d0ac40d5f77ca687e29f99ca7d4163bd4c758a60","target":"graph","created_at":"2026-07-05T11:18:43Z","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/2506.05981/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Modeling urban crime is an important yet challenging task that requires understanding the subtle visual, social, and cultural cues embedded in urban environments. Previous work has mainly focused on rule-based agent-based modeling (ABM) and deep learning methods. ABMs offer interpretability of internal mechanisms but exhibit limited predictive accuracy. In contrast, deep learning methods are often effective in prediction but are less interpretable and require extensive training data. Moreover, both lines of work lack the cognitive flexibility to adapt to changing environments. Leveraging the c","authors_text":"Fengli Xu, Haoyang Li, Jinzhu Mao, Qingbin Zeng, Ruotong Zhao, Yong Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-06T11:01:21Z","title":"CrimeMind: Simulating Urban Crime with Multi-Modal LLM Agents"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05981","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":"sha256:08926e44975771c4f6af1406a1878c242f7ab4f5674888b84f6601e7fe2bdb51","target":"record","created_at":"2026-07-05T11:18:43Z","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":"b5a810ea0e28b6050e560a30dd5885ad89eb90560961f646dfed5cc3962ce417","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-06T11:01:21Z","title_canon_sha256":"d46920a2e5413f4a85a0b59fa4c0e881c6e23597286e2867522bf6b060d11bd9"},"schema_version":"1.0","source":{"id":"2506.05981","kind":"arxiv","version":2}},"canonical_sha256":"e2220fea48f44e642dd7a8cd07138cd806a4fb11c2c749516d94642ee10ab224","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e2220fea48f44e642dd7a8cd07138cd806a4fb11c2c749516d94642ee10ab224","first_computed_at":"2026-07-05T11:18:43.584491Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:18:43.584491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aJYwnel1mBbY1V9wgoAb8G40V1+4nMD6cdtounEWZzYVZ7k6w5o9G+w7joBKLs92/K+S9A8+ePwEA3m++OK9Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:18:43.584911Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.05981","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:08926e44975771c4f6af1406a1878c242f7ab4f5674888b84f6601e7fe2bdb51","sha256:7f960b740d29c68e4cdcbda2d0ac40d5f77ca687e29f99ca7d4163bd4c758a60"],"state_sha256":"3cdb977f2c06828acee6546d2bf950a8ec1a49b5a367a24cc006e3cb0f0e92db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JW/8UC0DgrOP4ZG+TGc0ayB5yQLfwe+CA349rclVfSbXwHmFXKEhZW1vdRDCVyXYGkIAkimsJltEMNDyyOD3Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T07:50:30.535436Z","bundle_sha256":"f0b55baff29a46ec2801e07672c4da44254c392b0eb9a2c279e03adf899083d8"}}