{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ULU5XG62YJGN5OPLS3U4TUXZJP","short_pith_number":"pith:ULU5XG62","schema_version":"1.0","canonical_sha256":"a2e9db9bdac24cdeb9eb96e9c9d2f94bdd0619c50e39090b59e2be12db544032","source":{"kind":"arxiv","id":"2606.12657","version":1},"attestation_state":"computed","paper":{"title":"TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB","cs.RO"],"primary_cat":"cs.AI","authors_text":"Khurram Shafique, Lingyi Zhao, Li Xiong, Siyu Li, Toan Tran","submitted_at":"2026-06-10T20:32:52Z","abstract_excerpt":"Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation. Existing LLM-based generators typically rely on either prompt engineering, which preserves zero-shot reasoning but lacks fine-grained spatiotemporal grounding, or trajectory-level fine-tuning, which improves statistical precision but incurs substantial computational cost and may weaken general reasoning. We propose TrajGenAgent, a semantic-aware hierarchical LLM-agent frame"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2606.12657","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-06-10T20:32:52Z","cross_cats_sorted":["cs.DB","cs.RO"],"title_canon_sha256":"0405e065767191edeaa042adefbb614b1780c2062fa494bae3e19fb6e0d899b5","abstract_canon_sha256":"7f51e0a3ddb70e8875c7f1d3fdcd470a999ac80134a1a887cc9ccb397f54aeea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:08:43.043498Z","signature_b64":"tuzgU8L7nR3bbUfq3wVWwiNS+DtZqxduP29OWZh8ccvi5yNvqApfPdLDjLInXvRUHtHqlNgsg9ff6o/RkV7FBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a2e9db9bdac24cdeb9eb96e9c9d2f94bdd0619c50e39090b59e2be12db544032","last_reissued_at":"2026-06-12T01:08:43.042453Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:08:43.042453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TrajGenAgent: A Hierarchical LLM Agent for Human Mobility Trajectory Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.DB","cs.RO"],"primary_cat":"cs.AI","authors_text":"Khurram Shafique, Lingyi Zhao, Li Xiong, Siyu Li, Toan Tran","submitted_at":"2026-06-10T20:32:52Z","abstract_excerpt":"Human mobility data is important for transportation, urban planning, and epidemic control, but large-scale trajectory collection is often costly and privacy-constrained, motivating realistic synthetic trajectory generation. Existing LLM-based generators typically rely on either prompt engineering, which preserves zero-shot reasoning but lacks fine-grained spatiotemporal grounding, or trajectory-level fine-tuning, which improves statistical precision but incurs substantial computational cost and may weaken general reasoning. We propose TrajGenAgent, a semantic-aware hierarchical LLM-agent frame"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12657","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/2606.12657/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.12657","created_at":"2026-06-12T01:08:43.042597+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.12657v1","created_at":"2026-06-12T01:08:43.042597+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12657","created_at":"2026-06-12T01:08:43.042597+00:00"},{"alias_kind":"pith_short_12","alias_value":"ULU5XG62YJGN","created_at":"2026-06-12T01:08:43.042597+00:00"},{"alias_kind":"pith_short_16","alias_value":"ULU5XG62YJGN5OPL","created_at":"2026-06-12T01:08:43.042597+00:00"},{"alias_kind":"pith_short_8","alias_value":"ULU5XG62","created_at":"2026-06-12T01:08:43.042597+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP","json":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP.json","graph_json":"https://pith.science/api/pith-number/ULU5XG62YJGN5OPLS3U4TUXZJP/graph.json","events_json":"https://pith.science/api/pith-number/ULU5XG62YJGN5OPLS3U4TUXZJP/events.json","paper":"https://pith.science/paper/ULU5XG62"},"agent_actions":{"view_html":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP","download_json":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP.json","view_paper":"https://pith.science/paper/ULU5XG62","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.12657&json=true","fetch_graph":"https://pith.science/api/pith-number/ULU5XG62YJGN5OPLS3U4TUXZJP/graph.json","fetch_events":"https://pith.science/api/pith-number/ULU5XG62YJGN5OPLS3U4TUXZJP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP/action/storage_attestation","attest_author":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP/action/author_attestation","sign_citation":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP/action/citation_signature","submit_replication":"https://pith.science/pith/ULU5XG62YJGN5OPLS3U4TUXZJP/action/replication_record"}},"created_at":"2026-06-12T01:08:43.042597+00:00","updated_at":"2026-06-12T01:08:43.042597+00:00"}