{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CK7MCERNWBINMH2GTTZQ3DAH4X","short_pith_number":"pith:CK7MCERN","schema_version":"1.0","canonical_sha256":"12bec1122db050d61f469cf30d8c07e5da5a6069da485cb488a44a1b178bf812","source":{"kind":"arxiv","id":"2503.13952","version":2},"attestation_state":"computed","paper":{"title":"SimWorld: A Unified Benchmark for Simulator-Conditioned Scene Generation via World Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nanxin Zeng, Qingyu Xie, Ruiqi Song, Xinqing Li, Ye Wu, Yunfeng Ai","submitted_at":"2025-03-18T06:41:02Z","abstract_excerpt":"With the rapid advancement of autonomous driving technology, a lack of data has become a major obstacle to enhancing perception model accuracy. Researchers are now exploring controllable data generation using world models to diversify datasets. However, previous work has been limited to studying image generation quality on specific public datasets. There is still relatively little research on how to build data generation engines for real-world application scenes to achieve large-scale data generation for challenging scenes. In this paper, a simulator-conditioned scene generation engine based o"},"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":"2503.13952","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-03-18T06:41:02Z","cross_cats_sorted":[],"title_canon_sha256":"377a5f346b37172a0f7b8221a91c9fa4b221c210b1b1e42a789705a629bad20c","abstract_canon_sha256":"834fb2605d8429207ee4947a960b84556405a2d786919a64ff55fe1a6b6eea1a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:27:26.742779Z","signature_b64":"VFRMqcftkBWDXgCb2aqLJENIASVY45zGstbnhBRdyqGPv3QrQAQnt10Gh+o3jWEkj2+9NICaXH3VERkC5Sw0Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12bec1122db050d61f469cf30d8c07e5da5a6069da485cb488a44a1b178bf812","last_reissued_at":"2026-07-05T11:27:26.742216Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:27:26.742216Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SimWorld: A Unified Benchmark for Simulator-Conditioned Scene Generation via World Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nanxin Zeng, Qingyu Xie, Ruiqi Song, Xinqing Li, Ye Wu, Yunfeng Ai","submitted_at":"2025-03-18T06:41:02Z","abstract_excerpt":"With the rapid advancement of autonomous driving technology, a lack of data has become a major obstacle to enhancing perception model accuracy. Researchers are now exploring controllable data generation using world models to diversify datasets. However, previous work has been limited to studying image generation quality on specific public datasets. There is still relatively little research on how to build data generation engines for real-world application scenes to achieve large-scale data generation for challenging scenes. In this paper, a simulator-conditioned scene generation engine based o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.13952","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/2503.13952/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":"2503.13952","created_at":"2026-07-05T11:27:26.742271+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.13952v2","created_at":"2026-07-05T11:27:26.742271+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.13952","created_at":"2026-07-05T11:27:26.742271+00:00"},{"alias_kind":"pith_short_12","alias_value":"CK7MCERNWBIN","created_at":"2026-07-05T11:27:26.742271+00:00"},{"alias_kind":"pith_short_16","alias_value":"CK7MCERNWBINMH2G","created_at":"2026-07-05T11:27:26.742271+00:00"},{"alias_kind":"pith_short_8","alias_value":"CK7MCERN","created_at":"2026-07-05T11:27:26.742271+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.08006","citing_title":"Dreamland: Controllable World Creation with Simulator and Generative Models","ref_index":18,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X","json":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X.json","graph_json":"https://pith.science/api/pith-number/CK7MCERNWBINMH2GTTZQ3DAH4X/graph.json","events_json":"https://pith.science/api/pith-number/CK7MCERNWBINMH2GTTZQ3DAH4X/events.json","paper":"https://pith.science/paper/CK7MCERN"},"agent_actions":{"view_html":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X","download_json":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X.json","view_paper":"https://pith.science/paper/CK7MCERN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.13952&json=true","fetch_graph":"https://pith.science/api/pith-number/CK7MCERNWBINMH2GTTZQ3DAH4X/graph.json","fetch_events":"https://pith.science/api/pith-number/CK7MCERNWBINMH2GTTZQ3DAH4X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X/action/storage_attestation","attest_author":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X/action/author_attestation","sign_citation":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X/action/citation_signature","submit_replication":"https://pith.science/pith/CK7MCERNWBINMH2GTTZQ3DAH4X/action/replication_record"}},"created_at":"2026-07-05T11:27:26.742271+00:00","updated_at":"2026-07-05T11:27:26.742271+00:00"}