{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5PSTYZAHL5NPMDTBJPIPVXFGMG","short_pith_number":"pith:5PSTYZAH","schema_version":"1.0","canonical_sha256":"ebe53c64075f5af60e614bd0fadca66184f1c636140a54bcdf235560ac3731fa","source":{"kind":"arxiv","id":"2512.10958","version":2},"attestation_state":"computed","paper":{"title":"WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ao Liang, Benoit R. Cottereau, Changxin Gao, Dekai Zhu, Dongyue Lu, Guangfeng Jiang, Hongsi Liu, Jialong Zuo, Lai Xing Ng, Liang Pan, Linfeng Li, Lingdong Kong, Long Zhuo, Tianyi Yan, Wei Tsang Ooi, Wei Yin, Wesley Yang, Xiangtai Li, Yixuan Hu, Youquan Liu, Ziqi Huang, Ziwei Liu","submitted_at":"2025-12-11T18:59:58Z","abstract_excerpt":"Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. Despite rapid progress, the field still lacks a unified way to assess whether generated worlds preserve geometry, obey physics, or support reliable control. We introduce WorldLens, a full-spectrum benchmark evaluating how well a model builds, understands, and behaves within its generated world. It spans five aspects -- Generation, Reconstruction, Action-Following, Downstream Task, and Human Preference -- jointly cover"},"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":"2512.10958","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-12-11T18:59:58Z","cross_cats_sorted":[],"title_canon_sha256":"77131c5e3b37b0daf10175da347e9829b7f496567b3a76d7c06cb42837f19117","abstract_canon_sha256":"470e6a6ded407ce7375b29fa5898b0f89a69a7d1b832327a3f57556479128fa9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T03:05:04.534673Z","signature_b64":"qyd6N3nAD+b7O6CrDBmcdXS7CEHx95xLGwnUBDkhj7P0ZmwKCFcPXjau1wZXGl0ekOAkJmNcuSelNBkcoM0sAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ebe53c64075f5af60e614bd0fadca66184f1c636140a54bcdf235560ac3731fa","last_reissued_at":"2026-06-02T03:05:04.534164Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T03:05:04.534164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ao Liang, Benoit R. Cottereau, Changxin Gao, Dekai Zhu, Dongyue Lu, Guangfeng Jiang, Hongsi Liu, Jialong Zuo, Lai Xing Ng, Liang Pan, Linfeng Li, Lingdong Kong, Long Zhuo, Tianyi Yan, Wei Tsang Ooi, Wei Yin, Wesley Yang, Xiangtai Li, Yixuan Hu, Youquan Liu, Ziqi Huang, Ziwei Liu","submitted_at":"2025-12-11T18:59:58Z","abstract_excerpt":"Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. Despite rapid progress, the field still lacks a unified way to assess whether generated worlds preserve geometry, obey physics, or support reliable control. We introduce WorldLens, a full-spectrum benchmark evaluating how well a model builds, understands, and behaves within its generated world. It spans five aspects -- Generation, Reconstruction, Action-Following, Downstream Task, and Human Preference -- jointly cover"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2512.10958","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/2512.10958/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":"2512.10958","created_at":"2026-06-02T03:05:04.534224+00:00"},{"alias_kind":"arxiv_version","alias_value":"2512.10958v2","created_at":"2026-06-02T03:05:04.534224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2512.10958","created_at":"2026-06-02T03:05:04.534224+00:00"},{"alias_kind":"pith_short_12","alias_value":"5PSTYZAHL5NP","created_at":"2026-06-02T03:05:04.534224+00:00"},{"alias_kind":"pith_short_16","alias_value":"5PSTYZAHL5NPMDTB","created_at":"2026-06-02T03:05:04.534224+00:00"},{"alias_kind":"pith_short_8","alias_value":"5PSTYZAH","created_at":"2026-06-02T03:05:04.534224+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.13815","citing_title":"OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation","ref_index":5,"is_internal_anchor":true},{"citing_arxiv_id":"2605.08712","citing_title":"From Articulated Kinematics to Routed Visual Control for Action-Conditioned Surgical Video Generation","ref_index":47,"is_internal_anchor":true},{"citing_arxiv_id":"2604.16592","citing_title":"Human Cognition in Machines: A Unified Perspective of World Models","ref_index":106,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG","json":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG.json","graph_json":"https://pith.science/api/pith-number/5PSTYZAHL5NPMDTBJPIPVXFGMG/graph.json","events_json":"https://pith.science/api/pith-number/5PSTYZAHL5NPMDTBJPIPVXFGMG/events.json","paper":"https://pith.science/paper/5PSTYZAH"},"agent_actions":{"view_html":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG","download_json":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG.json","view_paper":"https://pith.science/paper/5PSTYZAH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2512.10958&json=true","fetch_graph":"https://pith.science/api/pith-number/5PSTYZAHL5NPMDTBJPIPVXFGMG/graph.json","fetch_events":"https://pith.science/api/pith-number/5PSTYZAHL5NPMDTBJPIPVXFGMG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG/action/storage_attestation","attest_author":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG/action/author_attestation","sign_citation":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG/action/citation_signature","submit_replication":"https://pith.science/pith/5PSTYZAHL5NPMDTBJPIPVXFGMG/action/replication_record"}},"created_at":"2026-06-02T03:05:04.534224+00:00","updated_at":"2026-06-02T03:05:04.534224+00:00"}