{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LTJPSQENT5HHT5ZZL6GIQWPGBB","short_pith_number":"pith:LTJPSQEN","schema_version":"1.0","canonical_sha256":"5cd2f9408d9f4e79f7395f8c8859e60861af57d0a44f69a006aae337ba69f3a6","source":{"kind":"arxiv","id":"2601.20540","version":1},"attestation_state":"computed","paper":{"title":"Advancing Open-source World Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"LingBot-World is an open-source world simulator from video generation that claims high fidelity across environments, minute-scale temporal consistency, and sub-second latency at 16 frames per second.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hanlin Wang, Hao Ouyang, Jiapeng Zhu, Jiayi Zhu, Jie Liu, Jingye Chen, Ka Leong Cheng, Kecheng Zheng, Qingyan Bai, Qiuyu Wang, Robbyant Team: Zelin Gao, Shuailei Ma, Xing Zhu, Yanhong Zeng, Yansong Cheng, Yao Yao, Yihang Chen, Yihao Meng, Yinghao Xu, Yixuan Li, Yue Yu, Yujun Shen, Zehong Shen","submitted_at":"2026-01-28T12:37:01Z","abstract_excerpt":"We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. (2) It enables a minute-level horizon while preserving contextual consistency over time, which is also known as \"long-term memory\". (3) It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public acces"},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2601.20540","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-01-28T12:37:01Z","cross_cats_sorted":[],"title_canon_sha256":"652536ea9e2faf1c5f170b65d4f2dfcdee205b52188f1ad877e30903215e7d56","abstract_canon_sha256":"5c05a07d4893e67a750e8ba4fe780192c84a1e1bcf350103f2faa439356d34c9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:38:48.414674Z","signature_b64":"8ne/IImPxsFv6q9Od1FD+dvDVkor0EFNNrLrh8UIz4X5nnGbtU0UL9Uhsxf+p7xJwhqZojVFLtRgonjfbJPeDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5cd2f9408d9f4e79f7395f8c8859e60861af57d0a44f69a006aae337ba69f3a6","last_reissued_at":"2026-05-17T23:38:48.414163Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:38:48.414163Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Advancing Open-source World Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"LingBot-World is an open-source world simulator from video generation that claims high fidelity across environments, minute-scale temporal consistency, and sub-second latency at 16 frames per second.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hanlin Wang, Hao Ouyang, Jiapeng Zhu, Jiayi Zhu, Jie Liu, Jingye Chen, Ka Leong Cheng, Kecheng Zheng, Qingyan Bai, Qiuyu Wang, Robbyant Team: Zelin Gao, Shuailei Ma, Xing Zhu, Yanhong Zeng, Yansong Cheng, Yao Yao, Yihang Chen, Yihao Meng, Yinghao Xu, Yixuan Li, Yue Yu, Yujun Shen, Zehong Shen","submitted_at":"2026-01-28T12:37:01Z","abstract_excerpt":"We present LingBot-World, an open-sourced world simulator stemming from video generation. Positioned as a top-tier world model, LingBot-World offers the following features. (1) It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. (2) It enables a minute-level horizon while preserving contextual consistency over time, which is also known as \"long-term memory\". (3) It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second. We provide public acces"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Positioned as a top-tier world model, LingBot-World offers the following features. (1) It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. (2) It enables a minute-level horizon while preserving contextual consistency over time, which is also known as 'long-term memory'. (3) It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the released model actually achieves the stated levels of fidelity, minute-scale consistency, and sub-second latency across diverse environments, as the abstract provides no benchmarks, comparisons, or implementation details to support these performance assertions.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"LingBot-World is presented as an open-source world model that delivers high-fidelity simulation, minute-level contextual consistency, and real-time interactivity under one second latency.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"LingBot-World is an open-source world simulator from video generation that claims high fidelity across environments, minute-scale temporal consistency, and sub-second latency at 16 frames per second.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"6c0d89bff1ef1ec2f4c7a4c82b9ed433f705778ab7bc5c55ce5bd2a06a28d6f6"},"source":{"id":"2601.20540","kind":"arxiv","version":1},"verdict":{"id":"13a3d94a-706b-43fb-9681-15e1d9735669","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T09:02:34.641612Z","strongest_claim":"Positioned as a top-tier world model, LingBot-World offers the following features. (1) It maintains high fidelity and robust dynamics in a broad spectrum of environments, including realism, scientific contexts, cartoon styles, and beyond. (2) It enables a minute-level horizon while preserving contextual consistency over time, which is also known as 'long-term memory'. (3) It supports real-time interactivity, achieving a latency of under 1 second when producing 16 frames per second.","one_line_summary":"LingBot-World is presented as an open-source world model that delivers high-fidelity simulation, minute-level contextual consistency, and real-time interactivity under one second latency.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the released model actually achieves the stated levels of fidelity, minute-scale consistency, and sub-second latency across diverse environments, as the abstract provides no benchmarks, comparisons, or implementation details to support these performance assertions.","pith_extraction_headline":"LingBot-World is an open-source world simulator from video generation that claims high fidelity across environments, minute-scale temporal consistency, and sub-second latency at 16 frames per second."},"references":{"count":92,"sample":[{"doi":"","year":2024,"title":"Diffusion for world modeling: Visual details matter in atari","work_id":"981f9f8f-23a1-43b6-b3a0-7985c1de3adf","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning","work_id":"a9c28401-f16a-4933-89f0-788e2f94e52b","ref_index":2,"cited_arxiv_id":"2506.09985","is_internal_anchor":true},{"doi":"","year":2025,"title":"Scaling instruction-based video editing with a high-quality synthetic dataset","work_id":"4db310ce-4ba1-4e8d-93a6-90134f0ff4e8","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Frozen in time: A joint video and image encoder for end-to-end retrieval","work_id":"990d77ea-f62a-4107-9b3c-0d0dfb5b355c","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Philip J. Ball, Jakob Bauer, Frank Belletti, Bethanie Brownfield, Ariel Ephrat, Shlomi Fruchter, Agrim Gupta, Kristian Holsheimer, Aleksander Holynski, Jiri Hron, Christos Kaplanis, Marjorie Limont, M","work_id":"fb735614-5ca1-448c-b17c-b9dde7a72be9","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":92,"snapshot_sha256":"8171259952c32e560004c2a558534df781de8fc596fb7ec0d2f85535fb170a48","internal_anchors":34},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d3caeb89f497cc9e0fa7dfd0446443149eaf6ced7c6845a254b91497ddf7e015"},"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":"2601.20540","created_at":"2026-05-17T23:38:48.414250+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.20540v1","created_at":"2026-05-17T23:38:48.414250+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.20540","created_at":"2026-05-17T23:38:48.414250+00:00"},{"alias_kind":"pith_short_12","alias_value":"LTJPSQENT5HH","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"LTJPSQENT5HHT5ZZ","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"LTJPSQEN","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":30,"internal_anchor_count":30,"sample":[{"citing_arxiv_id":"2605.11596","citing_title":"HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"2605.23345","citing_title":"SCOPE: Simulating Cross-game Operations in Playable Environments for FPS World Models","ref_index":51,"is_internal_anchor":true},{"citing_arxiv_id":"2605.22814","citing_title":"Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2605.22718","citing_title":"WorldKV: Efficient World Memory with World Retrieval and Compression","ref_index":23,"is_internal_anchor":true},{"citing_arxiv_id":"2605.04128","citing_title":"JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation","ref_index":74,"is_internal_anchor":true},{"citing_arxiv_id":"2605.20910","citing_title":"FlowLong: Inference-time Long Video Generation via Manifold-constrained Tweedie Matching","ref_index":24,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16395","citing_title":"OrbiSim: World Models as Differentiable Physics Engines for Embodied Intelligence","ref_index":36,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16713","citing_title":"GeoWorld-VLM: Geometry from World Models for Vision-Language Models","ref_index":41,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18346","citing_title":"Focused Forcing: Content-Aware Per-Frame KV Selection for Efficient Autoregressive Video Diffusion","ref_index":40,"is_internal_anchor":true},{"citing_arxiv_id":"2605.18601","citing_title":"Incantation: Natural Language as the Action Interface for Multi-Entity Video World Models","ref_index":39,"is_internal_anchor":true},{"citing_arxiv_id":"2605.19242","citing_title":"PhyWorld: Physics-Faithful World Model for Video Generation","ref_index":34,"is_internal_anchor":true},{"citing_arxiv_id":"2602.06949","citing_title":"DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos","ref_index":89,"is_internal_anchor":true},{"citing_arxiv_id":"2605.15178","citing_title":"SANA-WM: Efficient Minute-Scale World Modeling with Hybrid Linear Diffusion Transformer","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2603.11911","citing_title":"InSpatio-WorldFM: An Open-Source Real-Time Generative Frame Model","ref_index":32,"is_internal_anchor":true},{"citing_arxiv_id":"2603.28489","citing_title":"Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms","ref_index":235,"is_internal_anchor":true},{"citing_arxiv_id":"2604.02799","citing_title":"UNICA: A Unified Neural Framework for Controllable 3D Avatars","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"2605.11596","citing_title":"HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation","ref_index":21,"is_internal_anchor":true},{"citing_arxiv_id":"2604.27711","citing_title":"ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control","ref_index":22,"is_internal_anchor":true},{"citing_arxiv_id":"2605.00078","citing_title":"Being-H0.7: A Latent World-Action Model from Egocentric Videos","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2605.07514","citing_title":"Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models","ref_index":33,"is_internal_anchor":true},{"citing_arxiv_id":"2604.08995","citing_title":"Matrix-Game 3.0: Real-Time and Streaming Interactive World Model with Long-Horizon Memory","ref_index":38,"is_internal_anchor":true},{"citing_arxiv_id":"2604.07209","citing_title":"INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling","ref_index":78,"is_internal_anchor":true},{"citing_arxiv_id":"2604.04911","citing_title":"SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing","ref_index":47,"is_internal_anchor":true},{"citing_arxiv_id":"2604.04707","citing_title":"OpenWorldLib: A Unified Codebase and Definition of Advanced World Models","ref_index":116,"is_internal_anchor":true},{"citing_arxiv_id":"2604.14268","citing_title":"HY-World 2.0: A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds","ref_index":61,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":2,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB","json":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB.json","graph_json":"https://pith.science/api/pith-number/LTJPSQENT5HHT5ZZL6GIQWPGBB/graph.json","events_json":"https://pith.science/api/pith-number/LTJPSQENT5HHT5ZZL6GIQWPGBB/events.json","paper":"https://pith.science/paper/LTJPSQEN"},"agent_actions":{"view_html":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB","download_json":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB.json","view_paper":"https://pith.science/paper/LTJPSQEN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.20540&json=true","fetch_graph":"https://pith.science/api/pith-number/LTJPSQENT5HHT5ZZL6GIQWPGBB/graph.json","fetch_events":"https://pith.science/api/pith-number/LTJPSQENT5HHT5ZZL6GIQWPGBB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB/action/storage_attestation","attest_author":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB/action/author_attestation","sign_citation":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB/action/citation_signature","submit_replication":"https://pith.science/pith/LTJPSQENT5HHT5ZZL6GIQWPGBB/action/replication_record"}},"created_at":"2026-05-17T23:38:48.414250+00:00","updated_at":"2026-05-17T23:38:48.414250+00:00"}