[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85018-en":3,"doc-seo-85018-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85018,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Infinite Worlds with Versatile Interactions","Infinite Worlds with Versatile Interactions introduces LingBot-World 2.0 (LingBot-World-Infinity), an advanced interactive world model with four upgrades: unbounded interaction horizon through causal pretraining, real-time distillation for rapid responses supporting 720p video at 60 fps, and substantially richer interactive behaviors and text-driven events such as combat, archery, spell-casting, and ranged shooting. It also integrates an agentic harness: a pilot agent plans character actions while a director agent generates novel environmental elements, enabling multi-player shared immersion alongside a deployable paired 14B/1.3B setup.","arXiv :2607 .07534v 1 [ cs .CV] 8 Jul 2026  \nInfinite Worlds with Versatile Interactions  \nZelin Gao, Qiuyu Wang, Jiapeng Zhu, Jingye Chen, Zichen Liu, Qingyan Bai, Jiahao Wang, Yufeng Yuan, Hanlin Wang, Yichong Lu, Ka Leong Cheng, Haojie Zhang, Jian Gao,  \nTianrui Feng, Yuzheng Liu, Yao Yao, Yinghao Xu, Xing Zhu, Yujun Shen, Hao Ouyang†  \n†Project Lead  \nWe present LingBot-World 2.0 (also known as LingBot-World-Infinity), an advanced iteration of LingBotWorld featuring four distinct upgrades. (1) Our model achieves an unbounded interaction horizon while maintaining consistent output quality, benefiting from a carefully crafted causal pretraining paradigm. (2) Through distilling a real-time variant from the base model, our system guarantees rapid response time, sufficient to drive 720p video streams at 60 fps. (3) Compared to the previous version, this update introduces highly diverse interactive elements, comprising a broader spectrum of actions (e.g., attacking, archery, spell-casting, and shooting) alongside a richer variety of text-driven events. (4) We pioneer the integration of an agentic harness within the domain of world modeling, wherein a pilot agent is tasked with planning and executing character behaviors, while a director agent is responsible for synthesizing novel environmental elements as the scene progresses. Additionally, to facilitate a shared experience, we develop an interface that permits multiple players to simultaneously immerse themselves in this vivid world simulator. We pair our primary 14B model with a lightweight 1.3B counterpart, which supports effortless deployment on a single GPU.  \nWebsite: [https://technology.robbyant.com/lingbot-world-v2](https://technology.robbyant.com/lingbot-world-v2)[ ](https://technology.robbyant.com/lingbot-world-v2)Github: [https://github.com/robbyant/lingbot-world-v2](https://github.com/robbyant/lingbot-world-v2)[ ](https://github.com/robbyant/lingbot-world-v2)Checkpoints: [https://huggingface.co/robbyant/lingbot-world-v2](https://huggingface.co/robbyant/lingbot-world-v2)  \nFigure 1. LingBot-World-Infinity generates infinite worlds in real time, featuring versatile interactions.  \n1 Introduction  \nInteractive world models [3, 5, 22, 31, 41, 43, 49], generative systems that synthesize an environment frame by frame in response to a stream of user or agent actions, have recently emerged as a promising substrate for game generation [5, 22, 37, 46, 49] and embodied simulation [1, 10, 16] . Driven by advances in causal (autoregressive) video generation [7, 9, 45, 55, 59, 61], such models can in principle render an explorable world that unfolds indefinitely and reacts to its inhabitants in real time. Yet turning this principle into a usable system has remained difficult, for two reasons.  \nThe first concerns long-horizon stability. Because each frame is conditioned on the frames generated before it, errors are fed back into the model and accumulate; over time, textures smear, geometry warps, and the scene drifts away from any plausible world. Most existing systems therefore remain visually stable for only seconds to a few minutes before degrading, far short of the persistence one expects from a world meant to be lived in. The second concerns interactivity at high fidelity, which is computationally expensive: rendering detailed video while reacting to live inputstrains compute budgets, and prior work has typically bought interactivity by sacrificing resolution, smoothness, or control, leaving the user with little more than coarse camera movement through an otherwise inert scene.  \nWe argue that these two limitations are what stand between current models and genuinely open-ended worlds, and we tackle them directly. Our starting point is a causal video generation model trained to resist the error accumulation described above; the resulting backbone holds its quality far longer than prior open models and, in turn, makes a clean teacher for distillation. The distilled student is w","cbCairMfk0LdRLUf","https://ap.wps.com/l/cbCairMfk0LdRLUf","pdf",33920199,2,1,19,"English","en",105,"# Introduction\n## Long-horizon stability\n## High-fidelity interactivity\n## Proposed solution and system design\n## Contributions","[{\"question\":\"What is LingBot-World 2.0 (LingBot-World-Infinity)?\",\"answer\":\"LingBot-World 2.0 is an interactive world model designed to generate an unbounded, real-time environment that reacts to user and agent actions while maintaining visual quality over long horizons.\"},{\"question\":\"How does the system address long-horizon stability and error accumulation?\",\"answer\":\"It starts from a causal video generation backbone trained to resist error accumulation, with a stability mechanism that preserves quality for an extended session without visible decay.\"},{\"question\":\"How are interactivity and world persistence achieved in the proposed framework?\",\"answer\":\"A real-time distilled model enables responsive 720p/60fps rendering, while an agentic harness pairs a pilot agent for character behavior planning with a director agent that continuously seeds new environmental elements and events during 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