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TVWorld: Foundations for Remote-Control TV Agents

Paper · 2026 · Zhantao Ma, Quanfeng Lu, Shuai Zhong et al.

Summary

Most work on LVLM agents for device control assumes pointer or touch input. Smart TVs work differently: the user presses remote keys to move a highlight between elements, and only the highlighted item can be activated. To study this setting, the authors built TVWorld. They crawled real TV interfaces with automated key presses and recorded them as offline navigation graphs, where each node is a UI state and each edge is a key-triggered transition. This makes evaluation reproducible, fast and free of live devices.

From these graphs they derived two benchmarks. TVWorld-N tests multi-step navigation, and TVWorld-G tests locating the focused element. Existing agents do poorly on them, mainly because they lack awareness of the UI's transition structure. The authors therefore propose a two-stage Topology-Aware Training scheme, with supervised fine-tuning followed by reinforcement learning. They use it to train TVTheseus, which surpasses strong closed-source systems on an unseen TV platform. The work provides the first systematic resources for agents that operate TVs by remote control.

Key points

  • Graph data was collected from physical TVs over ADB, a capture card and a serial remote interface. TCL TV interfaces are used for training, and Google TV is held out entirely for out-of-domain evaluation. The action space has 9 options: four directional moves (UP, DOWN, LEFT, RIGHT) plus OK, EXIT, HOME, SETTING and FINISH.
  • TVTheseus is built on Qwen3-VL-8B-Instruct. On TVWorld-N, its success rate rises from 20.0 to 68.3, beating Gemini 3 Flash and GPT-5 mini. The best open-source baseline, Qwen3-VL-32B-Instruct, reaches 39.0. Config tasks are the hardest scenario.
  • On TVWorld-G (187 samples, Acc@0.5), TVTheseus scores 81.8 versus 78.1 for its base model, even though it received no grounding supervision.
  • Training traces target three behaviours: goal-directed focus moves, recovery from detours, and escaping stalled states. In the ablation, SFT alone gives 48.0 on TVWorld-N and 83.4 on TVWorld-G. Adding RL raises TVWorld-N to 68.3, with a slight drop in grounding.
  • Point-and-click agents such as GUI-Owl and OpenCUA degrade sharply in the remote-control setting. Code is released at github.com/Lqf-HFNJU/TVTheseus.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksSocial Sciences · 1,954 worksInformation Systems · 1,730 worksSociology and Political Science · 626 works

Cite

Cite as Zhantao Ma, Quanfeng Lu, Shuai Zhong, Dahai Yu, Ping Luo and Michael Ng. 2026. TVWorld: Foundations for Remote-Control TV Agents. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1792

@inproceedings{ma2026tvworld,
  title = {{TVWorld: Foundations for Remote-Control TV Agents}},
  author = {Zhantao Ma, Quanfeng Lu, Shuai Zhong, Dahai Yu, Ping Luo and Michael Ng},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1792},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1792}
}

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