构建电视遥控交互基准，提升大模型对电视导航拓扑的感知能力。
TVWorld: Foundations for Remote-Control TV Agents
- 基于真实电视操作构建离线图谱，支持可复现评估
- 提出拓扑感知训练框架，使模型在长序列导航中成功率超68%
- 适合研究智能设备控制、视觉语言模型落地的开发者
近期大型视觉-语言模型（LVLMs）在设备控制方面展现出强大潜力，但现有研究主要聚焦于点选点击（PnC）交互，而日常电视使用中常见的遥控器（RC）交互仍缺乏系统探索。为此，本文提出 extbf{TVWorld}，一个基于真实电视导航的离线图结构抽象，实现可复现且无需部署的评估。在此基础上，构建两个互补基准： extbf{TVWorld-N}（拓扑感知导航）与 extbf{TVWorld-G}（焦点感知定位），全面评估电视使用能力。实验揭示现有代理在焦点驱动、长时序电视导航中拓扑感知不足的关键缺陷。针对此问题，提出 extit{Topology-Aware Training} 框架，将拓扑知识注入 LVLM。基于该框架，开发专用于电视导航的 extbf{TVTheseus} 模型，在 TVWorld-N 上达到 68.3% 成功率，超越 Gemini 3 Flash 等闭源强基线，达当前最优（SOTA）水平。额外分析为高效电视使用代理的发展提供了关键洞见。
原文摘要 · Abstract (English)
Recent large vision-language models (LVLMs) have demonstrated strong potential for device control. However, existing research has primarily focused on point-and-click (PnC) interaction, while remote-control (RC) interaction commonly encountered in everyday TV usage remains largely underexplored. To fill this gap, we introduce \textbf{TVWorld}, an offline graph-based abstraction of real-world TV navigation that enables reproducible and deployment-free evaluation. On this basis, we derive two complementary benchmarks that comprehensively assess TV-use capabilities: \textbf{TVWorld-N} for topology-aware navigation and \textbf{TVWorld-G} for focus-aware grounding. These benchmarks expose a key limitation of existing agents: insufficient topology awareness for focus-based, long-horizon TV navigation. Motivated by this finding, we propose a \emph{Topology-Aware Training} framework that injects topology awareness into LVLMs. Using this framework, we develop \textbf{TVTheseus}, a foundation model specialized for TV navigation. TVTheseus achieves a success rate of $68.3\%$ on TVWorld-N, surpassing strong closed-source baselines such as Gemini 3 Flash and establishing state-of-the-art (SOTA) performance. Additional analyses further provide valuable insights into the development of effective TV-use agents.
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