Frontier models can now perform well on individual embodied navigation tasks through multi-round multimodal reasoning with simple tools. Across successive tasks, however, an agent must also rely on an evolving map and earlier search records, both of which may be incomplete or conflict with new observations. We present...
Xun-Yi Zhao, Jian Zhou, Si-Hao Lin et al.· 0 citations
Language models trained with long-horizon agentic reinforcement learning can generalize knowledge through reasoning, express precise actions, and pursue goals over many steps, raising the ceiling on what an embodied agent can understand and decide. Physical interaction, however, remains the domain of action policies, w...
Geng-Ze Zhou, Yicong Hong, Jiazhao Zhang et al.· 0 citations
The results show that a general-purpose model can already achieve competitive embodied control without a navigation policy, and term this organization agentic embodied control: the reasoning model directly steers every action, keeping reasoning and control aligned.
