Spatial Memory Agent: Experience-Grounded Procedural Memory for Spatial Intelligence
Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants. To improve the spatial reasoning ability of VLMs, existing work has mainly followed two lines. One line uses post-training methods, such as supervised fine-tuning and reinforcement learning. Another line adopts an agentic paradigm in which the model calls external spatial tools to gather intermediate spatial evidence. We study a complementary and underexplored route: Can a frozen VLM agent improve its spatial reasoning through \textbf{parameter-update-free self-evolution}, without depending on external expert spatial tools at inference time? We present \textbf{Spatial Memory Age...
| Authors | Haokai Zhang, Yuhang Ding, Yunshu Zhou et al. |
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| Primary category | cs.AI |
| Published | 13 August 2026 |
| Last updated at source | 29 September 2026 |
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