Latent World Models Should Plan with Distances Along the Latent Manifold
Latent world models such as LeWM plan by searching for an action sequence whose predicted terminal latent is closest to the goal latent under an ambient norm, such as the Euclidean distance.
ProofPaper ↗
Key points
- Planning with such a cost assumes that proximity in the ambient latent space reflects proximity to the goal in the environment.
- Yet neighborhood-based embeddings show that the latents retain the underlying physical state.
- To estimate this manifold distance, we propose the Neighborhood Graph Cost (NGC), which computes shortest-path distances on a mutual nearest-neighbor graph constructed over training latents.
- These results support the manifold hypothesis and our argument that latent world models should plan with distances along the latent manifold.
Sources (1)
- [1]Latent World Models Should Plan with Distances Along the Latent ManifoldarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 04:47 AM
Latent world models such as LeWM plan by searching for an action sequence whose predicted terminal latent is closest to the goal latent under an ambient norm, such as the Euclidean distance.
Planning with such a cost assumes that proximity in the ambient latent space reflects proximity to the goal in the environment.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 18, 2026World Model Interpretability: From Decoding Representations to Understanding Latent Worlds
- Oct 17, 2026deepseek-ai/DeepSeek-V4.1-Flash-1017
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