LTM: Latent Transition Based In-Context Memory for Vision-Language-Action Models

arXiv:2610.21362v1 Announce Type: cross Abstract: Robotic manipulation often requires remembering interactions that are no longer visible. We introduce Latent Transition Memory (LTM), which stores history as compact latent transitions between consecutive observations. Instantiated on $\pi_{0.5}$ as LTM-$\pi$, our method uses a frozen latent action encoder, a lightweight adapter, and causal KV caching for incremental inference without memory-specific semantic annotations or external reasoning-model calls. With DreamDojo transitions, LTM-$\pi$ achieves 58.54% success on RoboMME and 80.5% on LIBE

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