SmoLSTM: A Compact Vision-Language-Action Model with Recurrent Memory that Persists
Vision-language-action models often predict actions from only the current observation, which can leave tasks involving object occlusion or visually identical objects ambiguous without episode history. The usual countermeasure, widening the observation window, turns the horizon into a hyperparameter and lets per-step cost grow with it. We instead capture the episode in a recurrent state. SmoLSTM couples a frozen 256M-parameter SmolVLM backbone to a matrix-memory LSTM control layer in which observation tokens and action queries are unified in a single causal stream that is never reset throughout the entire episode. Recurrent-state storage is therefore O(1) in episode length. A flow-matching...
| Authors | Jan-Gerrit Habekost, Parsa Mastouri Kashani, Connor Gäde et al. |
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| Primary category | cs.RO |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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