SOURCE-LINKED INTELLIGENCE
PonderPounce: A Pretrained MLLM as an Episode Context Engine for Robot Control
Multimodal large language models (MLLMs) can integrate long visual histories and infer behavior from a few examples, yet vision-language-action models rarely use this capacity as episode memory. Instead of a purpose-built memory module, PONDERPOUNCE reuses an MLLM's native causal context. PONDER, a pretrained System 2 MLLM, integrates episode history and demonstrations to produce continuous cognition. POUNCE, a System 1 action model, asynchronously conditions control on the newest cognition and its age. Both are jointly trained end to end without separate bridge pretraining. Optimized per-call
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-25T06:24:36.000Z
First collected: 2026-09-21T10:22:00.206Z. This is not the publication date.