Foresight: planning future perception in streaming VLMs without retraining
Existing streaming vision-language models (VLMs) continuously perceive and reason over visual streams, but their computational pathways remain fixed throughout inference. Consequently, they cannot adapt computation to evolving scene dynamics, where different future events demand different levels and forms of perception. We show that streaming VLMs inherently possess the ability to anticipate the immediate future, and leverage this capability to dynamically configure future computation in a training-free manner. Realizing such anticipatory computation, however, is very challenging: future anticipation must be sufficiently reliable to guide computation, planning must run concurrently with s...
| Authors | Ashok Prasad Neupane, Dipan Bartaula, Ankit Belbase et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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