arXiv Artificial Intelligence

Foresight: planning future perception in streaming VLMs without retraining

1 hour ago 05.10.2026 07:00 👁 2 views
Foresight: planning future perception in streaming VLMs without retraining

Quick summary

arXiv:2610.03123v1 Announce Type: cross Abstract: 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 compu

Key takeaways

  • arXiv:2610.03123v1 Announce Type: cross Abstract: 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.

Why it matters

The importance of “Foresight: planning future perception in streaming VLMs without retraining” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗