FAVP: Future-Aware Vision Token Pruning for Unified World Action Models

Vision token pruning can accelerate inference in unified World Action Models (WAMs), yet existing approaches primarily use semantic, temporal, or action-related cues without explicitly exploiting the future-prediction knowledge learned during world modeling. To bridge this gap, we propose **FAVP**, a training-free **F**uture-**A**ware **V**ision Token **P**runing framework that reuses this capability to guide vision token selection during action inference. FAVP introduces three modules: (1) **Future-Prediction Query Augmentation** to efficiently extract future-aware visual information without full WM inference, (2) **Future-Aware Attention Masking** to preserve the original action-generation process and to block the queries from directly accessing the original non-visual tokens, and (3) **Action-Future Token Selection** to jointly exploit action-aware and future-aware importance to prune redundant and unimportant tokens. Extensive experiments across WAM backbones (i.e., RynnVLA-002 and UniVLA) in both simulation and real-world manipulation tasks demonstrate that FAVP achieves up to a 1.63× inference speedup on simulation benchmarks and a 1.67× speedup in real-world manipulation, with relatively small reductions in success rate.