Neural Introspection Gating for Adaptive KV-Cache Reuse in Vision-Language-Action Models
Vision-Language-Action(VLA) models map camera images and language instructions directly to motor commands through a single autoregressive transformer. In real-time control, they still spend substantial compute recomputing key-value(KV) representations for visual tokens that barely change across neighboring frames. Recent work such as VLA-Cache reduces that cost by reusing KV states for visually static patches, but its policy relies only on observation-space heuristics and does not account for the model's own uncertainty. We propose Gated VLA-Cache, a lightweight, training-free extension that augments visual-similarity caching with neural introspection. The method monitors the logit margin...
| Authors | Zhijie Wu, Kento Kawaharazuka, Kei Okada |
|---|---|
| Primary category | cs.RO |
| Published | 11 August 2026 |
| Last updated at source | 11 August 2026 |
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