OxyGen: Unified KV Cache Management for VLA Inference under Multi-Task Parallelism
Embodied AI agents increasingly require parallel execution of multiple tasks, such as manipulation, conversation, and memory construction, from shared observations under distinct time constraints. Recent Mixture-of-Transformers (MoT) Vision-Language-Action Models (VLAs) support such heterogeneous outputs, yet existing inference systems incur redundant computation and resource contention when serving concurrent tasks on-device. We identify isolated KV cache management as the root cause. To address this, we propose unified KV cache management, an inference design that treats the KV cache as a first-class shared resource across tasks and control cycles. This abstraction enables two key optim...
| Authors | Xiangyu Li, Huaizhi Tang, Xin Ding et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 March 2026 |
| Last updated at source | 14 October 2026 |
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