Agent tasks require sequences of interdependent decisions.
Autoregressive models support more flexible decision interfaces than conventional classifiers but incur the latency of token-by-token generation.
Recent shared-prefix methods reduce this cost by reusing encoded context and scoring multiple decisions in parallel, but do not model decision dependencies or verify execution.
We propose SharedKV-BT, where each active node of a behavior tree (BT) exposes stage-local fields and candidates, and Shared-KV scores the candidates in parallel and passes the selected decision to a separate execution system.
Testing
We tested SharedKV-BT on robot manipulation, mobile navigation, and computer-use tasks.
Across three tasks, SharedKV-BT made typed decisions 2.36-4.15 times faster than prompt-matched autoregressive decoding.
Manipulation Task
On the manipulation task, node-local Shared-KV improved joint decision accuracy from 75% to 94% and closed-loop success from 0% to 60%.
Policy Replay
Fixed-score policy replay showed that stage gating prevented out-of-order actions and external postconditions prevented premature completion.