SharedKV-BT: Node-Local Typed Decisions for Behavior-Tree Agents

Abstract: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. 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. On the manipulation task, node-local Shared-KV improved joint decision accuracy from 75% to 94% and closed-loop success from 0% to 60%. Fixed-score policy replay showed that stage gating prevented out-of-order actions and external postconditions prevented premature completion.

8 pages, 5 figures, 1 table. Last updated on October 5th, 2026

Robotics (cs.RO); Computation and Language (cs.CL)

arXiv:2610.07327 [cs.RO]

(or arXiv:2610.07327v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.07327 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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