SharedKV-BT: Node-Local Typed Decisions for Behavior-Tree Agents
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. ...
| Authors | Naoki Wake, Justin Wagle |
|---|---|
| Primary category | cs.RO |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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