From Fixed Keys to Readable Schemas: Small Language Models for Vehicle Agent Function Calls
In-vehicle assistants must translate natural-language requests into accurate vehicle function calls under strict memory and latency constraints, making small language models (SLMs) attractive for on-device deployment. For such models, a key design choice is how the available function surface is presented. Two approaches are to represent each function with a dedicated Functional Token (FT) or provide function schemas directly in the prompt. FTs enable compact inference but are restricted to functions learned during training, whereas Schema-in-Prompt (SIP) can generalize to unseen functions at the cost of longer prompts and higher inference overhead. We introduce a benchmark of 9,822 single...
| Authors | Hamed Jafarzadeh Asl, Yuanhao Yu, Vahid Partovi Nia |
|---|---|
| Primary category | cs.LG |
| Published | 8 September 2026 |
| Last updated at source | 4 October 2026 |
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