GoKawiilvLLM 0.31 adds constrained decoding to fix tool-call schema errors
vLLM, an open-source inference engine, has shipped a server-side flag in version 0.31 that enables constrained decoding, a technique forcing model output to match a required schema. The post's authors say that serving Kimi K2.6 on vLLM 0.28 produced correctly formatted tool calls only 92% of the time, but enabling the new flag raised accuracy to 100% for newer Kimi and DeepSeek models.
GoKawiil's interpretation of the reporting above, not reported fact.
Agentic workloads depend on multi-turn tool calls, so even small schema failure rates can compound into unusable context over a session, which suggests benchmark scores alone understate real-world reliability gaps between providers. The fix indicates that infrastructure-level engineering, not just model quality, can materially affect how open-weight models perform in production agent pipelines.
- vLLM 0.28 produced malformed tool calls for Kimi K2.6 about 8% of the time
- vLLM 0.31 introduces a server-side constrained decoding flag
- Enabling it reportedly achieves 100% schema accuracy for newer Kimi and DeepSeek models
Source: ubicloud.com, 2026-10-26
Published there as: “Fixing Tool-Call Accuracy in vLLM”
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