SPORK: Self-Speculative Forking to Accelerate Agentic LLM Inference
LLM agents are becoming a common interface for research, coding, and question answering, yet their Thought-Action-Observation loop is often serial: the model reasons, emits a tool call, then idles the GPU until the result returns. This wait consumes 16-37% of wall time in our workloads and 35-61% in prior reports. Speculative tool execution can hide this wait, but existing systems need auxiliary predictors, historical traces, or static workflow graphs, leaving a gap for training-free, day-one deployment. We observe that the model can be its own predictor: a probe forked at the start of generation predicts Qwen3-32B's upcoming tool name with 74.6-99.6% accuracy across five benchmarks. We p...
| Authors | Huajun Bai, Weiwei Lv, Huichuan Zheng et al. |
|---|---|
| Primary category | cs.DC |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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