[2606.29565] Speculative Pre-Positioning: Decoding Stateful Sessions to the Next Decision Point Off the Critical Path

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Computer Science > Machine Learning

arXiv:2606.29565 (cs)

[Submitted on 28 Jun 2026]

Title:Speculative Pre-Positioning: Decoding Stateful Sessions to the Next Decision Point Off the Critical Path

Authors:Victor Norgren<br>View a PDF of the paper titled Speculative Pre-Positioning: Decoding Stateful Sessions to the Next Decision Point Off the Critical Path, by Victor Norgren

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Abstract:A stateless inference server (vLLM, SGLang, TensorRT-LLM) idles between requests while the accelerator waits; a stateful session reclaims that idle time. Speculative pre-positioning decodes the session forward to its next decision point with the target model's own forward pass and no draft model, moving the cross-request prefill and entry-decode off the critical path: the next request resumes from a pre-paid entry on its delta, or, when a confidence gate fires, is answered from a cached distribution in one near-constant vocabulary scan with no decode, at a cost only of energy and a rare, bounded false accept. The payoff is conditional on capability: a capable model fires the gate at near-full coverage and about 87% precision (a smaller one never clears it), returning the first token in about 1.0 ms versus the 39 ms decode a prefix cache still pays.

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Machine Learning (cs.LG)

Cite as:<br>arXiv:2606.29565 [cs.LG]

(or<br>arXiv:2606.29565v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.29565

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arXiv-issued DOI via DataCite

Submission history<br>From: Victor Norgren [view email]<br>[v1]<br>Sun, 28 Jun 2026 19:10:02 UTC (27 KB)

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