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OUTLETS: Output-Length Prediction from Speculative Decoding Backbones

arxiv.org/abs/2609.01068

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Updated 5 h ago · first seen 11 Sept 2026

paper_01M294G692G89RFKSNSHH9CE2W

Published
11 Sept 2026
T1 · 5 h ago
arXiv
2609.01068
T1 · 5 h ago
Category
cs.CL
T1 · 5 h ago

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The heavy-tailed distribution of output lengths in Large Language Model (LLM) serving poses major challenges for resource provisioning and cluster scheduling. Although output-length prediction can mitigate these issues, existing approaches have key drawbacks: external proxy models add substantial latency and often have limited fidelity, whereas internal state-based methods are efficient but rely on shallow probes of current model states. We identify a structural connection between speculative decoding (SD) and length prediction: latent representations produced by the draft decoder in advanced frameworks (e.g., EAGLE-3) encode signals that are predictive of generation length. Building on this insight, we introduce OUTLETS (Output-Length Prediction from Speculative Decoding Backbones), which repurposes the speculative backbone as a trajectory-aware length predictor. When its draft representations are already computed for speculative decoding, OUTLETS adds only a lightweight regression head and achieves lower MAE than the evaluated methods. Under saturated disaggregated serving, OUTLETS predictions enable standard scheduling policies to prioritize shorter requests and distribute requests more evenly across decoding instances, reducing short-request P99 latency by 34.8%.currentcurrentarXiv (Atom API + RSS)T1highdeterministic

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