ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification

• URL: http://arxiv.org/abs/2602.18447v1
• Date: Wed, 28 Jan 2026 05:58:05 GMT
• Title: ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification
• Authors: Siran Liu, Cyril Y. He,
• Abstract summary: Step-level speculative reasoning aims to mitigate this cost, yet existing approaches face a long-standing trade-off.<n>We propose ConfSpec, a confidence-gated cascaded verification framework that resolves this trade-off.
• Score: 0.2578242050187029
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-Thought reasoning significantly improves the performance of large language models on complex tasks, but incurs high inference latency due to long generation traces. Step-level speculative reasoning aims to mitigate this cost, yet existing approaches face a long-standing trade-off among accuracy, inference speed, and resource efficiency. We propose ConfSpec, a confidence-gated cascaded verification framework that resolves this trade-off. Our key insight is an asymmetry between generation and verification: while generating a correct reasoning step requires substantial model capacity, step-level verification is a constrained discriminative task for which small draft models are well-calibrated within their competence range, enabling high-confidence draft decisions to be accepted directly while selectively escalating uncertain cases to the large target model. Evaluation across diverse workloads shows that ConfSpec achieves up to 2.24$\times$ end-to-end speedups while matching target-model accuracy. Our method requires no external judge models and is orthogonal to token-level speculative decoding, enabling further multiplicative acceleration.

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