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[2602.18447] ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification
Summary
The paper presents ConfSpec, a novel framework for efficient step-level speculative reasoning in large language models, achieving significant speedups while maintaining accuracy.
Why It Matters
As AI models grow in complexity, balancing speed, accuracy, and resource efficiency becomes critical. ConfSpec addresses this challenge, offering a solution that enhances performance in real-time applications, which is vital for advancing AI capabilities in practical scenarios.
Key Takeaways
- ConfSpec utilizes confidence-gated verification to improve inference speed.
- The framework allows for high-confidence decisions without needing large models for every step.
- It achieves up to 2.24x speed improvements while maintaining target model accuracy.
Computer Science > Computation and Language arXiv:2602.18447 (cs) [Submitted on 28 Jan 2026] Title:ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification Authors:Siran Liu, Cyril Y. He View a PDF of the paper titled ConfSpec: Efficient Step-Level Speculative Reasoning via Confidence-Gated Verification, by Siran Liu and 1 other authors View PDF HTML (experimental) 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-...