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新论文 · v1arXiv:2611.01950

CertClaim: Towards Certified Model-Claim Verification for Coding-Agent Services

Jiacheng Lu, Qixuan Yang, Tao Song, Daxin Jiang, Jing Li, Zuo Bai, Haibing Guan

原文摘要

Coding-agent services are priced by the model they claim, making the verification of such model claims crucial for their users. In this paper, we reveal that existing model auditing methods rely on a latent assumption that the deployment is static, i.e., the claimed model is always served in one reference configuration. This assumption does not hold for coding-agent services, whose providers may legitimately change the sampling preset, the reasoning mode or the system prompt without changing the model. As a result, existing methods either falsely accuse honest providers or miss cheap derivatives of the claimed model. To address this problem, we propose a certified model-claim verification method (dubbed CertClaim). Instead of testing the service against one reference deployment, we make the authorized deployments part of the claim and test whether some authorized deployment explains all the evidence of the service, by calibrating multiple evidence channels in every authorized deployment and fusing them within each deployment. We provide theoretical analyses of its false-accusation rate and detection condition. Extensive experiments on eight claims, two agent harnesses and three task suites verify the effectiveness of CertClaim, which is valid in every setting while separating substitutes far better than existing methods, as well as its resistance to potential adaptive attacks.

cs.CR

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1369ACL Anthology · ACL 2026

Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference

Zhouxuwen, Fangxin Liu, Chao Wang, Xiao Zheng, Hao Zheng Alibaba Cloud, Min He Alibaba Group, Li Jiang Shanghai Jiaotong University, Haibing Guan Shanghai Jiao Tong University

原文摘要

Speculative decoding accelerates autoregressive generation by letting draft tokens bypass full verification, but conventional frameworks suffer from frequent false rejections, particularly when draft models produce semantically correct but lexically divergent outputs. In this paper, we present Calibrated Speculative Decoding (CSD), a training-free framework that recovers valid tokens discarded by standard verification. Guided by the principle of “Frequency-Guided Candidate Selection and Probability-Guarded Acceptance,” CSD incorporates two lightweight modules: Online Correction Memory, which aggregates historical rejections to propose recurring divergence patterns as rescue candidates, and Semantic Consistency Gating, which verifies candidate admissibility using probability ratios instead of exact token matching. Our evaluation across diverse large language models demonstrates that CSD outperforms existing methods, achieving a peak throughput speedup of 2.33x. CSD preserves model accuracy across all tasks while further boosting performance on complex reasoning datasets. These results establish CSD as a highly effective, lightweight solution for practical LLM deployments.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表