Can you really trust your black-box LLM provider with correct inference and honest billing? Researchers from NUS, NTU, and UC Berkeley introduce IMMACULATE. This practical auditing framework uses verifiable computation to randomly check a small fraction of LLM requests. It detects economically motivated cheats like model substitution, quality degradation, and token overbilling without needing trusted hardware or internal model access. IMMACULATE reliably distinguishes honest vs. malicious LLM execution in dense and MoE models, adding less than 1% throughput overhead. IMMACULATE: A Practical LLM Auditing Framework via Verifiable Computation Paper: arxiv.org/pdf/2602.22700 Code: github.com/guo-yanpei/Immacu… Our report: mp.weixin.qq.com/s/WR9nXudXT… 📬 #PapersAccepted by Jiqizhixin

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