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arXiv · 2610.33587

Who Verifies the Verifiers? Accountable Semantic Consensus for AI-Mediated Web Information Verification

Yanna Jiang·Mingyuan Li·Xu Wang·Guangsheng Yu
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Abstract

Large language models (LLMs) increasingly decide whether Web evidence supports online claims, often by voting over several assessors whose errors are presumed independent and benign. On the open Web, retrieved pages can steer the assessors that read them, and assessors from one model family fail together. We prove that even for independent assessors whose every output sequence is possible on both valid and invalid claims, any protocol that decides only from their outputs and always reaches an agreed verdict never rejects invalid claims or rejects valid ones with positive probability. We present Astral, which pins all assessors to one committed snapshot of the Web evidence. With up to f assessors faulty, Astral accepts a verdict only when 2f+1 assessors attest to it above a confidence threshold and no signed challenge stands. On five public benchmarks with two of seven assessors adversarial, Astral lowers pooled attack success to 21.5-53.1%, against 54.7-77.8% for majority voting. The cost is coverage, as without attack 16.0-61.7% of claims receive a binary verdict and the rest are disputed for audit. Our code runs at https://anonymous.4open.science/r/Astral-55F1.

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Yanna Jiang, Mingyuan Li, Xu Wang, Guangsheng Yu. 2026-10-24. Who Verifies the Verifiers? Accountable Semantic Consensus for AI-Mediated Web Information Verification. https://arxiv.org/abs/2610.33587

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