让大模型输出可验证，验证成本远低于生成成本。
Tractable Asymmetric Verification for Large Language Models via Deterministic Replicability
- 基于确定性复现原理，在统一环境内验证输出真伪。
- 抽样验证效率比全量重生成快12倍以上。
- 适合构建可信AI系统，尤其适用于多智能体场景。
大型语言模型（LLM）正快速向动态多智能体系统演进，带来计算信任难题：如何验证一个智能体的输出确实由声称的模型生成，而非伪造或由廉价模型替代。本文提出一种可扩展的非对称验证框架，使验证成本显著低于生成成本。该方法基于自回归模型固有的确定性复现特性，要求所有智能体在完全一致的软硬件环境下运行。在此前提下，多个验证者可对模型输出的随机小片段进行概率性审计，并有效分配验证任务。仿真结果表明，针对性验证速度超过全量重生成12倍以上，且可通过参数调节检测概率。该机制为可审计的LLM系统提供基础支撑，是负责任AI与未来异构多智能体系统研究的重要基石。
原文摘要 · Abstract (English)
The landscape of Large Language Models (LLMs) shifts rapidly towards dynamic, multi-agent systems. This introduces a fundamental challenge in establishing computational trust, specifically how one agent can verify that another's output was genuinely produced by a claimed LLM, and not falsified or generated by a cheaper or inferior model. To address this challenge, this paper proposes a verification framework that achieves tractable asymmetric effort, where the cost to verify a computation is substantially lower than the cost to perform it. Our approach is built upon the principle of deterministic replicability, a property inherent to autoregressive models that strictly necessitates a computationally homogeneous environment where all agents operate on identical hardware and software stacks. Within this defined context, our framework enables multiple validators to probabilistically audit small, random segments of an LLM's output and it distributes the verification workload effectively. The simulations demonstrated that targeted verification can be over 12 times faster than full regeneration, with tunable parameters to adjust the detection probability. By establishing a tractable mechanism for auditable LLM systems, our work offers a foundational layer for responsible AI and serves as a cornerstone for future research into the more complex, heterogeneous multi-agent systems.
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