Modern prediction markets face two limitations that restrict their applicability in a range of settings:~(i)~they reveal what the crowd believes but not the evidence or reasoning behind those beliefs, and~(ii)~they require an event with an external ground truth that resolves at a known future date. We address these twin challenges by introducing evidence markets, a generalization of prediction markets that incentivizes the submission of evidence alongside beliefs and can be endogenously resolved using the crowd-sourced evidence if external resolution is not possible. At its core, the market uses a logarithmic market scoring rule whose liquidity parameter changes dynamically with the accumulated evidence quality. We prove that platform loss is bounded, evidence is rewarded proportional to the current market uncertainty, and can be equivalently implemented through an automated market maker. In the case where the marker resolves endogenously based on submitted evidence, we characterize how withholding evidence shifts a trader's belief about resolution and use it to prove truthful belief and evidence reporting is a always an $\varepsilon$-dominant strategy incentive compatible (DSIC) strategy. To address operational considerations, we propose evidence verification via an LLM-as-a-Judge framework with staking and give an asynchronous execution algorithm that is not bottle-necked by verification. Throughout the work, we use LLM evaluations -- determining which model is best for a given task -- as a salient and representative running example for our proposed market.

翻译：现代预测市场面临两大限制，制约了其在众多场景中的适用性：（i）它们只揭示群体的信念，而非这些信念背后的证据或推理过程；（ii）它们需要依赖已知未来日期可裁决的外部真实事件作为事件标的。针对这两个挑战，我们提出证据市场——这一预测市场的泛化形式，不仅激励参与者提交信念，还能通过众包证据实现内生性裁决，在缺乏外部裁决条件时可替代传统机制。该市场核心采用对数市场评分规则，其流动性参数会根据累积证据质量动态调整。我们证明平台损失有界，证据奖励与当前市场不确定性成正比，且可通过自动化做市商等价实现。对于基于提交证据进行内生裁决的市场情形，我们刻画了隐瞒证据如何改变交易者对裁决结果的认知，并据此证明真实信念与证据报告始终构成$\varepsilon$-占优策略激励相容（DSIC）策略。为应对实际运营需求，我们提出基于LLM-as-a-Judge框架的质押验证机制，并给出不受验证瓶颈限制的异步执行算法。本文始终以LLM评估（判定特定任务最优模型）作为所提市场的典型代表案例进行阐释。