ForesightFlow is an Information Leakage Score (ILS) framework for detecting informed trading on decentralized prediction markets. For an event-resolved binary market, the score quantifies the fraction of the terminal information move priced in before the public news event. Three operational scope conditions (edge effect, non-trivial total move, anchor sensitivity) are stated as preconditions for interpretation. The score admits a Murphy-decomposition reading that connects label generation to the proper-scoring-rule literature. A pilot empirical evaluation surfaces three findings. First, a resolution-anchored proxy for the public-event timestamp does not separate event-resolved markets from a matched control population (Mann-Whitney p = 1e-6, separation reversed), demonstrating that proxy quality is itself a binding constraint. Second, the article-derived timestamp on a single high-stakes case shifts the score by 0.444 in magnitude relative to the proxy and lies on the opposite side of zero. Third, an audit of the publicly documented Polymarket insider record reveals that documented cases are systematically deadline-resolved, falling outside the original ILS scope (0 of 24 FFIC inventory markets satisfied original scope conditions). This last finding motivates a deadline-ILS extension introduced in Section 7, anchored at the public-event timestamp rather than the news timestamp, and equipped with a per-category exponential hazard baseline for the time-to-event distribution. The extension closes the gap between the methodology and the population in which insider trading has been empirically documented. An end-to-end evaluation of the extension on the 2026 U.S.-Iran conflict cluster is reported in a companion paper. We release the FFIC inventory, the resolution-typology classification of the 911,237-market corpus, and all code at github.com/ForesightFlow.

翻译：ForesightFlow是一个用于检测去中心化预测市场上知情交易的信息泄漏评分（ILS）框架。对于事件已解决的二元市场，该评分量化了公开新闻事件发生前已定价的终端信息变动比例。解释该评分需满足三个操作适用范围条件：边缘效应、非平凡总变动和锚点敏感性。该评分支持Murphy分解解读，将标签生成与合理评分规则文献联系起来。初步实证评估揭示三项发现：第一，以事件解决为锚点的公开事件时间戳代理变量无法区分事件已解决市场与匹配对照组（Mann-Whitney p=1e-6，分组效果相反），表明代理变量质量本身就是约束条件；第二，在单一高风险案例中，基于论文提取的时间戳使评分相对于代理变量移动0.444量级，且处于零界面的相反侧；第三，对公开记录的Polymarket内幕交易记录审计显示，有记录的案例系统性地以截止日期为事件解决方式，超出原始ILS适用范围（24个FFIC库存市场中0个满足原始适用范围条件）。最后一项发现催生了第7节提出的截止日期ILS扩展方案——该方案以公开事件时间戳而非新闻时间戳为锚点，并配备基于类别的指数危险基线函数描述事件发生时间分布。该扩展弥合了方法论与实证记录内幕交易群体之间的差距。配套论文报告了该扩展在2026年美伊冲突集群上的端到端评估。我们已在github.com/ForesightFlow发布FFIC库存数据、911,237个市场的解决方案类型分类及全部代码。