arXiv cs.AI· Agostino Capponi, Alfio Gliozzo, Brian Zhu·· 14 小时前AI 评分34
智能体 AI 如何为预测市场构建聚类与关系发现管线
Agentic AI for Clustering and Relationship Discovery in Prediction Markets
AI 导读
研究提出分层智能体归约（HAR）管线，将预测市场按问题与结算文本划分主题聚类，再用带类型的 LLM 推理为小批量市场生成带理由的有符号关系图。基于 Kalshi 2026 年 2 月至 9 月八个月度目录、3030 万笔交易构建基准，发现管线识别的关系对在价格变化符号相关性上显著高于同主题匹配对照组，中等或高强度的关系提升更明显。
正文 · 原文
Abstract:Prediction market platforms list thousands of contracts whose logical and economic connections are implicit in their resolution rules. We introduce a hierarchical agentic reduction (HAR) pipeline for constructing inspectable cross-event relation graphs and a behavioral benchmark for evaluating their alignment with market prices. This partitions a catalog of prediction markets, specified by question and resolution text, into topic clusters, applies typed LLM reasoning to small batches, and proposes signed relations with rationales between specific markets. We evaluate semantic relation graphs spanning eight monthly Kalshi catalogs (February to September 2026). Using data from 30.3 million trades, we compute a benchmark that compares linked pairs' signed price-change correlations with those of matched, unlinked controls from the same topic cluster on identical timestamps. Under full-sample and leave-first-week-out evaluation, relations identified by our pipeline exhibit higher signed Pearson price-change correlations, with statistical significance, than matched within-topic controls. This increase also appears under Spearman correlation and becomes larger among relations labeled as medium or high strength. This benchmark provides a criterion for comparing relation-discovery methods against observations beyond their input text, and our results indicate the ability of agentic AI and LLM reasoning to identify meaningful relations between individual markets in connected Web marketplaces.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2512.02436 [cs.AI] |
| (or arXiv:2512.02436v4 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2512.02436 arXiv-issued DOI via DataCite |
Submission history
From: Brian Zhu [view email]
[v1]
Tue, 2 Dec 2025 05:45:48 UTC (327 KB)
[v2]
Sat, 8 Aug 2026 16:26:56 UTC (128 KB)
[v3]
Tue, 29 Sep 2026 14:55:04 UTC (128 KB)
[v4]
Tue, 27 Oct 2026 19:14:49 UTC (96 KB)
来源：arXiv cs.AI · arxiv.org