# Agentic AI for Clustering, Relationship Discovery, and Semantic Trading in Prediction Markets

arXiv: https://arxiv.org/abs/2512.02436 · HTML: https://mlsanity.com/paper/2512.02436/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 2 Dec 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 7 of 7)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities](https://mlsanity.com/paper/2409.19839/) (2024)
- [Balance and Frustration in Signed Networks](https://mlsanity.com/paper/1712.04628/) (2017)
- [Transduction is All You Need for Structured Data Workflows](https://mlsanity.com/paper/2508.15610/) (2025)

## Built on by (most cited first, 2 of 2)

- [PredictionMarketBench: A SWE-bench-Style Framework for Backtesting Trading Agents on Prediction Markets](https://mlsanity.com/paper/2602.00133/) (2026)
- [Unlocking the Forecasting Economy: A Suite of Datasets for the Full Lifecycle of Prediction Market: [Experiments & Analysis]](https://mlsanity.com/paper/2604.20421/) (2026)

## Abstract (arXiv)

Prediction markets allow users to trade on outcomes of real-world events, but are prone to fragmentation with overlapping questions, implicit equivalences, and hidden contradictions across markets. We present an agentic AI (AAI) pipeline that autonomously recovers cross-market structure from contract text before prices enter the analysis. The workflow first clusters markets into coherent topical groups using natural-language understanding over contract text and metadata, and then identifies contracts within each cluster, but from different event markets, that exhibit strong dependence or leader--follower relationships. We evaluate this system, along with a natural language inference (NLI) benchmark, on a large prediction market dataset from early 2026. Using resolved outcomes to evaluate identified relations, we find that AAI-identified relations are 62.8% consistent with exchange-recorded settlements, whereas the NLI benchmark only achieves 40.6% accuracy. Within clusters, the AAI output is sparse and also remarkably compatible as a signed graph with a frustration rate of 0.324%. As an application, we show how discovered relations inform semantics-based trading strategies on prediction markets. One such strategy yields 14.12% net ROI after fees in a two-month period in 2026. Overall, we demonstrate the potential for agentic AI as a structural discovery layer for prediction markets.