# kalshi Published articles for kalshi. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Agentic AI for Clustering and Relationship Discovery in Prediction Markets DevFeed: [Agentic AI for Clustering and Relationship Discovery in Prediction Markets]() Original publisher: [Read original article]() Author: Agostino Capponi, Alfio Gliozzo, Brian Zhu Published: 2026-10-30T04:00:00Z Content type: article Language: en Sources: [arXiv: Artificial Intelligence]() Topics: [Agentic AI](), [Artificial Intelligence](), [llm-reasoning](), [clustering](), [outlier-detection](), [Algorithms]() Tags: [agentic](), [agentic-ai](), [ai](), [benchmark](), [catalog](), [cluster](), [clustering](), [clusters](), [contracts](), [correlation](), [cs-ai](), [data](), [discovery](), [evaluation](), [graphs](), [kalshi](), [llm](), [llm-reasoning](), [markets](), [prediction](), [prediction-markets]() ### AI overview The hierarchical agentic reduction (HAR) pipeline turns prediction-market question and resolution text into inspectable cross-event relation graphs. It clusters markets by topic, applies typed LLM reasoning to small batches, and proposes signed links with rationales. Across eight monthly Kalshi catalogs and 30.3 million trades, linked pairs had higher signed price-change correlations than matched, unlinked pairs from the same topic at identical timestamps. This difference was statistically significant for Pearson correlations under full-sample and leave-first-week-out evaluation, also appeared with Spearman correlations, and grew for medium- and high-strength links. The abstract reports evidence that these text-based relations align with observed market behavior; it does not establish that they predict future prices. ### Source excerpt arXiv:2512.02436v4 Announce Type: replace 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. ## Novig's Sydney Sweeney Ad Briefly Surpassed Kalshi in US Search Interest DevFeed: [Novig's Sydney Sweeney Ad Briefly Surpassed Kalshi in US Search Interest]() Original publisher: [Read original article]() Author: EdwinMunyui Published: 2026-09-23T05:10:49Z Content type: article Language: en Sources: [HackerNoon]() Topics: [Users](), [digital]() Tags: [acquisition](), [competition](), [customer](), [digital-ad-spending](), [football](), [kalshi](), [markets](), [polymarket](), [prediction-markets](), [prediction-trading](), [sports](), [sports-betting-ads](), [sydney-sweeney](), [trader-acquisition](), [trading](), [us](), [volume]() ### AI overview Novig briefly surpassed Kalshi and Polymarket in US search interest after launching a Sydney Sweeney advertisement. The article examines whether that attention translated into regular traders, noting that public data cannot establish campaign-driven customer acquisition or costs. ### Source excerpt Novig overtook Kalshi in US search interest for three days after its Sydney Sweeney ad. But did the campaign turn attention into active traders? ## Testing Trust in Prediction Markets | Part 2 DevFeed: [Testing Trust in Prediction Markets | Part 2]() Original publisher: [Read original article]() Author: David Phillips Published: 2026-09-10T17:44:43Z Content type: opinion Language: en Sources: [Sift Science]() Topics: [trust](), [trust and safety]() Tags: [betting](), [collective](), [contracts](), [data](), [data-insights](), [decentralized](), [digital-trust](), [discovery](), [fraud](), [gambling](), [integrity](), [kalshi](), [manipulation](), [markets](), [military](), [minutes](), [model](), [oracle](), [polymarket](), [prediction-markets](), [sports](), [trust]() ### AI overview Prediction market forecasts reflect the rules and decision structures behind them, which create distinct integrity risks. Kalshi uses detailed contract rules and centralized resolution; the excerpt says this can produce outcomes at odds with common-sense interpretations. Polymarket uses decentralized, oracle-based resolution, where token-holder discretion may affect outcomes. Cited analyses found that five large UMA wallets held about 40% of votes and that some Polymarket traders voted on markets where they had positions. The excerpt also reports that 0.1% of Polymarket accounts received 67% of profits, while cautioning that concentration alone does not prove manipulation or fraud. Separately, the Anti-Corruption Data Collective reported that long-shot military contracts with implied odds below 35% paid off about 52% of the time. The excerpt frames these issues as questions about representation, contract interpretation, and market legitimacy. ### Source excerpt Missed Part 1? Read it here. Prediction markets make a bold claim that they are engines of truth discovery. Proponents argue that by aggregating collective judgment, prediction markets generate forecasts that are far more valuable than traditional gambling or sports betting. While that claim has merit, a prediction market's output is not merely a well [...] The post Testing Trust in Prediction Markets | Part 2 appeared first on Sift.