David Dai, @RuizheJia, and @ShihaoY develop a model of linked spot and prediction markets. Manipulation survives in equilibrium, making spot prices less informative but surprisingly increasing spot liquidity because manipulative BTC trades reduce adverse selection
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I solved 6 open Erdős problems in 5 days, using @OpenAI GPT-5.6 Sol.
I have a math background, but the Codex workflow I used does not require deep mathematical knowledge.
Here’s exactly how I approached it, including my prompts 🧵
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🚨New Paper with @Weiye_Xi , @ciamac and @Qiaoqiao2001
Here’s two different prediction markets priced at ~6.5%:
1. Will the US confirm the existence of Aliens in 2026?
2. *that* Spurs @ Knicks Game 4, ~4th quarter.
They suggest that both these events are the same probability, but intuitively these feel very different: the latter (Knicks won!) feels a lot more uncertain. But prediction markets don’t immediately give us a way to quantify it.
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Binance is arguably the world's largest crypto exchange.
Feb 2026: Polymarket lists a small 5-minute Bitcoin contract. Since then, Binance order flow spikes in the final seconds before settlement; prices revert soon after.
New paper with David Dai and Shihao Yu ( @ShihaoY ): Settlement Manipulation in Prediction Markets (
The finding, up front:
Traders push Bitcoin's price in the final seconds to decide the contract. That push makes the price less informative, yet Bitcoin's market more liquid. Market makers are largely insulated; ordinary traders lose $7.6M in two months.
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D² Keynote: Jarry Xiao from @ellipsis_labs
On perpetual DEXs and the infrastructure behind high-performance onchain trading
Explore the full program:
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D² Keynote: Rajiv Sethi
Sharing arguments from his upcoming book, Engines of Prophecy: The Power and Perils of Prediction Markets.
Tickets & full program:
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Joining us at D² as keynote speakers ✨
→ Rajiv Sethi - Engines of Prophecy: The Power & Perils of Prediction Markets
→ Jarry Xiao - Perpetual Decentralized Exchanges
Tickets & full program:
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