Will CMI declare a Millennium Prize Problem solved by ___?

According to the latest prediction market data for the query “Will CMI declare a Millennium Prize Problem solved by ___?”, traders have formed a strong consensus.

Currently, December 31, 2028 is dominating the market with an overwhelming 35.5% chance of winning. December 31, 2027 follows in second place at 14.5%, while December 31, 2026 sits in third with 3.4%. The betting volume for this specific market has already reached $444.8K, reflecting intense industry interest.

To better assess where each potential outcome stands, the market can be segmented into three distinct trading tiers based on implied probability and contract pricing:

🥇 Tier 1: The Dominant Leader

• December 31, 2028 (35.5%): Currently commanding the highest probability, December 31, 2028 is heavily favored by the order book. Traders looking to back this outcome face a “Buy Yes” contract price of 36¢, signaling a high degree of market conviction. This contract alone has generated $60.8K in volume.

🥈 Tier 2: The Primary Challengers

• December 31, 2027 (14.5%): Positioned as the most viable alternative, December 31, 2027 maintains a 14.5% chance of resolving true. Its “Buy Yes” shares currently trade at 14¢.
• December 31, 2026 (3.4%): Sitting in third place with a 3.4% probability, the market shows measured skepticism toward December 31, 2026, treating it as an outside wildcard unless momentum shifts.

This market will resolve to "Yes" if the Clay Mathematics Institute (CMI) awards a Millennium Prize for any of the six remaining unsolved Millennium Prize Problems, or otherwise officially declares any of these problems solved on its website or in an official communication, by 11:59 PM ET on the date specified in the title. Otherwise, this market will resolve to "No".

This market will resolve solely based on a qualifying declaration by CMI that one of the six remaining unsolved Millennium Prize Problems has been solved, regardless of whether the solution takes the form of a positive proof, counterexample, disproof, or any other resolution accepted by CMI.

A Millennium Prize award will qualify as an official declaration, but is not required; an official CMI statement that a problem has been solved is sufficient on its own. Whether the solution was produced in whole or in part by artificial intelligence, and whether any recipient accepts or declines a prize, will not be considered.

A published or publicly posted proof will not qualify on its own, regardless of its acceptance by the mathematical community.

The primary resolution source will be official information from the Clay Mathematics Institute (e.g., https://www.claymath.org); however, a consensus of credible reporting may be used to confirm that a qualifying CMI declaration or award has occurred.

While human consensus and speculative volume shape the broader prediction market, our quantitative algorithms offer a data-driven counter-perspective. By analyzing fundamental signals, underlying trends and historical distributions, our AI Valuation model calculates an independent “Fair Value” probability for each outcome.

Comparing this Fair Value against the current Trade Value uncovers major disparities — known as the Expected Value (EV) Gap. Contracts with a positive EV Gap represent statistically underpriced outcomes, whereas a negative EV Gap flags a potential market overreaction.

Top AI Alpha & Mispriced Arbitrage Opportunities

Based on the latest data model run, several key contracts stand out with significant deviations:

• The Best Value Play (Highest EV) Our model identifies December 31, 2028 as the premium value opportunity on the board. While the market only assigns it a 35.5% trading probability, our AI’s Fair Value assessment sits at 56.3% — yielding an impressive +20.8% EV Gap.
• Under-the-Radar Dark Horses Other notable discrepancies include December 31, 2027 (EV Gap: +19.5%) and December 31, 2026 (EV Gap: +3.4%). These long-tail opportunities are heavily discounted by the live order books despite stronger statistical backing from our predictive model.