Trader consensus on the 96.5% implied probability for “No” reflects the continued dominance of transformer-based autoregressive models from leading labs, with recent dLLM releases such as LLaDA 100B, Mercury 2, and Gemini Diffusion demonstrating competitive performance only on narrower tasks or smaller scales rather than frontier benchmarks. Diffusion architectures offer parallel decoding and reversal-curse advantages, yet they lag in overall capability and have not displaced top models through 2026. Remaining months to 2027 limit scope for sudden breakthroughs, though unexpected scaling successes, hybrid transformer-diffusion designs, or major lab pivots could still shift outcomes before year-end.
基于Polymarket数据的AI实验性摘要。这不是交易建议,也不影响该市场的结算方式。 · 更新于是
是
A Diffusion Large Language Model (dLLM) is any model for which official publicly released documentation, such as a model card, technical paper, or official statements from its developers, clearly identifies diffusion or iterative denoising as a central part of its text-generation or decoding process.
Results from the "Score" section on the Leaderboard tab of https://lmarena.ai/leaderboard/text set to default (style control on) will be used to resolve this market.
If two or models are tied for the top arena score at any point, this market will resolve to “Yes” if any of the joint-top ranked models are Diffusion Large Language Models.
The resolution source for this market is the Chatbot Arena LLM Leaderboard found at https://lmarena.ai/. If this resolution source is unavailable on December 31, 2026, 11:59 PM ET, this market will resolve based on all published Chatbot Arena LLM Leaderboard rankings prior to the period of lack of availability.
市场开放时间: Nov 14, 2025, 3:05 PM ET
Resolver
0x65070BE91...A Diffusion Large Language Model (dLLM) is any model for which official publicly released documentation, such as a model card, technical paper, or official statements from its developers, clearly identifies diffusion or iterative denoising as a central part of its text-generation or decoding process.
Results from the "Score" section on the Leaderboard tab of https://lmarena.ai/leaderboard/text set to default (style control on) will be used to resolve this market.
If two or models are tied for the top arena score at any point, this market will resolve to “Yes” if any of the joint-top ranked models are Diffusion Large Language Models.
The resolution source for this market is the Chatbot Arena LLM Leaderboard found at https://lmarena.ai/. If this resolution source is unavailable on December 31, 2026, 11:59 PM ET, this market will resolve based on all published Chatbot Arena LLM Leaderboard rankings prior to the period of lack of availability.
Resolver
0x65070BE91...Trader consensus on the 96.5% implied probability for “No” reflects the continued dominance of transformer-based autoregressive models from leading labs, with recent dLLM releases such as LLaDA 100B, Mercury 2, and Gemini Diffusion demonstrating competitive performance only on narrower tasks or smaller scales rather than frontier benchmarks. Diffusion architectures offer parallel decoding and reversal-curse advantages, yet they lag in overall capability and have not displaced top models through 2026. Remaining months to 2027 limit scope for sudden breakthroughs, though unexpected scaling successes, hybrid transformer-diffusion designs, or major lab pivots could still shift outcomes before year-end.
基于Polymarket数据的AI实验性摘要。这不是交易建议,也不影响该市场的结算方式。 · 更新于



警惕外部链接哦。
警惕外部链接哦。
常见问题