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テクノロジー·AI

DLLMは2027年までにトップAIモデルになるでしょうか？

Market icon
テクノロジー·AI

DLLMは2027年までにトップAIモデルになるでしょうか？

はい

11% chance
Polymarket
NEW

はい

11% chance
Polymarket
NEW
This market will resolve to "Yes" if, at any point before December 31, 2026, 11:59 PM ET, an AI model which is a Diffusion Large Language Model (dLLM) has the highest score based off the Chatbot Arena LLM Leaderboard (https://lmarena.ai/). Otherwise, this market will resolve to "No". 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.Trader consensus on Polymarket assigns an 89.5% implied probability to "No" for a dense large language model (dLLM) topping the LMSYS Chatbot Arena leaderboard before 2027, driven by the entrenched dominance of Mixture-of-Experts (MoE) architectures in frontier AI models. Recent leaderboard updates as of March 2026 show Google's Gemini 3.1 Pro leading at 1505 Elo, followed closely by Anthropic's Claude Opus 4.6—both MoE designs that scale efficiently without activating all parameters per token, outpacing dense models on compute-intensive benchmarks. No verified dense LLM has challenged the top ranks in over a year, amid industry shifts favoring MoE for cost-effective inference at trillion-parameter scales. Key catalysts include anticipated MoE releases from OpenAI and xAI, with low odds (under 6%) on xAI even shipping a dLLM by mid-2026, though a dense efficiency breakthrough could shift sentiment.

Trader consensus on Polymarket assigns an 89.5% implied probability to "No" for a dense large language model (dLLM) topping the LMSYS Chatbot Arena leaderboard before 2027, driven by the entrenched dominance of Mixture-of-Experts (MoE) architectures in frontier AI models. Recent leaderboard updates as of March 2026 show Google's Gemini 3.1 Pro leading at 1505 Elo, followed closely by Anthropic's Claude Opus 4.6—both MoE designs that scale efficiently without activating all parameters per token, outpacing dense models on compute-intensive benchmarks. No verified dense LLM has challenged the top ranks in over a year, amid industry shifts favoring MoE for cost-effective inference at trillion-parameter scales. Key catalysts include anticipated MoE releases from OpenAI and xAI, with low odds (under 6%) on xAI even shipping a dLLM by mid-2026, though a dense efficiency breakthrough could shift sentiment.

Polymarketデータを参照したAI生成の実験的な要約 · 更新日
This market will resolve to "Yes" if, at any point before December 31, 2026, 11:59 PM ET, an AI model which is a Diffusion Large Language Model (dLLM) has the highest score based off the Chatbot Arena LLM Leaderboard (https://lmarena.ai/). Otherwise, this market will resolve to "No". 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.Trader consensus on Polymarket assigns an 89.5% implied probability to "No" for a dense large language model (dLLM) topping the LMSYS Chatbot Arena leaderboard before 2027, driven by the entrenched dominance of Mixture-of-Experts (MoE) architectures in frontier AI models. Recent leaderboard updates as of March 2026 show Google's Gemini 3.1 Pro leading at 1505 Elo, followed closely by Anthropic's Claude Opus 4.6—both MoE designs that scale efficiently without activating all parameters per token, outpacing dense models on compute-intensive benchmarks. No verified dense LLM has challenged the top ranks in over a year, amid industry shifts favoring MoE for cost-effective inference at trillion-parameter scales. Key catalysts include anticipated MoE releases from OpenAI and xAI, with low odds (under 6%) on xAI even shipping a dLLM by mid-2026, though a dense efficiency breakthrough could shift sentiment.

Trader consensus on Polymarket assigns an 89.5% implied probability to "No" for a dense large language model (dLLM) topping the LMSYS Chatbot Arena leaderboard before 2027, driven by the entrenched dominance of Mixture-of-Experts (MoE) architectures in frontier AI models. Recent leaderboard updates as of March 2026 show Google's Gemini 3.1 Pro leading at 1505 Elo, followed closely by Anthropic's Claude Opus 4.6—both MoE designs that scale efficiently without activating all parameters per token, outpacing dense models on compute-intensive benchmarks. No verified dense LLM has challenged the top ranks in over a year, amid industry shifts favoring MoE for cost-effective inference at trillion-parameter scales. Key catalysts include anticipated MoE releases from OpenAI and xAI, with low odds (under 6%) on xAI even shipping a dLLM by mid-2026, though a dense efficiency breakthrough could shift sentiment.

Polymarketデータを参照したAI生成の実験的な要約 · 更新日

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よくある質問

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