GPT Astra 6.1+: HLE-Diamond Debut?

根據「GPT Astra 6.1+: HLE-Diamond Debut?」的最新預測市場資料，交易者已形成強烈共識。

目前，60%+ 以壓倒性的 63% 獲勝機率主導市場；55%+ 以 59% 位居第二，65%+ 以 25% 排名第三。該市場的下注量已達 $112K，反映出市場的高度關注。

為了更好地評估各潛在結果的位置，可依據隱含機率與合約定價將市場劃分為三個明顯的交易梯隊：

🥇 第一梯隊：絕對領跑者

• 60%+ (63%)：60%+ 目前擁有最高機率，深受訂單簿青睞。看好該結果的交易者面對的「Buy Yes」合約價為 63¢，顯示出市場的高度確信。僅該合約就已產生 $22.6K 的成交量。

🥈 第二梯隊：主要挑戰者

• 55%+ (59%)：作為最可行的替代選項，55%+ 保持著 59% 的成真機率，其「Buy Yes」份額目前成交價為 59¢。
• 65%+ (25%)：以 25% 的機率位列第三，市場對 65%+ 持謹慎懷疑態度，除非勢頭轉變，否則視其為外圍黑馬。

🥉 第三梯隊：長尾選項（合計約 0%）

在前三名之外，還有大量宏觀變數與冷門結果被持續追蹤。儘管單個機率偏低，但它們是投機交易者的重要對沖：

• 替代選項：包括 70%+ (19.5%)。
• 投機成交：儘管統計機率偏低，像 70%+ 這類長尾合約仍吸引著可觀的關注。

This market will resolve to "Yes" if the next OpenAI GPT Astra model (6.1+) added to the Humanity's Last Exam results at https://agi.safe.ai/ has an HLE-Diamond Accuracy of at least the specified percentage at 12:00 PM ET on the calendar date following the date on which it first appears on the site. Otherwise, this market will resolve to "No".

A qualifying model must be an OpenAI model designated as version 6.1 or higher that includes "Astra" in its name or model identifier, regardless of capitalization or surrounding prefixes, suffixes, dates, or descriptors. For example, GPT-6.1 Astra, Astra 6.1, or GPT Astra 7 would all qualify. GPT-6 Astra, including any rollout, promotion, Pro or Fast mode, or snapshot of it, will not qualify. Tier-labeled models such as GPT-5.6 Sol, GPT-5.6 Terra, or GPT-5.6 Luna and their successors, and task-specific or modality lines such as GPT-5.6-Cyber, Daybreak models, Codex, GPT Image, or Realtime models, will not qualify.

The percentage displayed as "HLE-Diamond Accuracy" for the model in its result card on the "AI Progress on HLE-Diamond" chart at https://agi.safe.ai/ will be used to resolve this market. This market will resolve solely based on the HLE-Diamond Accuracy, regardless of the model's Calibration Error, chart position, other configuration scores, any note that a score was computed on a sample of questions, or any underlying granular or unrounded data presented elsewhere.

If the Center for AI Safety (CAIS), which maintains the official Humanity's Last Exam results, publishes a qualifying score (defined below) for the model, and the model has not been displayed on the chart by 11:59 PM ET on the third calendar day (ET) after the first such publication, that score will be used instead, rounded to one decimal place. If multiple qualifying scores for the model have been published by that time, the highest will be used.

A qualifying score is one issued under CAIS's name (for example, on https://agi.safe.ai/, https://lastexam.ai/, or https://dashboard.safe.ai/, including jointly with Scale AI) that is clearly identified as overall accuracy on the full HLE-Diamond question set (1,000 questions, released September 22, 2026), without tools, at the reasoning setting labeled "high" in HLE-Diamond's main results.

No score other than the chart value or a qualifying score will count, even if no qualifying score is published. This includes scores on the original Humanity's Last Exam or HLE-Rolling, "reasoning max" or "with tools" results, Reasoning or Knowledge partition scores, values available only through an API, data file, or page source, scores from Scale AI's separate leaderboards, other evaluators, or the model's developer, and any score where there is reasonable doubt that it meets the conditions above.

A model first appears on the site on the earlier of the date (ET) it is first displayed on the chart and the date (ET) a qualifying score for it is first published. If the chart value is used, the check described in the first paragraph takes place at 12:00 PM ET on the calendar date following the model's first display on the chart; if a qualifying score is used, it takes place at the end of the three-day period described above.

If multiple qualifying models are added to the Humanity's Last Exam results on the same calendar date (ET), the model with the highest HLE-Diamond Accuracy will be used for resolution. Models added to the results on the calendar date following the initial qualifying model's first appearance will not be considered.

If a model is first added to the Humanity's Last Exam results but is subsequently removed and not re-added, such that no HLE-Diamond Accuracy for it is available under these rules at the time of the check, its appearance will not qualify as added to the Humanity's Last Exam results.

A qualifying model must be newly added to the Humanity's Last Exam results. Whether the model was previously released, publicly accessible, in beta, or otherwise available before appearing on the site is irrelevant for this market.

In the case that the Resolution Source publishes data, sufficient to settle a Market, that is clearly erroneous and the result of technical or operational error, or of unauthorized interference with the Resolution Source or its data, the Market may remain open for a period of up to seven (7) calendar days (ET) from the time of the original release, in anticipation of a correction by the Resolution Source. If the Resolution Source publishes a corrected value within that period, that value will be eligible for resolution. If no corrected value is published within that period, Polymarket will issue a Clarification to determine whether the originally published value may be eligible for resolution, or whether other action is required to preserve the orderly functioning of the Market.

The primary resolution source for this market is the official Humanity's Last Exam website found at https://agi.safe.ai/. If this resolution source is unavailable at the time of the check, this market will resolve based on the first subsequent instance at which the model's HLE-Diamond Accuracy becomes available. If it remains unavailable through the end of the seventh day after the qualifying model first appears on the site or if no qualifying model is added by June 30, 2027, 11:59 PM ET, this market will resolve to "No".

人群共識與投機成交塑造了更宏觀的預測市場，而我們的量化演算法提供了資料驅動的反向視角。透過分析基本面訊號、底層趨勢與歷史分布，我們的 AI 估值模型為每個結果獨立測算出一個「公允價值」機率。

將該公允價值與當前交易價值對比，可揭示出重大背離——即期望值（EV）差。正 EV 差代表統計上被低估的結果，而負 EV 差則提示市場可能存在反應過度。

頂級 AI Alpha 與錯誤定價套利機會

根據最新一輪資料模型測算，以下幾個關鍵合約存在顯著偏離：

• 最被高估的結果：70%+ 當前交易價為 19.5%，但我們的 AI 測算其公允價值僅為 12.1%，形成 -7.4% 的較大負 EV 差，表明人群可能過度炒作該結果、把溢價推得過高。
• 最佳價值標的（最高 EV）：我們的模型將 55%+ 識別為盤面上最具價值的機會。市場僅給予其 59% 的交易機率，而我們 AI 的公允價值評估為 85.2%——形成可觀的 +26.2% EV 差。
• 被忽視的黑馬：其他值得注意的偏離包括 65%+（EV 差：+7.9%）。儘管我們的預測模型給予更強的統計支撐，這些長尾機會仍被即時訂單簿大幅低估。