Alexander Wei ✓
@alexwei_ · San Francisco, CA · joined 12 Mar 2022
Reasoning @OpenAI 🍓 @UCBerkeley PhD '23 | @Harvard '20
24 866Followers
218Following
95Posts total
25.3MViews on collected posts
โพสต์ล่าสุด
กำลังค้นหาโพสต์ใหม่…
It is remarkable—and takes a moment to process—how quickly the next generation of models will accelerate our research, and breathe new life into old problems.
This was an incredible project done by an incredible team (and model) in just a week. Ad Astra we go ✨ https://t.co/XIy2
67.6K views · 442 likes
· 44 reposts · 22 replies
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A delightful two-page proof of the Cycle Double Cover Conjecture, from a model you can access at home!
13.6K views · 121 likes
· 6 reposts · 3 replies
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Yesterday, we made GPT-5.6 Sol Ultra generally available. Today, we're sharing that it produced a proof of the 50-year-old Cycle Double Cover Conjecture using 64 subagents in just under one hour. We're sharing the prompt and proof below. We're excited to see what you all do with
2.5M views · 6.8K likes
· 545 reposts · 232 replies
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1/ Ten months ago, I was ecstatic that AI could win IMO gold.
Today, that excitement feels quaint: an internal @OpenAI model has refuted Erdos’s unit distance conjecture—a research result that one could recommend “acceptance without any hesitation” to the Annals of Mathematics.
187.3K views · 773 likes
· 52 reposts · 12 replies
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Today, we share a breakthrough on the planar unit distance problem, a famous open question first posed by Paul Erdős in 1946.
For nearly 80 years, mathematicians believed the best possible solutions looked roughly like square grids.
An OpenAI model has now disproved that https:
13.7M views · 26.4K likes
· 3.8K reposts · 1.2K replies
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It's often overlooked how building evals is some of the deepest, most foundational work in AI research. Congrats to @tejalpatwardhan and team!!
Here's my favorite plot from the paper—brings into focus the current pace of progress: https://t.co/QKKmHfMlGW
10.1K views · 48 likes
· 3 reposts · 2 replies
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Understanding the capabilities of AI models is important to me. To forecast how AI models might affect labor, we need methods to measure their real-world work abilities. That’s why we created GDPval. https://t.co/YsQvmdGK94
1.1M views · 1.3K likes
· 184 reposts · 58 replies
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@alexwei_ This is cool; it seems you already have models surpassing GPT-5 internally.
3K views · 17 likes
· 0 reposts · 1 replies
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@alexwei_ @OpenAI So what's the next goalpost?
What's the next thing LLMs will never be able to do?
49.1K views · 253 likes
· 2 reposts · 28 replies
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11/N Lastly, we'd like to congratulate all the participants of the 2025 IMO on their achievement! We are proud to have many past IMO participants at @OpenAI and recognize that these are some of the brightest young minds of the future.
102.9K views · 686 likes
· 12 reposts · 38 replies
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9/N Still—this underscores how fast AI has advanced in recent years. In 2021, my PhD advisor @JacobSteinhardt had me forecast AI math progress by July 2025. I predicted 30% on the MATH benchmark (and thought everyone else was too optimistic). Instead, we have IMO gold. https://t.
122.8K views · 748 likes
· 47 reposts · 6 replies
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10/N If you want to take a look, here are the model’s solutions to the 2025 IMO problems! The model solved P1 through P5; it did not produce a solution for P6. (Apologies in advance for its … distinct style—it is very much an experimental model 😅)
https://t.co/Pm3qd8BXQs
150.9K views · 789 likes
· 46 reposts · 18 replies
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7/N HUGE congratulations to the team—@SherylHsu02, @polynoamial, and the many giants whose shoulders we stood on—for turning this crazy dream into reality! I am lucky I get to spend late nights and early mornings working alongside the very best.
113.8K views · 640 likes
· 13 reposts · 7 replies
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8/N Btw, we are releasing GPT-5 soon, and we’re excited for you to try it. But just to be clear: the IMO gold LLM is an experimental research model. We don’t plan to release anything with this level of math capability for several months.
523.7K views · 1.8K likes
· 186 reposts · 43 replies
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6/N In our evaluation, the model solved 5 of the 6 problems on the 2025 IMO. For each problem, three former IMO medalists independently graded the model’s submitted proof, with scores finalized after unanimous consensus. The model earned 35/42 points in total, enough for gold! 🥇
158.5K views · 718 likes
· 25 reposts · 7 replies
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5/N Besides the result itself, I am excited about our approach: We reach this capability level not via narrow, task-specific methodology, but by breaking new ground in general-purpose reinforcement learning and test-time compute scaling.
276.8K views · 985 likes
· 59 reposts · 10 replies
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4/N Second, IMO submissions are hard-to-verify, multi-page proofs. Progress here calls for going beyond the RL paradigm of clear-cut, verifiable rewards. By doing so, we’ve obtained a model that can craft intricate, watertight arguments at the level of human mathematicians. https
193.6K views · 885 likes
· 38 reposts · 9 replies
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3/N Why is this a big deal? First, IMO problems demand a new level of sustained creative thinking compared to past benchmarks. In reasoning time horizon, we’ve now progressed from GSM8K (~0.1 min for top humans) → MATH benchmark (~1 min) → AIME (~10 mins) → IMO (~100 mins).
141.8K views · 768 likes
· 28 reposts · 3 replies
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2/N We evaluated our models on the 2025 IMO problems under the same rules as human contestants: two 4.5 hour exam sessions, no tools or internet, reading the official problem statements, and writing natural language proofs. https://t.co/eCehaJeYgi
157.4K views · 829 likes
· 31 reposts · 14 replies
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1/N I’m excited to share that our latest @OpenAI experimental reasoning LLM has achieved a longstanding grand challenge in AI: gold medal-level performance on the world’s most prestigious math competition—the International Math Olympiad (IMO). https://t.co/SG3k6EknaC
5.7M views · 7.2K likes
· 1.3K reposts · 393 replies
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เทียบกับบัญชีขนาดเดียวกัน
3 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงในวงกว้าง แต่มีผู้ชมตอบสนองน้อย.
ยอดดูมัธยฐาน67 644บัญชีนี้935ค่ามัธยฐานของ 10K–100K
การเข้าถึง, %272.03%บัญชีนี้3.43%ค่ามัธยฐานของ 10K–100K
การมีส่วนร่วม, %0.76%บัญชีนี้1.55%ค่ามัธยฐานของ 10K–100K
| ตัวชี้วัด | บัญชีนี้ | ค่ามัธยฐานของ 10K–100K | อัตราส่วน |
|---|---|---|---|
| ยอดดูมัธยฐานต่อโพสต์ | 67 644 | 935 | 72.3× |
| การเข้าถึง (ยอดดู ÷ ผู้ติดตาม) | 2.7× audience | 3.43% | 79.3× |
| อัตราการมีส่วนร่วม | 0.76% | 1.55% | 0.49× |
บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
523.7K19 Jul
113.8K
150.9K
122.8K
102.9K
49.1K
3K
1.1M25 Sep
10.1K
13.7M20 May
187.3K
2.5M10 Jul
13.6K
67.6K1 Aug
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.41%19 Jul
0.58%
0.58%
0.66%
0.72%
0.58%
0.61%
0.15%25 Sep
0.52%
0.24%20 May
0.45%
0.32%10 Jul
0.97%
0.76%1 Aug
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.55%.
What the audience does
Likes68.9%52 210 in total
Reposts8.4%6 381 in total
Replies2.8%2 118 in total
Quotes4.1%3 121 in total
Bookmarks15.8%11 933 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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