Haven Feng ✓

@HavenFeng

Postdoc @berkeley_ai, building Impossible. Prev. @MPI_IS @AdobeResearch @uni_tue Interested in recreating the physical world and the intelligence to do so.

5 228Followers

1 119Following

259Posts total

1.1MViews on collected posts

Последние посты

Haven Feng ✓@HavenFeng16 Jul

@HavenFeng thinking like a physicist is all you need? 6.5K views · 46 likes · 0 reposts · 2 replies Open on X →

Haven Feng ✓@HavenFeng16 Jul

@guanningzeng @Jiani_Wang_ @wenjie_ma @shaofeng_y27736 @arcprize 16.6K views · 98 likes · 0 reposts · 3 replies Open on X →

Haven Feng ✓@HavenFeng16 Jul

[schema]'s saturation of the ARC-AGI-3 public set is only a starting point. There is much more to explore! Full blog: https://t.co/A13KTXczUX Agent traces: https://t.co/Gc6hP4OitW Amazing team effort with @guanningzeng, @Jiani_Wang_, @wenjie_ma, @shaofeng_y27736, https://t.co/b

25.9K views · 223 likes · 14 reposts · 3 replies Open on X →

Haven Feng ✓@HavenFeng16 Jul

[schema] handles the state and mechanism in one editable program, a symbolic world model. It designs experiments to verify hypotheses, backtests the program against history, and plans inside its world at zero action cost. 24.7K views · 143 likes · 6 reposts · 2 replies Open on X →

Haven Feng ✓@HavenFeng16 Jul

ARC-AGI-3 gives an agent a 64×64 grid plus legal actions, no rules, stated goal, or reward. The agent must discover both what the world is and how it works like a physicist: 1. State grounding -> identify objects, relations, and goals. 2. Mechanism discovery -> infer how these h

31.3K views · 203 likes · 4 reposts · 7 replies Open on X →

Haven Feng ✓@HavenFeng16 Jul

Today, we’re introducing [schema]: a harness reaching 99% RHAE with Opus 4.8 + Fable 5 and 95.35% with GPT-5.6 Sol on ARC-AGI-3 Public set. [schema] makes an LLM think like a physicist. 🧵 https://t.co/U0C0JFj62e

0:47

950K views · 2.5K likes · 297 reposts · 125 replies Open on X →

На фоне аккаунтов своего размера

6 постов за последние 90 дней рядом с диапазоном under 10K подписчиков. показывается широко, но откликается мало кто.

Медианные просмотры25 306этот аккаунт4 175медиана для under 10K

Охват, %484.04%этот аккаунт250.82%медиана для under 10K

Вовлечённость, %0.66%этот аккаунт1.41%медиана для under 10K

Показатель	Этот аккаунт	Медиана для under 10K	Отношение
Медианные просмотры на пост	25 306	4 175	6.06×
Охват (просмотры ÷ подписчики)	4.8× audience	2.5× audience	1.93×
Вовлечённость	0.66%	1.41%	0.47×

Другие в этом диапазоне → Сравнить с другим аккаунтом → Как считаются эти ориентиры →

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

950K16 Jul

31.3K

24.7K

25.9K

16.6K

6.5K

Last 6 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.32%16 Jul

0.69%

0.62%

0.94%

0.61%

0.74%

Reactions — likes, reposts, replies and quotes — divided by views. Median for under 10K accounts is 1.41%.

What the audience does

Likes51.5%3 207 in total

Reposts5.2%321 in total

Replies2.3%142 in total

Quotes2.3%143 in total

Bookmarks38.7%2 409 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.

Последние посты

На фоне аккаунтов своего размера

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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