Ziming Liu

@ZimingLiu11 · joined 07 May 2021

Assistant Professor @Tsinghua_Uni CollegeAI. Chief Scientist @MetaCircle_AI Science of AI, AI for AI, AI for Science, KAN.

14 462Followers

890Following

680Posts total

2MViews on collected posts

Against accounts of the same size

5 posts from the last 90 days, next to the 10K–100K follower range. shown to more people than peers of the same size.

Median views6 276this account2 747median for 10K–100K

Reach, %43.40%this account8.35%median for 10K–100K

Engagement, %1.38%this account1.45%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	6 276	2 747	2.28×
Reach (views ÷ followers)	43.40%	8.35%	5.20×
Engagement rate	1.38%	1.45%	0.96×

Others in this range → Compare with another account → How these benchmarks are built →

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

1M1 May

5K14 Aug

921.4K21 Aug

6.3K23 Aug

9.6K28 Aug

5.1K30 Aug

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

0.62%1 May

1.38%14 Aug

0.54%21 Aug

1.69%23 Aug

0.98%28 Aug

1.82%30 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.45%.

What the audience does

Likes61.8%9 495 in total

Reposts10.7%1 641 in total

Replies1.6%241 in total

Quotes2.3%353 in total

Bookmarks23.6%3 625 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.

Latest posts

Foundation Model is dead, Meta Model is future. Stage 1: Expert systems -- Specialized models Stage 2: Foundation models -- Unified models Stage 3: Meta Model -- A unified model that designs specialized models 天下大势，分久必合，合久必分。 https://t.co/Je1xpGxwwf https://t.co/MgmDDWElS7 5.1K views · 75 likes · 12 reposts · 5 replies 30 Aug 2026 At @MetaCircle_AI , we are building a "Meta Model" (a model for model) that aims to predict model preformance and design better models automatically. Here is our prediction for the Marin Hero Run: https://t.co/SEsSCZVVtk Can't wait Marin's final results to be revealed ! https:/ 9.6K views · 79 likes · 11 reposts · 4 replies 28 Aug 2026 Staring from 2022, I have the habit of MANUALLY browsing and collecting fun arXiv papers, gathering ~2000 papers in total. Today we release this paper list, and test if an LLM agent could learn my taste simply from this paper list. https://t.co/RNv4M7kRwc https://t.co/nvp8IIQf5 6.3K views · 88 likes · 11 reposts · 6 replies 23 Aug 2026 🚢 Marin 535B-A23B started training this week! As usual, the whole process is open. Voyage plan: pretraining (80%) + midtraining (20%) on 18.75T tokens on 11 x GB200 NVL72 for ~3 months (2.7e24 FLOPs). Post-training will follow. Before kicking off the run, we trained a 4-rung htt 921.4K views · 4.2K likes · 577 reposts · 107 replies 21 Aug 2026 Astronomy has three stages: Tycho — Kepler — Newton. Previously, the “Physics of AI” is still in the pre-Tycho era — we only observed a few stars (e.g, scaling laws, grokking) in a vast phenomenological sky. Today, we are finally entering the Tycho era. https://t.co/mcPMtJtmOW 5K views · 60 likes · 3 reposts · 6 replies 14 Aug 2026 MLPs are so foundational, but are there alternatives? MLPs place activation functions on neurons, but can we instead place (learnable) activation functions on weights? Yes, we KAN! We propose Kolmogorov-Arnold Networks (KAN), which are more accurate and interpretable than MLPs.🧵 1M views · 5K likes · 1K reposts · 113 replies 01 May 2024