Alexia Jolicoeur-Martineau ✓

@jm_alexia

Principal Researcher @ Microsoft 🐱‍💻 2025 ARC Prize Winner I build generative AI for images, videos, text, tabular data, weights, molecules, and video games.

26 009Followers

2 209Following

9 959Posts total

654.6KViews on collected posts

Against accounts of the same size

4 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views52 832this account1 089median for 10K–100K

Reach, %203.13%this account3.50%median for 10K–100K

Engagement, %0.36%this account2.00%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	52 832	1 089	48.5×
Reach (views ÷ followers)	2.0× audience	3.50%	58.0×
Engagement rate	0.36%	2.00%	0.18×

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

530.8K31 Aug

26.3K

18.2K

79.3K

Last 4 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.35%31 Aug

0.28%

0.38%

0.95%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 2.00%.

What the audience does

Likes61.4%2 450 in total

Reposts3.7%148 in total

Replies1.7%66 in total

Quotes1.8%71 in total

Bookmarks31.4%1 252 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

@jm_alexia @JFPuget @RheaSukthanker @CameronPashmina @Emy_Aze I'm a bit disappointed you made the title so misleading. Just add *in post training* to it, like you did here. 79.3K views · 736 likes · 9 reposts · 7 replies 31 Aug 2026 · Open on X →

Linear attention has limited memory, so it must choose which tokens to remember and which to forget. Trying to learn this during post-training at a small cost is misguided. 18.2K views · 61 likes · 4 reposts · 4 replies 31 Aug 2026 · Open on X →

Retrofitting an LLM to use linear attention sounds great in theory, but it does not deliver its promises. It adds cost and complexity while degrading performance on long context massively. 26.3K views · 67 likes · 4 reposts · 1 replies 31 Aug 2026 · Open on X →

Simple beats complicated: We show that switching to a sliding-window attention mask with attention sinks (at no cost) beats linear attention post-training. Huge thanks to my collaborators @RheaSukthanker, @CameronPashmina, and @Emy_Aze. Paper: https://t.co/h8DIc223Su 530.8K views · 1.6K likes · 131 reposts · 54 replies 31 Aug 2026 · Open on X →