Jacob Andreas

@jacobandreas · Cambridge, MA · joined 25 Mar 2007

Teaching computers to read. Assoc. prof @MITEECS / @MIT_CSAIL / @NLP_MIT (he/him). https://t.co/5kCnXHjtlY https://t.co/2A3qF5vdJw

24 918Followers

956Following

2 857Posts total

200.6KViews on collected posts

Against accounts of the same size

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

Median views11 940this account1 384median for 10K–100K

Reach, %47.92%this account4.31%median for 10K–100K

Engagement, %0.68%this account1.92%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	11 940	1 384	8.63×
Reach (views ÷ followers)	47.92%	4.31%	11.1×
Engagement rate	0.68%	1.92%	0.35×

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

26.9K29 Jun

36.4K1 Jul

8.8K

31.6K3 Jul

11.9K

37.9K7 Jul

1.6K

750

270

34.4K23 Jul

10.1K24 Jul

Last 11 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.69%29 Jun

0.68%1 Jul

0.83%

0.55%3 Jul

0.62%

1.55%7 Jul

0.32%

1.47%

1.11%

0.48%23 Jul

0.64%24 Jul

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.92%.

What the audience does

Likes60.5%1 334 in total

Reposts8.7%191 in total

Replies1.6%36 in total

Quotes1.3%28 in total

Bookmarks28.0%617 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

👉 New preprint! (and a direction I'm excited to do more work on soon) 10.1K views · 63 likes · 1 reposts · 1 replies 24 Jul 2026 Pluralistic alignment is thriving as a research agenda yet failing at its goal: making the AI systems people actually use more pluralistic🌈 🚨New position paper: we argue adoption in deployed models should be the fields main goal & we provide a roadmap of how to get there 🧵1 34.4K views · 121 likes · 28 reposts · 7 replies 23 Jul 2026 @jacobandreas @evanqed @arnab_api Your name is on it too and it’s kind of weird that you wrote your post not being explicit about that.. You shouldn’t feel like there’s anything wrong with promoting your own research. 270 views · 3 likes · 0 reposts · 0 replies 07 Jul 2026 @jacobandreas @evanqed @arnab_api one of the earliest (mech) interp papers that I remember reading! Also a good follow-up: https://t.co/8dgm091XtR 750 views · 11 likes · 0 reposts · 0 replies 07 Jul 2026 @jacobandreas @evanqed @arnab_api Jacob-ian there, done that! 1.6K views · 4 likes · 0 reposts · 1 replies 07 Jul 2026 If you're excited about Anthropic's J-space work, definitely worth checking out the original paper on Jacobian lenses by @evanqed and @arnab_api! https://t.co/gttqzgB4VB 37.9K views · 511 likes · 62 reposts · 10 replies 07 Jul 2026 👉 New preprint (we had a big backlog 😅)! Revisiting adversarial imitation learning for the era of RLVR: 11.9K views · 62 likes · 8 reposts · 4 replies 03 Jul 2026 Higher benchmark scores do not always mean better models for users. Why? We claim that RL teaches LMs to be correct but not how to be correct: code can pass tests but be unreadable; explanations can be right but unclear. How do we train LMs to be right in the right way? (1/n) 31.6K views · 131 likes · 33 reposts · 5 replies 03 Jul 2026 👉 Preprint: understanding learning dynamics & mechanisms in LMs trained to explain / predict their own behaviors! 8.8K views · 64 likes · 7 reposts · 2 replies 01 Jul 2026 New Paper 📄: LMs just want to explain themselves! When we SFT an LM on explanations of its own behaviors, do they learn to actually introspect, or do they merely imitate the original training distribution? We find evidence for the former. Despite training on a static set of http 36.4K views · 196 likes · 36 reposts · 6 replies 01 Jul 2026 👉 New preprint! Automated interpretability by approximating / replacing NN components (here attention heads) with programs. 26.9K views · 168 likes · 16 reposts · 0 replies 29 Jun 2026