Paul Liang ✓

@pliang279 · joined 08 Jul 2012

Assistant Professor MIT @medialab @MITEECS @nlp_mit || Foundations of self-evolving multisensory AI to enhance the human experience.

11 209Followers

462Following

3 781Posts total

198.4KViews on collected posts

Neueste Beiträge

Paul Liang ✓@pliang2798 Sep

@pliang279 @medialab @MITEECS 听起来很棒！新增的多模态代理、推理能力和触觉/嗅觉模态更新非常有前瞻性，结合实际应用案例会更有吸引力。 187 views · 0 likes · 0 reposts · 0 replies Open on X →

Paul Liang ✓@pliang2797 Sep

@pliang279 @medialab @MITEECS 有点意思啊能把共产党 AI 一下吗 xixi 386 views · 0 likes · 0 reposts · 0 replies Open on X →

Paul Liang ✓@pliang2797 Sep

@pliang279 @medialab @MITEECS thanks 548 views · 1 likes · 0 reposts · 0 replies Open on X →

Paul Liang ✓@pliang2797 Sep

Sharing lecture videos for the **How to AI (Almost) Anything/Multimodal AI** course I taught at MIT in spring 2026. This course became quite a hit the last time I shared it in spring 2025. Spring 2026's updated version contains updated topics on multimodal agents, reasoning, htt

56.8K views · 1.2K likes · 184 reposts · 8 replies Open on X →

Paul Liang ✓@pliang2791 Sep

Your inference engine is secretly a continual learner! Reef is an infrastructure we've been building that turns inference into a learning loop. It enables general-purpose model, agent, and harness evolution from the feedback signals naturally produced during deployment, 12.9K views · 87 likes · 12 reposts · 2 replies Open on X →

Paul Liang ✓@pliang27917 Jul

all of this is possible because Russ @rsalakhu is the best advisor anyone could ask for, by creating a supportive, collaborative environment grounded in technical rigor while always encouraging curiosity and exploration 🙏 100.6K views · 150 likes · 6 reposts · 5 replies Open on X →

Paul Liang ✓@pliang27925 Apr

Keane @keanetomato and David @ddvd233 presenting Human Behavior Atlas at #iclr2026 - data resources for training models for human behavior analysis, spanning sentiment, emotions, humor, sarcasm, social interactions, multi-party dynamics etc. using Human Behavior Atlas, we https:

2.7K views · 18 likes · 4 reposts · 1 replies Open on X →

Paul Liang ✓@pliang27925 Apr

Ao @ao_qu18465 presenting MEM1 at #iclr2026 a new RL method to jointly learn reasoning and long context memory management for agents. sota results on long horizon multihop QA, web agents, deep research tasks paper: https://t.co/ljgj60Ink5 code: https://t.co/gUsTGXfU9U 🕥 10:30

3.9K views · 22 likes · 1 reposts · 0 replies Open on X →

Paul Liang ✓@pliang27923 Jul 2025

🚀 Excited to share my first tweet and to introduce our latest work: MEM1: RL for Memory Consolidation in Long-Horizon Agents. Long-horizon agents (e.g., deep research, web agents) typically store all observations, actions, and intermediate thoughts in context. However, much of h

0:32

20.5K views · 63 likes · 14 reposts · 3 replies Open on X →

Im Vergleich zu Konten gleicher Größe

5 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird weit gezeigt, aber nur wenige dieser Zuschauer reagieren.

Medianaufrufe548dieses Konto924Median für 10K–100K

Reichweite, %4.89%dieses Konto3.62%Median für 10K–100K

Interaktion, %0.18%dieses Konto1.52%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	548	924	0.59×
Reichweite (Aufrufe ÷ Follower)	4.89%	3.62%	1.35×
Interaktionsrate	0.18%	1.52%	0.12×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

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

20.5K23 Jul

3.9K25 Apr

2.7K

100.6K17 Jul

12.9K1 Sep

56.8K7 Sep

548

386

1878 Sep

Last 9 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%23 Jul

0.59%25 Apr

0.90%

0.16%17 Jul

0.79%1 Sep

2.40%7 Sep

0.18%

0.00%

0.00%8 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes43.1%1 507 in total

Reposts6.3%221 in total

Replies0.5%19 in total

Quotes0.4%14 in total

Bookmarks49.6%1 734 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.

Neueste Beiträge

Im Vergleich zu Konten gleicher Größe

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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