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Yisong Yue ✓

@yisongyue · Pasadena, CA · joined 28 Jul 2009

AI Professor @Caltech (@YueLabCaltech)

24 467Followers

3 126Following

8 594Posts total

272.9KViews on collected posts

Latest posts

Yisong Yue ✓@yisongyue7 Aug

Neurosymbolic AI is back. Can’t wait for recursive self-improvement to give us neuro(symbolicneuro)symbolic AI. 4.8K views · 34 likes · 3 reposts · 1 replies Open on X →

Yisong Yue ✓@yisongyue5 Aug

Knowledge flywheels are a new scaling dimension for self-improving AI. https://t.co/gQ4QBMtf7n 137K views · 943 likes · 95 reposts · 27 replies Open on X →

Yisong Yue ✓@yisongyue30 Jul

AI learns to improve AI, full-stack version. 2.8K views · 8 likes · 0 reposts · 0 replies Open on X →

Yisong Yue ✓@yisongyue29 Jul

Knowledge flywheels might be the most effective path to recursive self improvement. Can significantly outperform prompt and harness optimization at a fraction of the cost. 2.5K views · 8 likes · 1 reposts · 1 replies Open on X →

Yisong Yue ✓@yisongyue29 Jul

Amazing work by @AsariAILabs! Full-stack optimization of 1T+ parameter models with rigorous correctness checks. Agentic self-improvement at work. 1.8K views · 11 likes · 0 reposts · 0 replies Open on X →

Yisong Yue ✓@yisongyue29 Jul

The most interesting part? Seeing our agents learn and improve from each run. While optimizing DeepSeek v4 Pro, the agents learned how to avoid a distributed deadlock. When they later hit a similar situation optimizing GLM 5.2, they generalized that insight and saved 44 minutes

12.1K views · 28 likes · 2 reposts · 1 replies Open on X →

Yisong Yue ✓@yisongyue29 Jul

Our self-improving agents optimized the full @vLLM_project inference stack, with up to 16% more throughput and interactivity for @deepseek_ai v4 Pro and @Zai_org GLM 5.2 on B200s (no MTP). Every change was verified and our agents got better and faster at it with each iteration.

0:16

111.9K views · 253 likes · 32 reposts · 12 replies Open on X →

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.