Zhihao Jia (@jiazhihao) 's Twitter Profile
Zhihao Jia

@jiazhihao

Assistant professor of Computer Science at Carnegie Mellon University. Research on systems and machine learning.

ID: 777869916

linkhttps://www.cs.cmu.edu/~zhihaoj2/ calendar_today24-08-2012 10:21:01

153 Tweet

2,2K Followers

640 Following

Matei Zaharia (@matei_zaharia) 's Twitter Profile Photo

Excited to launch Agent Bricks, a new way to build auto-optimized agents on your tasks. Agent Bricks uniquely takes a *declarative* approach to agent development: you tell us what you want, and we auto-generate evals and optimize the agent. databricks.com/blog/introduci…

Yixin Dong (@yi_xin_dong) 's Twitter Profile Photo

Databricks 's Agent Bricks is powered by XGrammar for structured generation, and achieves high quality and efficiency. It helps you complete AI tasks without needing to worry about the algorithmic details. Give it a try!

Tianqi Chen (@tqchenml) 's Twitter Profile Photo

Check out our work on parallel reasoning 🧠; We bring an AI-assisted curator that identifies parallel paths in sequential traces, then tune models into native parallel thinkers that runs efficiently with prefix sharing and batching. Really excited about this general direction

Beidi Chen (@beidichen) 's Twitter Profile Photo

Say hello to Multiverse — the Everything Everywhere All At Once of generative modeling. 💥 Lossless, adaptive, and gloriously parallel 🌀 Now open-sourced: multiverse4fm.github.io I was amazed how easily we could extract the intrinsic parallelism of even SOTA autoregressive

Zhihao Jia (@jiazhihao) 's Twitter Profile Photo

📢Exciting updates from #MLSys2025! All session recordings are now available and free to watch at mlsys.org. We’re also thrilled to announce that #MLSys2026 will be held in Seattle next May—submissions open next month with a deadline of Oct 30. We look forward to

📢Exciting updates from #MLSys2025! All session recordings are now available and free to watch at mlsys.org.
We’re also thrilled to announce that #MLSys2026 will be held in Seattle next May—submissions open next month with a deadline of Oct 30. We look forward to
Tianqi Chen (@tqchenml) 's Twitter Profile Photo

#MLSys2026 will be led by the general chair Luis Ceze and PC chairs Zhihao Jia and Aakanksha Chowdhery. The conference will be held in Bellevue on Seattle's east side. Consider submitting and bringing your latest works in AI and systems—more details at mlsys.org.

Anjiang Wei (@anjiangw) 's Twitter Profile Photo

We introduce CodeARC, a new benchmark for evaluating LLMs’ inductive reasoning. Agents must synthesize functions from I/O examples—no natural language, just reasoning. 📄 arxiv.org/pdf/2503.23145 💻 github.com/Anjiang-Wei/Co… 🌐 anjiang-wei.github.io/CodeARC-Websit… #LLM #Reasoning #LLM4Code #ARC

We introduce CodeARC, a new benchmark for evaluating LLMs’ inductive reasoning. Agents must synthesize functions from I/O examples—no natural language, just reasoning.
📄 arxiv.org/pdf/2503.23145
💻 github.com/Anjiang-Wei/Co…
🌐 anjiang-wei.github.io/CodeARC-Websit…
#LLM #Reasoning #LLM4Code #ARC
NovaSky (@novaskyai) 's Twitter Profile Photo

✨Release: We upgraded SkyRL into a highly-modular, performant RL framework for training LLMs. We prioritized modularity—easily prototype new algorithms, environments, and training logic with minimal overhead. 🧵👇 Blog: novasky-ai.notion.site/skyrl-v01 Code: github.com/NovaSky-AI/Sky…

✨Release: We upgraded SkyRL into a highly-modular, performant RL framework for training LLMs. We prioritized modularity—easily prototype new algorithms, environments, and training logic with minimal overhead.

🧵👇
Blog: novasky-ai.notion.site/skyrl-v01
Code: github.com/NovaSky-AI/Sky…
Francis Y. Yan (@francisyan_) 's Twitter Profile Photo

🚀 [OSDI ’25, Tue 11:10am] How do you “divide and conquer” large-scale resource allocation problems like GPU cluster scheduling or WAN traffic engineering? Our answer: “decouple and decompose” the underlying optimization using DeDe. (1/3)

🚀 [OSDI ’25, Tue 11:10am]
How do you “divide and conquer” large-scale resource allocation problems like GPU cluster scheduling or WAN traffic engineering? Our answer: “decouple and decompose” the underlying optimization using DeDe. (1/3)
Wentao Guo (@wentaoguo7) 's Twitter Profile Photo

🦆🚀QuACK🦆🚀: new SOL mem-bound kernel library without a single line of CUDA C++ all straight in Python thanks to CuTe-DSL. On H100 with 3TB/s, it performs 33%-50% faster than highly optimized libraries like PyTorch's torch.compile and Liger. 🤯 With Ted Zadouri and Tri Dao

🦆🚀QuACK🦆🚀: new SOL mem-bound kernel library without a single line of CUDA C++ all straight in Python thanks to CuTe-DSL. On H100 with 3TB/s, it performs 33%-50% faster than highly optimized libraries like PyTorch's torch.compile and Liger. 🤯 

With <a href="/tedzadouri/">Ted Zadouri</a> and <a href="/tri_dao/">Tri Dao</a>