Muhang Xie

Incoming PhD in SII & GAIR

[Pengfei Liu @stefan_fee]

Can AI accelerate AI itself? Let's look at what actually happened!!!

AI autonomously discovered a new RL algorithm, invented 100+ novel architectures, and evolved a data curation strategy — each one outperforming the best human-designed counterparts:

🤖 RL algorithm design — discovered novel optimization mechanisms that beat human-designed GRPO by +12.5 pts on AMC
🧠 Neural architecture search — invented 100+ new architectures, surpassing Mamba, GatedDeltaNet, and more by +0.97 pts over SOTA
📦 Pretraining data curation — evolved a pipeline that outperforms FineWeb-Edu, DCLM, and Nemotron-CC by +18 pts on MMLU
🧬 Drug-target interaction — discovered a new architecture that outperforms all prior human-designed methods by +6.94 AUROC
These aren't toy demos. These are frontier-level results — produced without a human in the loop.

This is ASI-Evolve: an open-source agentic framework that closes the loop between knowledge → hypothesis → experiment → analysis, and repeats it autonomously until it finds something that works. We built it for AI research. But the loop doesn't care about the domain.

A year ago, we made a prediction:

Scale computation → Scale scientific discovery.

The world caught up faster than we expected.

Which raises a question we can't stop thinking about — and we think you shouldn't either:

In the post-AGI era, when AI generates breakthroughs faster than humanity can absorb them — when the rate of discovery permanently outpaces the rate of value conversion — what becomes the new scarcity?

Not intelligence. Not data. Not compute.

Something far more irreplaceable.

We don't have the full answer. But we think it's worth asking — loudly, and together.

💻 Code: https://t.co/rBicTvkxYT
📄 Paper: https://t.co/2DriXJ4uFl (1/8)
⭐ Star it. Fork it. Point it at your hardest problem.

[Muhang Xie @Midoria_7]

Let’s build pre-training science together!

[Yi wei Qin @QinYi88814]

🚨 We can download models, but not see how they were built.
Introducing daVinci-LLM: most transparent LLM pretraining project.
🔓 Open source: model weights, data pipeline, training process, ablations.
🎯 3B model matching 7B performance
🔗 Report: https://t.co/HgTcXhSQSS (1/7)