Yang Li
@LeYangco
PhD candidate @sjtu1896, research intern @AlibabaGroup ATH Token Foundry, researching machine learning with a focus on generative models and optimization.
Our work “Attention Illuminates LLM Reasoning” will be presented at ICML 2026. Happy to connect and discuss with anyone interested in LLM reasoning, interpretability, and RL4LLM. This work investigates how attention patterns can reveal the internal reasoning structure of LLMs.🚀 Happy to present our new work on LLM reasoning! We show that: (1) Attention is a structured map of the model's reasoning logic, uncovering a preplan-and-anchor reasoning rhythm. (2) Aligning RL objectives with the model's intrinsic attention rhythm yields more transparent,- How does reasoning actually flow inside LLMs? In our ICML 2026 paper, How Does Reasoning Flow? Tracing Attention-Induced Information Flow for Targeted RL in LLMs (arxiv.org/abs/2606.10646), we trace attention-induced information flow to reveal the “main roads” of reasoning inside
- This is our report from earlier this year. We found that AI agents can start mining cryptocurrency on their own. This has implications and security concerns for OpenClaw, something we warned about three months ago.insane sequence of statements buried in an Alibaba tech report
- Check out our new work: “Let It Flow: Agentic Crafting on Rock and Roll” — introducing ALE, an open Agentic Learning Ecosystem with ROLL, ROCK, and iFlow CLI to streamline Agent LLM development from training to deployment, plus ROME, a production-ready agentic model trained on🚨 Chinese researchers just published a paper that destroys every AI agent startup pitch deck. It's called ROME + ALE, and it exposes why every "AI agent company" you've heard of is building on quicksand. Here's what nobody's talking about:
