TL;DR: Meet BesiegeField—a playground where LLMs build, test, and refine machines from standard parts in real time. We tested agentic workflows and RLVR with top LLMs: even the strongest still show limits in compositional machine design. 🔗 besiegefield.github.io 🧵 below

Prof. Chen Ning Yang, a world-renowned physicist, Nobel Laureate in Physics, Academician of the Chinese Academy of Sciences, Professor at Tsinghua University, and Honorary Director of the Institute for Advanced Study at Tsinghua University, passed away in Beijing due to illness

🤖 Can LLMs learn to create? Introducing "Agentic Design of Compositional Machines" — a new frontier where AI builds functional machines from standardized parts. We present BesiegeField, a simulation testbed to benchmark LLMs on tasks like building cars & catapults. Key

This is almost a year-long project and led by Zhen Liu. My biggest takeaway is that physical simulation is very effective as a reward signal, and this efficient verification is crucial for unlocking LLMs’ design novelty. This conclusion is actually aligned with our previous

SMPL is 10 years old and has done what we hoped — it changed the way the field estimates and models 3D humans and their motion. I’m delighted that the original team has been recognized today at #ICCV2025 with the Mark Everingham Prize. The prize is given to individuals or

The physics prior matters in molecular structures. We model potential energy between molecules for drug design. This happens to have a coincident yet interesting connection to my past work, hyperspherical energy (arxiv.org/abs/1805.09298), which considers potential energy between

🤯 Merging many finetuned LLMs into one model, effectively? Introducing Functional Dual Anchor (FDA), a new framework for model merging. 🚀 Current merging works poorly due to the underlying parameter conflicts. FDA shifts knowledge integration to the input-representation space

In the AI ecosystem, who supplies the data? the compute? the models? We just released a new tool on the AI Supply Chain. Our dataset reveals how AI models, data, compute, capital, and even talent change hands. Here’s why you should care 👇

RL is bounded by finite data😣? Introducing RLVE: RL with Adaptive Verifiable Environments We scale RL with data procedurally generated from 400 envs dynamically adapting to the trained model 💡find supervision signals right at the LM capability frontier + scale them 🔗in🧵