Lianhui Qin
@Lianhuiq • 7,438 subscribers
AI/ML faculty at UCSD CSE. I’m interested in building self-evolving AI agents in complex (embodied & scientific) environments, and latent diffusion LLMs.
Videos
Scaling embodied AI starts with automating the environments. Introducing SimWorld Studio: a self-evolving factory for endless interactive 3D environments where agents act, fail, and learn. With coding-agent + embodied-agent co-evolution, navigation success improves from 50% → 90%. 1/
Lianhui Qin59,079 просмотров • 2 месяцев назад
🚨🚨Can agents earn money, run a business, or even self-organize a society in the physical social world? 🤖🤖 Can agents learn continually to survive and thrive in embodied environments, like how human babies grow? 👶 Super excited to introduce SimWorld, an open-ended simulator of LLM agents in infinite, realistic embodied worlds. SimWorld features 3 key designs: 1⃣Open-ended realistic world simulation - built on Unreal Engine 5, with accurate physical social dynamics - 100+ built-in environments (city, island, wilderness ...) - language-controllable procedural generation - text-to-3D asset generation 2⃣Native interface for LLM/VLM agents - Gym-like agent-environment interaction APIs - plug in any LLMs/VLMs (GPTs, Gemini, Qwen ...) - rich multi-modal perception - open-vocabulary natural-language action outputs 3⃣Diverse physical and social reasoning scenarios - long-horizon embodied reasoning - multi-agent collaboration / competition - easily customizable for any reasoning tasks SimWorld is fully open-sourced, with a hope to become a foundational infrastructure for real-world agent research across disciplines: robotics, economy, public health, education, etc. Project website + more details in the thread👇 ...1/
Lianhui Qin65,079 просмотров • 7 месяцев назад
💡Divergence thinking💡 is a hallmark of human creativity and problem-solving 🤖Can LLMs also do divergent reasoning to generate diverse solutions🤔? Introducing Flow-of-Reasoning (FoR) 🌊, a data-efficient way of training LLM policy to generate diverse, high-quality reasoning trajectories Unlike existing RL (like PPO) and planning (like MCTS) to find the max-reward trajectory (akin to convergent thinking), FoR connects LLM reasoning with the #GFlowNet formulation and enables LLMs to find trajectories proportional to reward distribution. 🎬The demo video illustrates how FoR learns and infers multiple solutions to a ♠️Game24 puzzle. 🎯Inferring for diverse solutions could be useful for robustness, data augmentation, and enhanced model generalization. Project page: Paper: Github:
Lianhui Qin50,447 просмотров • 2 лет назад
Been quiet here for a bit, but had to share what I just tried with Mirage 2!! 📣📣📣 I dropped in a random ❄️Game of Thrones image and suddenly I could walk around inside it with just my keyboard, then typed 🧟♂️🧟♀️ “Night King + White Walkers” and they appeared 😱 The controls (especially the camera) were tricky at first, but soon felt natural and honestly addictive. Feels like making a video game world in real time. 😁🎮🏄♀️So much fun! My control skill still kinda sucks lol😂
Lianhui Qin20,045 просмотров • 11 месяцев назад
🤖Coding agents like Claude Code are already game changers for digital tasks in 2026. But what if they could write code to build physical worlds? 🏙️ Imagine going from a single line of prompt → a controllable, interactive simulated world. Such environments could open new frontiers for game creation, RL training, large-scale world simulation, and studying complex social reasoning. Our SimWorld agent coding team is working toward releasing a platform that lets anyone build their own virtual worlds. Stay tuned.
Lianhui Qin10,647 просмотров • 4 месяцев назад
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