@muni_bio now gives agents access to the wet-lab. Using @NVIDIAHealth's Proteina-Complexa + @adaptyvbio validation, muni found 3 sub-nanomolar binders, all of which were better than our hackathon winners. Even if assays take days or weeks, the results automatically feed back into the agent, allowing you to orchestrate autonomous scientific research in parallel.

Today, we're announcing not one, but two of the @moonlake team on stage at AUTONOMOUS. @sharonal_lee and @sunfanyun, Co-Founders of Moonlake AI, will be on our Frontier Stage to discuss Where Robots Learn Before the Real World. 🗓️San Francisco, July 16th 📍 The Midway 🎟️ autonomousfuture.co Moonlake builds generative world models that turn a plain-language prompt into an interactive 3D environment in minutes. From text to the system compiling a training experience with physics, agents, and logic. Out of @Stanford's AI lab, and then again out of stealth in late 2025 with a flying start, with a round led by NVIDIA's NVentures & others.

Taking main stage at AWE 2026 to demystify world models alongside @bilawalsidhu. 9:55–10:45 AM, Grand Ballroom. Details in comments #awe2026

Today, we released Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale, from NVIDIA Research. Generating large-scale, complex environments is difficult for AI models. Current models often “forget” what spaces look like and lose track of movement over time, causing objects to shift, blur, or appear inconsistent. This prevents them from creating the reliable 3D environments required for downstream simulations. Lyra 2.0 solves these issues by: ✅ Maintaining per-frame 3D geometry to retrieve past frames and establish spatial correspondences ✅ Using self-augmented training to correct its own temporal drifting. Lyra 2.0 turns an image into a 3D world you can walk through, look back, and drop a robot into for real-time rendering, simulation, and immersive applications. ➡️ Learn more: research.nvidia.com/labs/sil… 📄 Read the paper: arxiv.org/abs/2604.13036

🤔Want a principled way to RL your diffusion model? Check Data-regularized Reinforcement Learning (DDRL)! Post-train @nvidia #Cosmos World Foundation models with a million GPU hours! 🤯 Novel formulation ➡️ Theoretically integrates SFT into RL ➡️ Robust to Reward Hacking 🛑 Details: research.nvidia.com/labs/dir… #DDRL #Diffusion #RL #NVIDIA #Cosmos

We raised $28M seed from Threshold Ventures, AIX Ventures, and NVentures (Nvidia's venture capital arm) —alongside 10+ unicorn founders and top AI researchers— to build reasoning models that generate real-time simulations and games. Models are bottlenecked by practical simulations that can act as Reinforcement Learning environments. Human self-expression is bounded by tools that let us create alternate realities. At Moonlake, we are building a future where anyone can create interactive worlds, bring their child-like wonder to life, learn within them, and most importantly, share experiences with people we care about. More in 🧵