Introducing FunCAD: Designing functional, working CAD mechanical mechanisms from a single line of text ⚙️ Everyone is hyped about GPT-6 Astra’s 3D demos, but building real mechanical assemblies from scratch tells a different story: ❌ Astra’s edge is just an interface — 3D fluency ≠ spatial & physical understanding. Fable beats Astra in physics, and blindfolding Astra barely matters. ❌ Not 3D reconstruction — zero reference images, just a text goal (e.g., "bring a back-row cup to the front") to invent CAD mechanisms that actually work. To test this, we built FunCADBench (46 mechanism families, from clamps and transmissions to mechanical counters, alarm clocks, and coin sorters) and FunCAD-Harness, which translates abstract requirements into candidate-specific physical tests and uses simulation diagnostics to guide revisions. What we found: 🙈 Blindfolding Astra barely matters: Agents spend 75–85% of their tool runtime in simulation and rarely inspect renders. Completely blinding Astra to build purely from code + physics logs still solves 20/46 tasks (vs. 22/46 with vision). 🏆 Fable beats Astra at 1/4 the cost: Because visual fluency ≠ physical reasoning, Fable solves 32/46 out of the box (vs. Astra's 22/46), leading on complex mechanisms. 💸 Cheaper models + retries win: Running Grok for a few extra attempts (with retained history) pushes it from 19/46 to 36/46—beating Astra at ~half the cost. 📉 Pass/fail isn't enough: With full diagnostic feedback—catching blocked steering, jammed clock hands, or tipping—Astra jumps from 8/46 to 22/46, but collapses to 0/46 under binary pass/fail. 🖨️ Sim-to-Real: We 3D-printed and assembled the AI-designed locks, counters, timers, and cup-fetching machines—they work in the real world! ✅ For more details, please check below👇 🌐 Demo & Page: thegenlab.ai/funcad/ 📄 Paper: arxiv.org/abs/2610.26861 💻 Code: github.com/thegenlab/fun-CAD… Work with Wenqian Zhang, @yangyi_huang_cn, Zigeng Xu, @Besteuler

We’re releasing a broad range of new mathematical results produced by an internal frontier model. We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results. github.com/openai/math

Can LLMs design real machines — from 🚗 cars to 🏹 catapults? Can they engineer through both 🧠 agentic workflows and 🌀 reinforcement learning (RL) — learning from physical simulation instead of text alone? We treat machine design as “machine code writing”, where LLMs assemble mechanisms from standard parts. To explore this, we built 🧩 BesiegeField — a real-time, physics-based sandbox where LLMs can build, test, and evolve machines through agentic planning or RL-based self-improvement. Our findings: 1️⃣ Even top LLMs fail to build working catapults — easy for humans but highly dynamic ⚙️ and nonlinear. 2️⃣ RL helps — working designs emerge through interaction. 3️⃣ Aligning reasoning 🧩 with construction 🔩 remains a key challenge. This marks the first step toward LLMs that learn to design through action — bridging reasoning, physics, and embodiment. 🛠️🤖 🌐 Project Website: besiegefield.github.io/ 💻 GitHub (RL & Agentic Workflow): github.com/Godheritage/Besie… 👥 Joint work w/ @Besteuler & Wenqian Zhang