MASSIVE claim in this paper. AI Architectural breakthroughs can be scaled computationally, transforming research progress from a human-limited to a computation-scalable process. So it turns architecture discovery into a compute‑bound process, opening a path to self‑accelerating model evolution without waiting for human intuition. The paper shows that an all‑AI research loop can invent novel model architectures faster than humans, and the authors prove it by uncovering 106 record‑setting linear‑attention designs that outshine human baselines. Right now, most architecture search tools only fine‑tune blocks that people already proposed, so progress crawls at the pace of human trial‑and‑error. 🧩 Why we needed a fresh approach Human researchers tire quickly, and their search space is narrow. As model families multiply, deciding which tweak matters becomes guesswork, so whole research agendas stall while hardware idles. 🤖 Meet ASI‑ARCH, the self‑driving lab The team wired together three LLM‑based roles. A “Researcher” dreams up code, an “Engineer” trains and debugs it, and an “Analyst” mines the results for patterns, feeding insights back to the next round. A memory store keeps every motivation, code diff, and metric so the agents never repeat themselves. 📈 Across 1,773 experiments and 20,000 GPU hours, a straight line emerged between compute spent and new SOTA hits. Add hardware, and the system keeps finding winners without extra coffee or conferences.