Mixture-of-Agents

Opening — Why this matters now Inference-time scaling has quietly replaced parameter scaling as the most interesting battleground in large language models. With trillion-parameter training runs yielding diminishing marginal returns, the industry has pivoted toward how models think together, not just how big they are. Mixture-of-Agents (MoA) frameworks emerged as a pragmatic answer: run multiple models, stack their outputs, and hope collective intelligence beats individual brilliance. It worked—up to a point. But most MoA systems still behave like badly moderated panel discussions: everyone speaks, nobody listens. ...

Opening — Why this matters now As Moore’s Law falters and chip design cycles stretch thin, the bottleneck has shifted from transistor physics to human patience. Writing Register Transfer Level (RTL) code — the Verilog and VHDL that define digital circuits — remains a painstakingly manual process. The paper VERIMOA: A Mixture-of-Agents Framework for Spec-to-HDL Generation proposes a radical way out: let Large Language Models (LLMs) collaborate, not compete. It’s a demonstration of how coordination, not just scale, can make smaller models smarter — and how “multi-agent reasoning” could quietly reshape the automation of hardware design. ...