Llm Architecture

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 Large Language Models are increasingly deployed in places where misunderstanding intent is not a harmless inconvenience, but a real risk. Mental‑health support, crisis hotlines, education, customer service, even compliance tooling—these systems are now expected to “understand” users well enough to respond safely. The uncomfortable reality: they don’t. The paper behind this article demonstrates something the AI safety community has been reluctant to confront head‑on: modern LLMs are remarkably good at sounding empathetic while being structurally incapable of grasping what users are actually trying to do. Worse, recent “reasoning‑enabled” models often amplify this failure instead of correcting it. fileciteturn0file0 ...