LLMs - Strengths and Weaknesses
We keep treating LLMs like reasoning machines — and then act surprised when they confidently get math, logic, or proofs wrong. CTO Robert Ross digs into the limits that rarely make it into the headlines.
We keep treating LLMs like reasoning machines — and then act surprised when they confidently get math, logic, or proofs wrong.
They’re not logic engines. They’re probability engines. And that distinction matters more than the hype suggests.
In this post, Robert Ross digs into a few limits that rarely make it into the headlines: why “universal approximation” isn’t the same as reasoning, why pattern-matching language isn’t understanding it, and why these systems tend to fall apart the moment you push them off familiar ground.
The real question isn’t whether LLMs are impressive — they are — but how much trust they’ve actually earned, and exactly where that trust breaks.