Synthreo

Can weaker AI get smarter by thinking more? We put it to the test.

We took inspiration from work on chained reasoning and tried something different:

Tie multiple GPT-3.5 models together to see if stepwise reasoning could improve performance — without upgrading the model.

Here’s the logic puzzle we used:

“Jack is looking at Anne. Anne is looking at George. Jack is married, George is not, and we don’t know Anne’s status.

Is a married person looking at an unmarried person?”

It’s deceptively simple… and GPT-3.5 completely fumbles the logic.

Round 1:

GPT-3.5 says “Yes” — but for the wrong reasons.

Round 2:

We pass that output to another GPT-3.5, prompting it to “think twice.”

Still wrong.

Round 3:

A third GPT-3.5 takes the two flawed responses and finally pieces it together.

The final answer? Correct — with sound reasoning.

This experiment shows that layering LLMs, even weaker ones, can simulate deeper reasoning.

Like a committee reviewing each other’s work — clarity emerges through iteration.

We’re not building smarter AIs here — just more thoughtful ones.

If you're building AI agents or workflows, you don’t always need GPT-4.

Sometimes, you just need more steps.

What else could we do with chained reasoning like this?