[milkkarten]

Using smaller, cheaper agents is one of the goals of the work. There is a Pareto frontier though: by using smaller, faster, cheaper agents, the number of steps required to converge increases. We touch upon this briefly in the paper

[MutedEstate45]

Thanks. That Pareto trade-off is exactly what I'm trying to quantify not just qualify. For example, if I've got a $50 budget, what's the sweet spot?

Scenario A: 100 agents × GPT-4o-mini × 500 steps Scenario B: 500 agents × local Llama 3-8B × 1,000+ steps

A quick table like "X agents × Y model × Z steps → tokens, $, convergence score" in the README would let new users budget experiments without having to read the whole paper plus run expensive experiments just to discover basic resource planning.

[milkkarten]

We ran each method in under 24 hours on a singular H100. I understand your point and think we will include this in future iterations of our work since this is very interesting from the user perspective. Though, in the paper we focus more on algorithmic concerns.

[MutedEstate45]

I'll look out for future iterations. Thanks and good luck with the paper.