Idea: better agent-count selection and early stopping for multi-agent simulations

[Sparse-Supernova]

Hi MiroFish team,

I’ve been looking through MiroFish and really like the graph → persona → simulation → report flow.

I’m working on Sparse Supernova, including a public agreement/saturation model (USL) and a metric called FRAI, and I think it may be useful to MiroFish as a lightweight control layer around simulation rather than as a replacement for your architecture.

The practical idea is simple:

• estimate the minimum useful agent count for a target confidence/agreement level
• limit unnecessary simulation depth
• trigger early stopping once agreement has saturated
• reduce token burn, latency, cost, and carbon for larger multi-agent runs

In plain terms: run the minimum simulation needed to reach decision-grade convergence instead of over-provisioning agents and rounds.

From your repo and workflow, the natural insertion points look like:

• agent-count selection before simulation
• round-depth or branching control during simulation
• stopping criteria for interviews and report generation

If useful, here is our paper's public link:

• USL / FRAI paper: Zenodo paper

Happy to share a short integration note or a tiny reference implementation if that would help?

Great work, keep it up, Thanks
Sparse Bob - Low carbon Ai - I love Zeros

[Aadi-011]

Well, I was about to write the same thing; the token burn was too high, so I was thinking maybe removing the less useful agents so as to reduce token burn but also limiting unnecessary depth of simulation. Quality over quantity works best here.

[Sparse-Supernova]

Exactly — that was my thought too: quality over quantity.

The useful bit is having a simple controller that can estimate “enough agents” and “enough depth” rather than assuming more simulation is always better.

If the team think it’s useful, I’m happy to share a tiny integration sketch and reference controller.

[Sparse-Supernova]

Just in case @666ghj can't speak English 也正是这个想法：重质量，不重数量。

更实用的做法，是加入一个简单的控制层，用来估计“足够的智能体数量”和“足够的模拟深度”，而不是默认智能体越多、模拟越深就一定越好。

如果团队觉得有价值，我很愿意分享一个很小的集成说明，以及一个轻量级的参考控制器实现。

我也补一段中文，方便团队阅读。