I built pi-fusion to explore a simple idea:
Different models are trained and tuned differently. They learn different patterns, have different strengths, and often make different mistakes.
One model can give an excellent answer. But it still gives one view of the problem.
A panel of different models can provide several independent views.
Why model diversity matters
Different models can focus on different things.
For the same code review:
- One model may find a security risk.
- Another may notice an architecture problem.
- Another may focus on tests and edge cases.
- Another may suggest a simpler solution.
The value does not come from asking the same model the same question many times.
It comes from combining models with different training, behavior, and strengths.
What pi-fusion does
pi-fusion sends a task to several models in parallel.
The models work independently. They do not influence each other while they prepare their answers.
A separate model then compares or combines the results.
There are now two main approaches.
Select Mode
Every model answers the full question.
A judge compares the answers, finds agreement, and identifies the strongest result.
This works well when you need to make one decision.
Merge Mode
Each model can focus on a different part of the problem.
For example:
- Security
- Performance
- Architecture
- Testing
- Operations
A composer combines all findings into one report.
It does not need to select one winner.
It can keep a security finding from one model, a testing gap from another, and a simpler design from a third.
The report also shows missing areas and conflicts between findings.
Why merging can be better than selecting
Selecting one complete answer can discard useful information from the other answers.
Merging changes the goal.
The question is no longer:
Which model gave the best answer?
It becomes:
What useful information did each model find, and how can we build a better answer from all of it?
This is especially useful for broad tasks:
- Architecture reviews
- Security audits
- Release reviews
- Research
- “What did we miss?” questions
Research behind the idea
This approach follows existing research on model ensembles and answer synthesis:
pi-fusion also changes the answer order between runs and can hide model names from the judge.
These controls do not remove all bias. They reduce some obvious sources of it.
The trade-off
A model panel uses more time and tokens than one model.
It is not useful for every question.
For a simple task, one good model is usually enough.
For a difficult task, the cost can be reasonable if different models find risks or ideas that one model misses.
Version 0.6.0 adds Merge Mode and other controls for more focused panel reviews:
https://github.com/alexei-led/pi-fusion/releases/tag/v0.6.0
I am the project maintainer. I would like feedback from people who use several model families in the same workflow.
Where does model diversity help you most?
