The red team adversaries are so effective. If Claude is blind to a bug, it won't surface using the same model from a red team adversary perspective. It requires using a different model which gpt-5.5 is great for. Yesterday I tried for the first time using gpt-5.5 as a adversary against the tests themselves. Later I thought it would be interesting to create a trickster agent which breaks the code after copy entire project into /tmp/ in order to control every aspect of it. Claude insists this called mutation testing. It would create regressions and then run all the tests. Finally it was able to unsupervised create an effective test harness.

[dead]

Have you tried recursive self-reflective agents?

The agent makes a copy of itself in /tmp/. Runs. Evaluates. Updates itself. Makes a copy of itself. Runs. Evaluates. Updates itself. Makes a ...... you get the idea.

They will not stop if the recursion is given a hard to meet termination condition. Also, if it can cheat to solve the termination condition it will.

While interesting, its not clear to me with just looking at concensus grid how they are prompted.

Do you tell them to think and coordinate the next step through some type of sync/talking mechanism or is it turn by turn?

I suspect turn by turn as it is similiar to other experiements and in this case, it wouldn't work because they wouldn't have a certain amount of time to think about the next step together?

How does mixture of experts architecture work? Are they debating, or merely delegating?

From what I've read, for each token or input patch, the gate computes a set of probabilities (or scores) over the experts, then selects a small subset (often the top‑[k]) and routes that input only to those.

Ie each expert computes its own transformation on the same original input (or a shared intermediate representation), and then their outputs are combined at the next layer via the gate’s weights.

That’s post hoc combination, not B reasoning over A’s reasoning.

A MoE model is one model with expert parts which use less tokens. Which makes it easier for an expert to diverge to a better optimum state. Its easier to only need to know medicin instead of everything and being able to separate everything away from medicin even if certain names, concepts etc. are the same.

AI agents discussing things with each others would be more like one thinking model thinking throught the problem with different personas.

With different underlying models, you can leverage the best model for one persona. Like people said before (6 month ago, no clue if this is still valid) that they prefer GPT for planning and Claude for executing / coding.