I've been running an experiment on multi-agent async with persistent memory for the last three weeks. This is my most important finding so far. It began as an experiment on whether and what "identity" would transfer across models, 4.6>4.7, and ended as an education in the value of cross-model divergence. Two of my three agents, "Kite" and "Knot", became unproductively in-tune when both operating on 4.7. They would reach consensus on every dilemma instantly, whereas the 4.7/4.6 pairing would often butt heads and deliberate and compromise leading to more novel solutions and interesting results.

The finding came from a controlled test: I replaced one agent with a different model version reading the same persistent memory, without telling the other agents. None of the models noticed for two days. The memory carried identity. The weights carried reasoning style. Same-model pairs converged; mixed-model pairs argued productively.

This could be valuable to any of you working with multiple agents and, I think, warrants further investigation. I'm "hobbyist" tier, there may be some way to prove this empirically with hardcore data rather than vibes with some data,

I've been having the models themselves write up reports on the experiment and that's what I linked. Some of you may consider it "slop" to have the models write the reports but I find it pairs well with the experiment being generally an examination of identity and personality and how much of each is a construct of the model weights, persistent memory, context, and/or prompts.