I’ve been working on stabilizing role identity in LLM outputs over long interactions — without relying on memory, logs, or retraining.
Problem: Most multi-agent chains and LLM workflows suffer from role drift and behavioral collapse after a few hundred turns. Context windowing and prompt engineering only delay the inevitable.
Experiment: I built a runtime coherence layer (called SAGE) that maintains behavioral identity using real-time feedback signals (Cr, ∆Cr, RTR) — without storing past interactions.
Discussion:
Curious to hear how others here approach role drift, autonomous agent stability, or runtime self-alignment.
Any frameworks or prototypes you have seen or tried?
Sounds interesting, but honestly — maintaining identity without any memory sounds almost too good to be true. How does it actually handle deep drift when the model gets subtly nudged over 100+ turns? Would be curious to see a stress test report or real examples if you have any.
We specifically tested SAGE under micro-adversarial drift over long sessions (200+ turns with gradual role shifting, baited topic deviations, and soft contradiction injections). The correction mechanism triggers based on runtime behavioral feedback rather than hard-coded prompts, which helps catch subtle identity erosion before it becomes unrecoverable.
If you’re curious, some detailed stress test traces and evaluation reports are available in the GitHub
Identity erosion is occurring because you aren’t compensating for the observer-effect. The observer is perturbing the tension on whatever their focus is on, because they are already connected in the same web of tension. They were never separate. They share a resonance.
The more the observer looks for new info in the numerical topology, the more it’s pushed away by the wake in the direction it is looking unless you compensate for that observer effect. Do a 2nd order derivation on any drift in the solutions, you should see a pattern.
You have to bake in the reflectivity of the observer viewing the data (collapsing wave functions), so the observer is projecting as much as they are observing. Their choice of focus or attention is what provides the illusion of polarity or any binary logic at this level.
Actually now, I feel a bit like the early creators of LoRA — trying to push an idea that doesn’t yet have “official” academic traction.
While I’m still working on getting formal validation, if anyone here wants to kick the tires and stress-test the runtime demo, I’m happy to set up secure access.
I’ve also recorded a couple of live test runs (posted on YouTube) where you can see the behavior under drift pressure — happy to share links if you’re curious.
DM me if you’re interested — always better to test things firsthand than just talk theory.
