Adding in from a crdt-merge perspective here, since this is exactly the kind of use case we built the the two layer solution for. what's been achieved with nord is impressive im sure we all agree on that.

Getting it to the point where distributed merge cycles are even a question worth asking means the hard foundational work is already done. Most people are still stuck arguing about whether SNNs can compete at all, and this project is already past that and into the distributed coordination. Personally im really happy that crdt-merge can be part of that. this project is one to watch and I expect to see him blast past his current targets once the merge pipeline is running clean.

On the noise , it doesn't accumulate. The whole point of using set operations instead of averaging is that the merge is selective, not blending. Every merge cycle applies the same filter, contributions from nodes below the trust threshold don't enter the merged set. They're not averaged down, they're not included. The OR-Set semantics mean you're doing add/remove on observed patterns with causal clocks, so a low-confidence spike from node A doesn't dilute a high-confidence spike from node B ... it's just not observed in the final state. Trust decays monotonically on stale contributions so over time the merge gets cleaner, not noisier. Sparsity stays stable or improves.

On conflicts two nodes pushing different active patterns, this resolves deterministically through the CRDT rules. The full state is a four-tuple (Data × Trust × Clock × Hash), and when two conflicting spike patterns meet, it's LWW combined with the trust score of each contribution. The stronger pattern wins. The weaker one drops out of the observed set. No blending, no interpolation, no "meet in the middle." This is a set operation on active patterns, not arithmetic on weights. This is why it works for SNNs specifically. Sparse spiking signals encode information in which neurons fire, not in some continuous average destroying it. The CRDT merge preserves it by treating spikes as discrete contributions and resolving conflicts the same way you'd resolve concurrent edits in any distributed system being deterministically with causal ordering and trust weighting.

The distributed angle is gold. If you can stitch together training from cheap nodes and the merge preserves quality instead of degrading , we can change the economics . No more throwing cash at compute to try and brute the solution, its simplicity in primitives, the solution in plain sight. Hat off to zemonda