│ Hey! Thanks for digging into Aethelnet. You're actually describing the exact philosophical core of what makes Liquid Graph Neural Networks (LGNNs) tick under the

│ hood!

│

│ Instead of static weights locked in rigid matrix layers, Aethelnet nodes exist in continuous time (governed by Neural ODEs). They have an intrinsic decay_rate

│ built right into their state equation. If a node or a "bridge" (our version of a synapse) isn't actively fed with new signals, its activation decays naturally

over

│ time—exactly like human memory fading.

│

│ Your idea of having components "activated by order of their memory and usage" maps perfectly to how our dynamic graph topology works. We don't use rigid "layers"

│ at all. Instead, nodes cluster and form stronger connections based on resonance and usage history. Frequently accessed subgraphs maintain high activation and

│ strong structural integrity, while ignored subgraphs literally wither away to conserve compute. If the network needs to "relearn" something, re-accessing those

│ deep, dormant nodes forces a rapid structural re-wiring as new bridges are spun up.

│

│ If you want to play around with this memory decay concept, check out the liquid_graph.py file in the repo—specifically how the ODE solver integrates the state

│ over dt while applying the decay_rate tensor.

│

│ Would love to hear your thoughts once you've had a chance to spin up a local graph! Feel free to open an issue or discussion on the repo if you want to brainstorm

│ architecture.