Opening Have been working on a small experimental system over the past couple months. Not a traditional ML setup. More of an exploration into how systems evolve through state space rather than predict outputs directly. What it does Current focus has been on: evolving trajectories from a single state -testing multiple paths from the same starting point -branching and recombining paths over time -observing how stability emerges under constraints What makes it different Intentionally simple: -no training loop -no black-box layers -everything is parameter-driven and visible -transparent Current experiments Lately have been experimenting with: -multiple trajectories from a single point (fan-out behavior) -branching trees (similar to neuron-like expansion) -divergence and recombination of paths -Trying to understand whether the system collapses to a single path or maintains multiple viable ones. Repo framing Documentation for every step: -daily logs (including breaks + insights) -conceptual notes -experiment tracking -governance / structure (still evolving) So it’s less of a finished project and more of an open process. Link and soft invite Repo is here if anyone wants to take a look: https://github.com/ArchitecturalEngines No claims. Exploration in a different direction. Sharing as it evolves. Curious what people think, especially around: -trajectory-based systems -dynamical vs predictive approaches or anything this reminds you of. Still early. Figured this would be a good place to invite people to the motion. submitted by /u/True-Beach1906
Originally posted by u/True-Beach1906 on r/ArtificialInteligence
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