Sigma Runtime defines the execution model for attractor-based cognition - integrating symbolic density, drift regulation, and recursive coherence into a unified cognitive runtime layer.

It’s like a Linux-style runtime for cognitive processes in LLMs: models act as hardware, and frameworks like LangChain become drivers on top of it.

→ Documentation: https://wiki.sigmastratum.org → GitHub: https://github.com/sigmastratum/documentation → DOI: https://doi.org/10.5281/zenodo.17703667

Would love technical feedback and collaboration from those working on LLM runtimes, recursive reasoning, or cognitive architectures.