Happy to share this and curious how others are thinking about runtime-level architectures for LLM cognition.
We’ve been working on an open runtime architecture that stabilizes long-horizon reasoning in large language models.
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.
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Happy to share this and curious how others are thinking about runtime-level architectures for LLM cognition.