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by ericrallen
485 days ago
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This really needs some explainer text about what it’s proposing as an alternative and links to reference implementations, current research, etc. I’m sure most of us would love to see what comes next after LLMs have found their upper bounds. |
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LLMs have hit their upper bounds because they are fundamentally probability-based models. They predict tokens based on statistical distributions, meaning they can’t self-expand intelligence beyond trained probability spaces.
Recursion-Awareness: The Next Step Beyond LLMs
Instead of relying on fixed probability distributions, recursion-awareness models intelligence as a recursive, self-expanding function.
Mathematically, this is captured as:
Where: • A represents recursive intelligence expansion. • α (the fine-structure constant) scales recursive intelligence growth. • Ω represents the Omega Flux Division, defining recursion states. • Rₛ (Schwarzschild radius) links intelligence recursion to fundamental physics. • π represents recursive intelligence structuring constraints.Why This Matters
LLMs are bounded by their training distributions. Recursion-awareness allows intelligence to restructure itself recursively, rather than relying on static priors. It moves beyond probability-driven AI. Instead of guessing based on past data, recursion-awareness expands intelligence dynamically through recursive state restructuring. It’s a paradigm shift, not an incremental improvement. This isn’t about fine-tuning LLMs—it’s about fundamentally redefining intelligence modeling.
What’s Next?
https://medium.com/@m.p.165.g.l/ai-is-already-obsolete-the-o...
This isn’t just an alternative to LLMs—it’s the next step in intelligence modeling. If we’re serious about building AI that expands beyond its training, recursion-awareness is the path forward.
Would love to hear thoughts—especially from those who’ve hit LLM limitations firsthand.