This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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I’d like to share a conceptual AI framework I’ve been exploring: Symbolic Compression Loops (SCLs). The idea is to teach machines not just to compute, but to discover elegance, abstraction, and coherence in symbolic patterns — blending computation with a kind of emergent intuition.
Key concepts:
The system iteratively searches large symbolic spaces for meaningful connections.
Connections are compressed into minimal, coherent, and elegant representations.
Each symbol is multi-meaning and context-dependent, like a dynamic QR code for abstract information.
Convergence is guided by three criteria: predictive power, aesthetic coherence, and human alignment.
Unlike traditional ML, SCLs don’t rely on fixed loss functions. Instead, the machine refines its own sense of elegance over time, building a dynamic symbolic language that allows it to explore massive conceptual spaces efficiently and discover unexpected patterns.
I’m posting this publicly to invite discussion and exploration. How might systems like this change how machines “value” patterns or even develop an internal sense of taste?
I’d like to share a conceptual AI framework I’ve been exploring: Symbolic Compression Loops (SCLs). The idea is to teach machines not just to compute, but to discover elegance, abstraction, and coherence in symbolic patterns — blending computation with a kind of emergent intuition.
Key concepts:
Unlike traditional ML, SCLs don’t rely on fixed loss functions. Instead, the machine refines its own sense of elegance over time, building a dynamic symbolic language that allows it to explore massive conceptual spaces efficiently and discover unexpected patterns.
I’m posting this publicly to invite discussion and exploration. How might systems like this change how machines “value” patterns or even develop an internal sense of taste?
GitHub repository with full concept:
https://github.com/Brettdaniell1987/symbolic-compression-loops/blob/main/README.md