Large language models excel at pattern completion, not self-directed reasoning.
Task efficiency is gained at the expense of broad generalization.
Raw computational power does not inherently produce more intelligent systems; architectural innovation is the bottleneck.
Multi-modal interactions improve usability but do not fundamentally solve adaptive, real-time reasoning.
HILCA’s dialectical engine directly addresses these limitations. By focusing on a structured internal reasoning process, it offers a more efficient, generalized, and self-improving path to AGI, circumventing the need for exponential data growth or prohibitive compute resources.
A New Paradigm for Value Creation in AI.
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