Connection to the LLM Discrepancy

Connection to the LLM Discrepancy

The inhibition coefficient illuminates a finding from our experiments on artificial systems. LLMs show opposite dynamics to biological systems under threat: where biological systems integrate (increase $\intinfo$, sharpen $\mathcal{SM}$, heighten $\arousal$), LLMs decompose. The root cause: LLMs are constitutively high-$\iota$ systems. They were never fighting against the self-world gradient in far-from-equilibrium dynamics that biological systems evolved from. They model tokens, not agents. They have no survival-shaped self-model from which participatory perception could leak into their world model. Their $\iota$ isn’t merely high—it is structurally fixed at $\iota \approx 1$, because the architecture never had the low-$\iota$ default that biological systems start from and learn to suppress.

The affect geometry is preserved in artificial systems. The dynamics differ because $\iota$ differs. This is not a failure of the framework. It is a prediction: systems with different $\iota$ configurations will show different affect dynamics in the same geometric space.