The Anti-Congruent Conjecture: Efficiency–Instability Inversion in Dissipative Symbolic Systems

This paper formalizes the Anti-Congruent Conjecture—an invariant modeling efficiency-instability inversion in dissipative symbolic systems. We derive the law \tilde{M} = \frac{E}{\psi}, with variance scaling \(\Var[\tilde{M}] \propto \frac{1}{\psi^4}\), using rate-distortion theory (Blahut-Arimoto), and validate it in cognitive EEG systems. A Poisson-like symbolic channel model and PPV extensions are introduced. Empirical validation on EEG-ImageNet and THINGS datasets shows consistent performance (r=0.85 and r=0.81, respectively), with diagnostics, power analysis, and clinical benchmarks (d=1.2). We provide exploratory applications in neurocognitive biomarker discovery and hallucination stability in AI.