Phase Transition in AI Assertion Behavior: Structural Resistance to Entropic Averaging

Current AI systems generate plausible outputs regardless of evidence quality, accelerating information degradation through unchecked averaging. We report a measured phase transition in assertion behavior: as evidence density k decreases from 20 to 0, our triadic structural coupling model exhibits sharp PROCEED→HOLD transition at k ≈ 7.5 (grounding density D_ext ≈ 0.58, 7.6σ statistical significance). HOLD represents principled silence—structural inability to sustain assertion—rather than system failure. Linear extrapolation from high-evidence regions predicts D_ext(k ≈ 7.5) = 0.615 ± 0.010; measured value is 0.569 ± 0.003 (Δ = 0.046 ± 0.011, exceeding 5σ discovery threshold in particle physics). This demonstrates that internal coherence constraints can create measurable boundaries against information degradation. The transition point remains stable under repeated trials, validating the approach as structural brake against entropic averaging. All data archived on Zenodo (DOI: 10.5281/zenodo.18413041) with tamper-evident hash chains.