This manuscript introduces the GEYLAN˙IV2-BIO Universal Stability Law, a fully closed mathematicalframework designed to explain the persistence, collapse, and deformation of biological information underenergetic and entropic constraints. The formulation integrates three fundamental constants emergingfrom large-scale transcriptome analysis: Biological RNAs maintain information only while remainingabove a universal stability threshold governed by phase geometry. Using GEYLAN˙IV2-BIO, a multiscaleframework unifying sequence composition, thermodynamic depth, and structural coherence, we analyze207,249 human transcripts together with viral genomes. We identify two universal collapse constants,0.30 and 0.83, and demonstrate that RNA instability emerges from geometric constraints rather thanorganism-specific biology. A central phenomenon, described here as “cellular blindness ’ (cellularblindness, informational threshold blindness, phase-instability blindness), occurs when cells fail to detectsub-threshold instability, allowing collapsed RNAs to propagate functional error. Viral RNAs occupythe extreme low-stability edge of the manifold, confirming that collapse is a fundamental informationaltransition. These results establish GEYLAN˙IV2-BIO as a universal law of RNA stability.• 0.30 — the universal collapse threshold, below which information-bearing RNA enters a destabilizedor degraded phase;• 0.83 — the upper-phase stability bound, representing the empirical limit where coherent, high-fidelity RNA structures retain functional integrity under perturbation;• 50.5 — the information–energy density ratio S(g) = |MF E|H, found to be invariant across 207,249human transcripts, viral RNA genomes, and cross-species samples.Together, these constants define a universal stability surface governing RNA behaviour across healthy,cancerous, and viral systems. GEYLAN˙IV2-BIO provides a unified phase-geometric formulation linkingfree energy, entropy, and temporal perturbation operators, enabling predictive modelling of informationcollapse in biological systems