Analog computing systems employing expectation-biased stochastic resonance (EBSR) can operate beyond the 10^80-state epistemic horizon—achievable with only N ≥150 coupled oscillators—transcending the measurable limits of the universe. By operating in continuous high-dimensional phase spaces with structured noise, these systems access computational regimes fundamentally inaccessible to discrete symbolic systems. We introduce EBSR as a computational primitive that transforms detection into selective processing through expectation-modulated energy barriers. Unlike classical stochastic resonance which merely enhances signal detection, EBSR performs analog Bayesian inference where the computation IS the pattern of selective amplification. We validate this framework through specific benchmarks in pattern recognition and manifold navigation, with direct applications to secure, unpredictable autonomous systems. The implications for artificial general intelligence and our understanding of biological computation are profound.