RISE:Meltdown-Limited Recursion as a Mechanism for Dark Energy: Information-Theoretic Constraints from DESI Year 1 Observations

The accelerated expansion of the universe, attributed to dark energy, remains a significant puzzle. The standard ΛCDM model, while successful, lacks a fundamental explanation for the cosmological constant, and tensions with some observations have emerged. Project RISE - Recursive Intelligence Systems Emergence - presents a novel cosmological model where dark energy arises from the increasing complexity of cosmic structure formation. We posit that as structure forms hierarchically, the "information content" or "computational cost" of representing that structure grows. When this complexity, quantified by a function, bits(z), exceeds a critical threshold, Cmax, "meltdown" events occur, releasing energy and contributing to cosmic acceleration. This model, which we term "Meltdown-Limited Recursion" modifies the Friedmann equation by introducing a dark energy density that depends on bits(z), implemented here as the fraction of matter in dark matter halos above a minimum mass, Mmin. We also incorporate a time-varying dark energy equation of state, w(a), motivated by Renormalization Group arguments. We constrain the model parameters using Type Ia supernovae (Pantheon+), Baryon Acoustic Oscillations (BAO) and Redshift-Space Distortions (RSD) from the DESI Year 1 data release and BOSS, CMB priors from Planck 2018, and a weak lensing prior from DES Y3. Our Bayesian analysis finds strong evidence for the meltdown model over ΛCDM (Δlog(Z) ≈ +6.1). The best-fit model parameters are physically plausible, with Cmax consistent with estimates from the Bekenstein bound applied to cosmic filaments. The model predicts a lower S8 = 0.765 ± 0.015, potentially alleviating the S8 tension. This work suggests a new paradigm for understanding dark energy, linking it to fundamental limits on information processing in the evolving cosmic web, with potential implications for and links to computational physics and other complex systems.