What if dark matter is not a missing particle, but the gravitational memory of the Universe's past quantum collapses — and that memory helps shape what collapses next? In a companion paper, we proposed that dark matter emerges from the thermodynamic cost of irreversible decoherence, yielding ΩDM = 0. 29 ± 0. 09 with zero fitted parameters. But that first framework treated the archive as passive: a record of past quantum-to-classical transitions with no influence on the future. This paper takes one step further. We propose that the accumulated archive is causally active: a gravitational rail that preferentially channels future decoherence events into already dense regions. In this picture, dark matter is not only the record of what happened — it also narrows the corridor within which what happens next can unfold. The result is a minimal extension of the original model that preserves the background dark matter abundance while introducing a striking new prediction at the perturbative level: environment-dependent structure growth. A₀ ≈ 30–50 is not a free fit. It is independently constrained by perturbative consistency and by the companion equation-of-state framework. Different observables converge on the same phenomenological scale. The framework makes three falsifiable predictions for Stage-IV surveys: Environment-split fσ8 (z): overdense archival regions grow faster, void-like regions more slowly, with a potentially detectable split at high redshift in DESI DR2/DR3. Formation-history weighted clustering: a marked-correlation signal sensitive to integrated archival history, peaking at 1–10 Mpc. Environment-split weak lensing: a separation in the convergence power spectrum testable with Euclid Year 1. In this framework, dark matter is not merely an invisible substance. It is the gravitational memory of cosmic history — and an active participant in structure formation. Keywords: Dark Matter; Information-Theoretic Cosmology; Wave Function Collapse; Quantum Decoherence; Landauer Principle; Structure Growth; Assembly Bias; DESI; Euclid.
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everton behenck
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everton behenck (Tue,) studied this question.
www.synapsesocial.com/papers/69bb938e496e729e62981817 — DOI: https://doi.org/10.5281/zenodo.19075193