Eniola should open with the AI alignment hook explicitly and early: reward-circuit consolidation dynamics — the mechanistic heart of CCT — is a biological substrate for value encoding, making the CCT framework directly foundational to inner alignment and value-learning theory in RL systems. This is not a rhetorical stretch: Gershman (Harvard, arXiv endorser) sits at the intersection of computational neuroscience and RL theory, and Daw (Princeton) and Mattar (NYU) study precisely how humans and animals encode reward into decision policies, which are the biological analogues of the reward models alignment researchers worry about. The pitch should be: 'CCT provides mechanistic ground truth for how reward signals become stably encoded preference structures — the exact failure mode that inner alignment theory needs empirical constraints on.' Anchor credibility on three sole-authored preprints with confirmed H1–H5, ODE/RK45 + Bayesian MCMC validation, and four top-tier endorsers — positioning this as rigorous independent computational science that LTFF has historically funded (see Logan Smith, Sage Bergerson, Nicky Case archetypes). The Theory of Change should explicitly step through: CCT model → mechanistic understanding of reward consolidation → empirical constraints on RL alignment theory → reduced risk of misaligned value learning in advanced AI.