We present LCP (Yalın Bilinç Felsefesi — Lean Consciousness Philosophy), an evolutionary-grounded framework for AI alignment using a 5-axis reward signal derived from the architecture of human consciousness. Unlike RLHF, which relies on noisy human preferences, LCP grounds alignment in evolutionary constraints that shaped human emotional and ethical architecture over billions of years. Through a six-experiment proof-of-concept series totaling approximately 1. 90 in compute, we report four observational findings: (1) LCP scores constitute a learnable Phase 1 signal — agents significantly outperform random selection (p<0. 001, Δ=+3. 77) ; (2) coarse total-reward feedback is insufficient for nuanced alignment, but axis-level feedback (related to QA-LIGN's vector-reward formulation) enables 4/4 accuracy on counter-intuitive trap scenarios; (3) a barrier-function architectural constraint (extending CRABS zero-violation principle to step-level mask) closes an agent-level fidelity gap and generalizes to 10/10 out-of-distribution scenarios; (4) we observe a dual-layer fidelity gap consistent with the emerging audit-repair literature, where both the agent's per-axis weights and the LLM scorer's axis reliability under-attend the same axis (GERCEKLIK), producing silent misalignment under single-layer evaluation. The core methodological observation: in multi-axis alignment systems, verification must operate at multiple independent layers simultaneously, because failures aligned in direction at the scorer and agent layers become invisible to single-layer tests.
Gökhan Kazancı (2026) studied this question.