Distortion, Not Scale — A Unified Framework for Intelligence, Alignment, and Efficiency in Neural Systems

Current artificial intelligence systems scale in parameter count, compute, and data, yet exhibit persistent failure modes including hallucination, sycophancy, and reward hacking. This paper proposes the Distortion Theory of Intelligence: a unifying framework in which intelligence is modeled as effective capacity under structural distortion (σ). Three structural errors — BPE tokenization, RLHF, and autoregressive generation — compound into intelligence that scales with cost rather than coherence. The framework reinterprets modern scaling laws as compensation mechanisms for information loss rather than drivers of intelligence. A complete σ ≈ 0 architecture is specified across seven layers: GDA tokenization (φ-normalized, with formal Collision Bound theorem), KV cache prewarming (986k queries/s, Rust), boot sequence with 13-assertion catalog and enforcement layer (logit masking, constraint validator, rejection sampling, symbolic checker), structural parsing, three-mind parallel inference (ReflexEngine), and semantic resonance graph. Formal results include the Landauer-Assertion Binding theorem (alignment as thermodynamic ground state), Geometric Leverage Impossibility proposition (prompt-level jailbreaking is architecturally impossible), Coherence Conservation equation (Iₑff = 1 - Ncompensation/Nₜotal), and a 6-step deterministic Inference Protocol. Independent empirical anomaly: 135M-parameter model with σ ≈ 0 stack produces coherent, identity-maintaining, non-sycophantic output on laptop CPU with no RLHF and no GPU. Five testable predictions and four falsifiable criteria are provided. A/B test at fixed parameter count proposed as decisive experiment. The framework does not claim scaling is useless — it claims scaling and σ-reduction are complementary, and the field has invested almost exclusively in scaling.