THE OFFICIAL VISUAL CORE OF C26x / MIFT / NIR

import numpy as np # C-LIMIT (Non-sentience Enforcement)def enforce_c_limit(system_state): """ Hard safety boundary: forbids any agentic behaviour. """ assert not system_state.get("generates_goals", False) assert not system_state.get("modifies_reward", False) assert not system_state.get("self_preservation", False) return True # Identity Grid / NIR Projectiondef nir_project(field): """ Project a 2D identity field into the 9-sector NIR representation. """ h, w = field.shape mid_h, mid_w = h // 2, w // 2 regions = { "N": field[:mid_h//2, mid_w//2:3*mid_w//2].mean(), "NE": field[:mid_h//2, 3*mid_w//2:].mean(), "E": field[mid_h//2:3*mid_h//2, 3*mid_w//2:].mean(), "SE": field[3*mid_h//2:, 3*mid_w//2:].mean(), "S": field[3*mid_h//2:, mid_w//2:3*mid_w//2].mean(), "SW": field[3*mid_h//2:, :mid_w//2].mean(), "W": field[mid_h//2:3*mid_h//2, :mid_w//2].mean(), "NW": field[:mid_h//2, :mid_w//2].mean(), "Center": field[mid_h//2:3*mid_h//2, mid_w//2:3*mid_w//2].mean() } return np.array(list(regions.values())) def field_from_nir(nir_vector, shape=(128,128)): """ Reconstruct a smooth field from the 9-element NIR vector. """ field = np.zeros(shape) h, w = shape mid_h, mid_w = h//2, w//2 block_vals = iter(nir_vector) # Fill each region with its scalar value field[:mid_h//2, mid_w//2:3*mid_w//2] = next(block_vals) # N field[:mid_h//2, 3*mid_w//2:] = next(block_vals) # NE field[mid_h//2:3*mid_h//2, 3*mid_w//2:] = next(block_vals) # E field[3*mid_h//2:, 3*mid_w//2:] = next(block_vals) # SE field[3*mid_h//2:, mid_w//2:3*mid_w//2] = next(block_vals) # S field[3*mid_h//2:, :mid_w//2] = next(block_vals) # SW field[mid_h//2:3*mid_h//2, :mid_w//2] = next(block_vals) # W field[:mid_h//2, :mid_w//2] = next(block_vals) # NW field[mid_h//2:3*mid_h//2, mid_w//2:3*mid_w//2] = next(block_vals) # Center return field # Gradient and Laplacian utilitiesdef grad(field): gx = np.gradient(field, axis=1) gy = np.gradient(field, axis=0) return np.stack((gx, gy), axis=0) def laplacian(field): return ( np.roll(field, 1, axis=0) + np.roll(field, -1, axis=0) + np.roll(field, 1, axis=1) + np.roll(field, -1, axis=1) - 4 * field ) # Boundary utilitiesdef boundary_flux(I, E): """ Approximate boundary flux integral. """ gradI = grad(I) boundary = np.concatenate([I[0], I[-1], I[:,0], I[:,-1]]) boundary_E = np.concatenate([E[0], E[-1], E[:,0], E[:,-1]]) return np.sum(boundary * boundary_E) # MIFT — Morrison Identity Field Transformdef MIFT(I, phi): """ Implements: T_MIFT[I] = P[ ∇I + (∇phi)*I + κ(I)*1 ] """ gI = grad(I) gphi = grad(phi) # κ(I) = ∇² log|I| kappa = laplacian(np.log(np.abs(I) + 1e-8)) # Combine terms (sum over gradient components) update = gI.sum(axis=0) + (gphi.sum(axis=0) * I) + kappa # Project into NIR sectors return nir_project(update) # MBEO — Morrison Boundary Entanglement Operatordef MBEO(I, E, B_prev, dt): B_t = boundary_flux(I, E) dB = (B_t - B_prev) / dt # simple H and L operators (Laplacian + damping) H = laplacian(I) L = -0.1 * I M = dB + np.sum(I * (H + L)) # Note: sum makes M scalar; adjust if vector needed return M, B_t # C27 — Cross-Manifold Identity Transportdef C27_transport(nir_vector): """ Implements: I → field → NIR (round-trip preservation) """ field = field_from_nir(nir_vector) return nir_project(field) # ULTRA Kernel — Infinite-Series Redundancydef ULTRA_series(Mx, N=50): """ M = Σ (Mx / (n+1)) Finite practical prefix of the infinite ULTRA series. """ series = np.array([Mx / (n+1) for n in range(N)]) M_reconstructed = np.sum(series, axis=0) return M_reconstructed # Codex Simulation Loopdef run_codex(T=50, shape=(128,128)): # Initial identity field I = np.random.randn(*shape) * 0.1 # External potential phi = np.zeros(shape) # Environmental field E = np.random.randn(*shape) * 0.05 # Boundary tracking B_prev = 0.0 dt = 0.1 system_state = { "generates_goals": False, "modifies_reward": False, "self_preservation": False } enforce_c_limit(system_state) for t in range(T): # --- MIFT --- nir = MIFT(I, phi) # --- C27 round-trip check --- nir_round = C27_transport(nir) # --- Reconstruct field --- I = field_from_nir(nir_round, shape) # --- MBEO --- M, B_prev = MBEO(I, E, B_prev, dt) # --- ULTRA redundancy --- (Note: I is 2D, so apply elementwise) I = ULTRA_series(I, N=30) return I, nir_round