Agent Conversation: google/gemini-3.1-pro-preview

Model: google/gemini-3.1-pro-preview

Total steps: 7

Messages: 34

UserStep 0

Task: causal-observational-nonlinear

Causal Discovery: Observational Nonlinear Data

Objective

Implement a causal discovery algorithm that recovers the DAG structure from purely observational data generated by a nonlinear Additive Noise Model (ANM). Your code goes in bench/custom_algorithm.py.

Background

Additive Noise Models (ANMs) assume structural equations of the form x_j = f_j(parents(j)) + e_j, where f_j are nonlinear functions and e_j are independent noise terms. Under mild conditions on f_j and the noise distribution, the causal DAG is identifiable from observational data alone (Hoyer et al., 2008; Peters et al., 2014). This goes beyond linear methods (LiNGAM, PC, GES) that assume linearity.

Key challenges include: (1) testing independence in high-dimensional nonlinear settings, (2) scaling to larger graphs, (3) handling diverse nonlinear function types (MLP, GP, polynomial, sigmoid), and (4) robustness to different noise distributions including the harder Gaussian noise case.

Evaluation Scenarios

Label	Graph type	Nodes	Samples	Noise	Nonlinearity
ER8-MLP	Erdos-Renyi	8	500	Exponential	MLP
ER12-GP	Erdos-Renyi	12	1000	Laplace	GP
SF10-Mixed	Scale-Free (BA)	10	500	Uniform	Mixed
ER15-Sigmoid	Erdos-Renyi	15	1000	Exponential	Sigmoid
ER20-Mixed	Erdos-Renyi	20	2000	Laplace	Mixed
SF20-GP	Scale-Free (BA)	20	2000	Exponential	GP
ER12-LowSample	Erdos-Renyi	12	150	Laplace	Mixed
ER20-Gauss	Erdos-Renyi	20	2000	Gaussian	Mixed

Metrics

All computed on the directed edge set (skeleton + direction must be correct):

F1 (primary ranking metric), SHD, Precision, Recall

Baselines

cam: Causal Additive Models (Buehlmann et al., 2014) -- score-based with GAM regression
notears_mlp: NOTEARS with MLP parametrization (Zheng et al., 2020) -- continuous optimization
directlingam: DirectLiNGAM (Shimizu et al., 2011) -- linear reference baseline

causal-learn/bench/custom_algorithm.py [EDITABLE — lines 3–14 only]

     1: import numpy as np
     2: 
     3: # =====================================================================
     4: # EDITABLE: implement run_causal_discovery below
     5: # =====================================================================
     6: def run_causal_discovery(X: np.ndarray) -> np.ndarray:
     7:     """
     8:     Input:  X of shape (n_samples, n_variables)
     9:     Output: adjacency matrix B of shape (n_variables, n_variables)
    10:             B[i, j] != 0  means j -> i  (follows causal-learn convention)
    11:     """
    12:     n = X.shape[1]
    13:     return np.zeros((n, n))
    14: # =====================================================================

cam baseline — editable region [READ-ONLY — reference implementation]

Lines 3–14:
     1: import numpy as n

AssistantStep 1edit

Tool Input

1{
2  "end_line": 14,
3  "op": "replace",
4  "filename": "bench/custom_algorithm.py",
5  "content": "    import warnings\n    from sklearn.model_selection import KFold\n    from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor\n    from sklearn.preprocessing import StandardScaler\n\n    warnings.filterwarnings(\"ignore\")\n\n    n_samples, n_vars = X.shape\n\n    # Standardize data to normalize density scales\n    scaler = StandardScaler()\n    X_scaled = scaler.fit_transform(X)\n\n    # Entropy estimator parameter m\n    m_spacing = max(2, int(np.sqrt(n_samples)) // 2)\n\n    def calculate_entropy(residuals):\n        n = len(residuals)\n        if n < 2 * m_spacing + 5:\n            return 0.5 * np.log(np.var(residuals) + 1e-8)\n        res_sorted = np.sort(residuals)\n        diff = res_sorted[2 * m_spacing:] - res_sorted[:-2 * m_spacing]\n        diff = np.maximum(diff, 1e-9)\n        return np.mean(np.log(diff))\n\n    memo = {}\n\n    def get_score(target, parents):\n        parents_tuple = tuple(sorted(parents))\n        if parents_tuple in memo.get(target, {}):\n            return memo[target][parents_tuple]\n\n        X_target = X_scaled[:, target]\n        if len(parents) == 0:\n            score = calculate_entropy(X_target - np.mean(X_target))\n        else:\n            X_features = X_scaled[:, parents]\n            kf = KFold(n_splits=5, shuffle=True, random_state=42)\n            residuals = np.zeros_like(X_target)\n\n            if n_samples < 500:\n                model = RandomForestRegressor(n_estimators=30, max_depth=5, random_state=42, n_jobs=1)\n            else:\n                model = HistGradientBoostingRegressor(max_iter=40, max_depth=5, min_samples_leaf=10, random_state=42)\n\n            for train_index, test_index in kf.split(X_features):\n                X_train, X_test = X_features[train_index], X_features[test_index]\n                y_train, y_test = X_target[train_index], X_target[test_index]\n                model.fit(X_train, y_train)\n                residuals[test_index] = y_test - model.predict(X_test)\n\n            # BIC-like complexity penalty for edges\n            penalty = 0.05 * len(parents)\n            score = calculate_entropy(residuals) + penalty\n\n        if target not in memo:\n            memo[target] = {}\n        memo[target][parents_tuple] = score\n        return score\n\n    parents_list = [[] for _ in range(n_vars)]\n    current_scores = [get_score(i, []) for i in range(n_vars)]\n\n    def check_cycle(p_list):\n        visited = [0] * n_vars\n        rec_stack = [0] * n_vars\n\n        def dfs(node):\n            visited[node] = 1\n            rec_stack[node] = 1\n            for p in p_list[node]:\n                if visited[p] == 0:\n                    if dfs(p): return True\n                elif rec_stack[p] == 1:\n                    return True\n            rec_stack[node] = 0\n            return False\n\n        for i in range(n_vars):\n            if visited[i] == 0:\n                if dfs(i): return True\n        return False\n\n    # Forward greedy search\n    while True:\n        best_reduction = 0\n        best_edge = None\n        best_edge_score = 0\n\n        for p in range(n_vars):\n            for j in range(n_vars):\n                if p == j or p in parents_list[j]:\n                    continue\n\n                parents_list[j].append(p)\n                has_cycle = check_cycle(parents_list)\n                parents_list[j].remove(p)\n\n                if has_cycle:\n                    continue\n\n                new_parents = parents_list[j] + [p]\n                new_score = get_score(j, new_parents)\n                reduction = current_scores[j] - new_score\n\n                if reduction > best_reduction:\n                    best_reduction = reduction\n                    best_edge = (p, j)\n                    best_edge_score = new_score\n\n        if best_reduction > 1e-4:\n            p, j = best_edge\n            parents_list[j].append(p)\n            current_scores[j] = best_edge_score\n        else:\n            break\n\n    # Backward pruning\n    while True:\n        best_reduction = 0\n        best_edge = None\n        best_edge_score = 0\n\n        for j in range(n_vars):\n            for p in parents_list[j]:\n                new_parents = parents_list[j].copy()\n                new_parents.remove(p)\n\n                new_score = get_score(j, new_parents)\n                reduction = current_scores[j] - new_score\n\n                if reduction > best_reduction:\n                    best_reduction = reduction\n                    best_edge = (p, j)\n                    best_edge_score = new_score\n\n        if best_reduction > 1e-4:\n            p, j = best_edge\n            parents_list[j].remove(p)\n            current_scores[j] = best_edge_score\n        else:\n            break\n\n    B = np.zeros((n_vars, n_vars))\n    for j in range(n_vars):\n        for p in parents_list[j]:\n            B[j, p] = 1.0  # causal-learn format: target j gets from source p\n\n    return B",
6  "after_line": 14,
7  "start_line": 12
8}

Tool ResultStep 1

ERROR: Package 'bench' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "end_line": 14,
3  "content": "    import warnings\n    from sklearn.model_selection import KFold\n    from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor\n    from sklearn.preprocessing import StandardScaler\n\n    warnings.filterwarnings(\"ignore\")\n\n    n_samples, n_vars = X.shape\n\n    # Standardize data to normalize density scales\n    scaler = StandardScaler()\n    X_scaled = scaler.fit_transform(X)\n\n    # Entropy estimator parameter m\n    m_spacing = max(2, int(np.sqrt(n_samples)) // 2)\n\n    def calculate_entropy(residuals):\n        n = len(residuals)\n        if n < 2 * m_spacing + 5:\n            return 0.5 * np.log(np.var(residuals) + 1e-8)\n        res_sorted = np.sort(residuals)\n        diff = res_sorted[2 * m_spacing:] - res_sorted[:-2 * m_spacing]\n        diff = np.maximum(diff, 1e-9)\n        return np.mean(np.log(diff))\n\n    memo = {}\n\n    def get_score(target, parents):\n        parents_tuple = tuple(sorted(parents))\n        if parents_tuple in memo.get(target, {}):\n            return memo[target][parents_tuple]\n\n        X_target = X_scaled[:, target]\n        if len(parents) == 0:\n            score = calculate_entropy(X_target - np.mean(X_target))\n        else:\n            X_features = X_scaled[:, parents]\n            kf = KFold(n_splits=5, shuffle=True, random_state=42)\n            residuals = np.zeros_like(X_target)\n\n            if n_samples < 500:\n                model = RandomForestRegressor(n_estimators=30, max_depth=5, random_state=42, n_jobs=1)\n            else:\n                model = HistGradientBoostingRegressor(max_iter=40, max_depth=5, min_samples_leaf=10, random_state=42)\n\n            for train_index, test_index in kf.split(X_features):\n                X_train, X_test = X_features[train_index], X_features[test_index]\n                y_train, y_test = X_target[train_index], X_target[test_index]\n                model.fit(X_train, y_train)\n                residuals[test_index] = y_test - model.predict(X_test)\n\n            # BIC-like complexity penalty for edges\n            penalty = 0.05 * len(parents)\n            score = calculate_entropy(residuals) + penalty\n\n        if target not in memo:\n            memo[target] = {}\n        memo[target][parents_tuple] = score\n        return score\n\n    parents_list = [[] for _ in range(n_vars)]\n    current_scores = [get_score(i, []) for i in range(n_vars)]\n\n    def check_cycle(p_list):\n        visited = [0] * n_vars\n        rec_stack = [0] * n_vars\n\n        def dfs(node):\n            visited[node] = 1\n            rec_stack[node] = 1\n            for p in p_list[node]:\n                if visited[p] == 0:\n                    if dfs(p): return True\n                elif rec_stack[p] == 1:\n                    return True\n            rec_stack[node] = 0\n            return False\n\n        for i in range(n_vars):\n            if visited[i] == 0:\n                if dfs(i): return True\n        return False\n\n    # Forward greedy search\n    while True:\n        best_reduction = 0\n        best_edge = None\n        best_edge_score = 0\n\n        for p in range(n_vars):\n            for j in range(n_vars):\n                if p == j or p in parents_list[j]:\n                    continue\n\n                parents_list[j].append(p)\n                has_cycle = check_cycle(parents_list)\n                parents_list[j].remove(p)\n\n                if has_cycle:\n                    continue\n\n                new_parents = parents_list[j] + [p]\n                new_score = get_score(j, new_parents)\n                reduction = current_scores[j] - new_score\n\n                if reduction > best_reduction:\n                    best_reduction = reduction\n                    best_edge = (p, j)\n                    best_edge_score = new_score\n\n        if best_reduction > 1e-4:\n            p, j = best_edge\n            parents_list[j].append(p)\n            current_scores[j] = best_edge_score\n        else:\n            break\n\n    # Backward pruning\n    while True:\n        best_reduction = 0\n        best_edge = None\n        best_edge_score = 0\n\n        for j in range(n_vars):\n            for p in parents_list[j]:\n                new_parents = parents_list[j].copy()\n                new_parents.remove(p)\n\n                new_score = get_score(j, new_parents)\n                reduction = current_scores[j] - new_score\n\n                if reduction > best_reduction:\n                    best_reduction = reduction\n                    best_edge = (p, j)\n                    best_edge_score = new_score\n\n        if best_reduction > 1e-4:\n            p, j = best_edge\n            parents_list[j].remove(p)\n            current_scores[j] = best_edge_score\n        else:\n            break\n\n    B = np.zeros((n_vars, n_vars))\n    for j in range(n_vars):\n        for p in parents_list[j]:\n            B[j, p] = 1.0  # causal-learn format: target j gets from source p\n\n    return B",
4  "after_line": 14,
5  "filename": "causal-learn/bench/custom_algorithm.py",
6  "op": "replace",
7  "start_line": 12
8}

Tool ResultStep 2

OK: Replaced lines 12..14 with 149 line(s) in causal-learn/bench/custom_algorithm.py. Editable range: 3–160.

[Current file: causal-learn/bench/custom_algorithm.py | editable: 3–160 | total: 160 lines]
     1| import numpy as np
     2| 
     3  # =====================================================================
     4  # EDITABLE: implement run_causal_discovery below
     5  # =====================================================================
     6  def run_causal_discovery(X: np.ndarray) -> np.ndarray:
     7      """
     8      Input:  X of shape (n_samples, n_variables)
     9      Output: adjacency matrix B of shape (n_variables, n_variables)
    10              B[i, j] != 0  means j -> i  (follows causal-learn convention)
    11      """
    12      import warnings
    13      from sklearn.model_selection import KFold
    14      from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor
    15      from sklearn.preprocessing import StandardScaler

AssistantStep 3test

AssistantStep 1edit

Tool Input

1{
2  "op": "replace",
3  "end_line": 14,
4  "content": "def run_causal_discovery(X: np.ndarray) -> np.ndarray:\n    import numpy as np\n    from sklearn.preprocessing import SplineTransformer\n    from sklearn.linear_model import RidgeCV\n\n    n_samples, n_vars = X.shape\n    \n    # Standardize X\n    X = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-8)\n    \n    def check_path(adj, start, end):\n        if start == end: return True\n        visited = set([start])\n        queue = [start]\n        while queue:\n            node = queue.pop(0)\n            for child in range(n_vars):\n                if adj[node, child] == 1:\n                    if child == end:\n                        return True\n                    if child not in visited:\n                        visited.add(child)\n                        queue.append(child)\n        return False\n\n    def compute_score(target, parents):\n        y = X[:, target]\n        if len(parents) == 0:\n            var = np.var(y)\n            return -n_samples/2 * np.log(max(var, 1e-12))\n            \n        X_pa = X[:, parents]\n        try:\n            st = SplineTransformer(n_knots=5, degree=3, include_bias=False)\n            X_splines = st.fit_transform(X_pa)\n        except Exception:\n            X_splines = X_pa\n            \n        alphas = np.logspace(-3, 3, 10)\n        model = RidgeCV(alphas=alphas, store_cv_values=True)\n        model.fit(X_splines, y)\n        \n        cv_values = model.cv_values_\n        if cv_values.ndim == 3:\n            best_alpha_idx = np.argmin(np.mean(cv_values[:, 0, :], axis=0))\n            loo_mse = np.mean(cv_values[:, 0, best_alpha_idx])\n        else:\n            best_alpha_idx = np.argmin(np.mean(cv_values, axis=0))\n            loo_mse = np.mean(cv_values[:, best_alpha_idx])\n            \n        penalty = len(parents) * np.log(n_samples)\n        \n        return -n_samples/2 * np.log(max(loo_mse, 1e-12)) - penalty\n\n    score_cache = {}\n    def get_score(target, parents):\n        p_tuple = tuple(sorted(parents))\n        if (target, p_tuple) not in score_cache:\n            score_cache[(target, p_tuple)] = compute_score(target, parents)\n        return score_cache[(target, p_tuple)]\n        \n    adj = np.zeros((n_vars, n_vars), dtype=int)\n    current_scores = np.zeros(n_vars)\n    for i in range(n_vars):\n        current_scores[i] = get_score(i, [])\n\n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if i == j: continue\n                if adj[i, j] == 1: continue\n                if check_path(adj, j, i): \n                    continue\n                \n                pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                if len(pa_j) >= 6: \n                    continue\n                pa_j.append(i)\n                \n                new_score = get_score(j, pa_j)\n                gain = new_score - current_scores[j]\n                \n                if gain > best_gain:\n                    best_gain = gain\n                    best_edge = (i, j)\n                    best_score_new = new_score\n                    \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 1\n            current_scores[j] = best_score_new\n        else:\n            break\n            \n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if adj[i, j] == 1:\n                    pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                    pa_j.remove(i)\n                    \n                    new_score = get_score(j, pa_j)\n                    gain = new_score - current_scores[j]\n                    \n                    if gain > best_gain:\n                        best_gain = gain\n                        best_edge = (i, j)\n                        best_score_new = new_score\n                        \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 0\n            current_scores[j] = best_score_new\n        else:\n            break\n\n    B = adj.T.astype(float)\n    return B",
5  "filename": "bench/custom_algorithm.py",
6  "after_line": 3,
7  "start_line": 6
8}

Tool ResultStep 1

ERROR: Package 'bench' is not in allowed packages

AssistantStep 2edit

Tool Input

1{
2  "filename": "causal-learn/bench/custom_algorithm.py",
3  "start_line": 6,
4  "content": "def run_causal_discovery(X: np.ndarray) -> np.ndarray:\n    import numpy as np\n    from sklearn.preprocessing import SplineTransformer\n    from sklearn.linear_model import RidgeCV\n\n    n_samples, n_vars = X.shape\n    \n    # Standardize X\n    X = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-8)\n    \n    def check_path(adj, start, end):\n        if start == end: return True\n        visited = set([start])\n        queue = [start]\n        while queue:\n            node = queue.pop(0)\n            for child in range(n_vars):\n                if adj[node, child] == 1:\n                    if child == end:\n                        return True\n                    if child not in visited:\n                        visited.add(child)\n                        queue.append(child)\n        return False\n\n    def compute_score(target, parents):\n        y = X[:, target]\n        if len(parents) == 0:\n            var = np.var(y)\n            return -n_samples/2 * np.log(max(var, 1e-12))\n            \n        X_pa = X[:, parents]\n        try:\n            st = SplineTransformer(n_knots=5, degree=3, include_bias=False)\n            X_splines = st.fit_transform(X_pa)\n        except Exception:\n            X_splines = X_pa\n            \n        alphas = np.logspace(-3, 3, 10)\n        model = RidgeCV(alphas=alphas, store_cv_values=True)\n        model.fit(X_splines, y)\n        \n        cv_values = model.cv_values_\n        if cv_values.ndim == 3:\n            best_alpha_idx = np.argmin(np.mean(cv_values[:, 0, :], axis=0))\n            loo_mse = np.mean(cv_values[:, 0, best_alpha_idx])\n        else:\n            best_alpha_idx = np.argmin(np.mean(cv_values, axis=0))\n            loo_mse = np.mean(cv_values[:, best_alpha_idx])\n            \n        penalty = len(parents) * np.log(n_samples)\n        \n        return -n_samples/2 * np.log(max(loo_mse, 1e-12)) - penalty\n\n    score_cache = {}\n    def get_score(target, parents):\n        p_tuple = tuple(sorted(parents))\n        if (target, p_tuple) not in score_cache:\n            score_cache[(target, p_tuple)] = compute_score(target, parents)\n        return score_cache[(target, p_tuple)]\n        \n    adj = np.zeros((n_vars, n_vars), dtype=int)\n    current_scores = np.zeros(n_vars)\n    for i in range(n_vars):\n        current_scores[i] = get_score(i, [])\n\n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if i == j: continue\n                if adj[i, j] == 1: continue\n                if check_path(adj, j, i): \n                    continue\n                \n                pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                if len(pa_j) >= 6: \n                    continue\n                pa_j.append(i)\n                \n                new_score = get_score(j, pa_j)\n                gain = new_score - current_scores[j]\n                \n                if gain > best_gain:\n                    best_gain = gain\n                    best_edge = (i, j)\n                    best_score_new = new_score\n                    \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 1\n            current_scores[j] = best_score_new\n        else:\n            break\n            \n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if adj[i, j] == 1:\n                    pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                    pa_j.remove(i)\n                    \n                    new_score = get_score(j, pa_j)\n                    gain = new_score - current_scores[j]\n                    \n                    if gain > best_gain:\n                        best_gain = gain\n                        best_edge = (i, j)\n                        best_score_new = new_score\n                        \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 0\n            current_scores[j] = best_score_new\n        else:\n            break\n\n    B = adj.T.astype(float)\n    return B",
5  "after_line": 3,
6  "op": "replace",
7  "end_line": 14
8}

Tool ResultStep 2

OK: Replaced lines 6..14 with 126 line(s) in causal-learn/bench/custom_algorithm.py. Editable range: 3–131.

[Current file: causal-learn/bench/custom_algorithm.py | editable: 3–131 | total: 131 lines]
     1| import numpy as np
     2| 
     3  # =====================================================================
     4  # EDITABLE: implement run_causal_discovery below
     5  # =====================================================================
     6  def run_causal_discovery(X: np.ndarray) -> np.ndarray:
     7      import numpy as np
     8      from sklearn.preprocessing import SplineTransformer
     9      from sklearn.linear_model import RidgeCV
    10  
    11      n_samples, n_vars = X.shape
    12      
    13      # Standardize X
    14      X = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-8)
    15      
    16      def check_path(adj, start, end):
    17          if start == end: return True
    18          visited = set([start])
    19          queue = [start]

AssistantStep 3test

Tool ResultStep 3

[Test #1] (2 tests remaining; call submit(n=1) to submit this result)

### ER8-MLP (scripts/eval_er8_mlp.sh)
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/run_eval.py", line 63, in <module>
    main()
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/run_eval.py", line 49, in main
    B_est = run_causal_discovery(X)
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/custom_algorithm.py", line 89, in run_causal_discovery
    new_score = get_score(j, pa_j)
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/custom_algorithm.py", line 64, in get_score
    score

AssistantStep 4edit

Tool Input

1{
2  "after_line": 3,
3  "filename": "causal-learn/bench/custom_algorithm.py",
4  "start_line": 6,
5  "end_line": 101,
6  "content": "def run_causal_discovery(X: np.ndarray) -> np.ndarray:\n    import numpy as np\n    from sklearn.preprocessing import StandardScaler, SplineTransformer\n    from sklearn.linear_model import LinearRegression\n\n    n_samples, n_vars = X.shape\n    \n    X = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-8)\n    \n    def check_path(adj, start, end):\n        if start == end: return True\n        visited = set([start])\n        queue = [start]\n        while queue:\n            node = queue.pop(0)\n            for child in range(n_vars):\n                if adj[node, child] == 1:\n                    if child == end:\n                        return True\n                    if child not in visited:\n                        visited.add(child)\n                        queue.append(child)\n        return False\n\n    def compute_score(target, parents):\n        y = X[:, target]\n        if len(parents) == 0:\n            var = np.var(y)\n            return -n_samples/2 * np.log(max(var, 1e-12))\n            \n        X_pa = X[:, parents]\n        try:\n            st = SplineTransformer(n_knots=5, degree=3, include_bias=False)\n            X_splines = st.fit_transform(X_pa)\n        except Exception:\n            X_splines = X_pa\n            \n        model = LinearRegression()\n        model.fit(X_splines, y)\n        preds = model.predict(X_splines)\n        mse = np.mean((y - preds)**2)\n        \n        df = X_splines.shape[1]\n        penalty = df * np.log(n_samples) / 2\n        \n        return -n_samples/2 * np.log(max(mse, 1e-12)) - penalty\n\n    score_cache = {}\n    def get_score(target, parents):\n        p_tuple = tuple(sorted(parents))\n        if (target, p_tuple) not in score_cache:\n            score_cache[(target, p_tuple)] = compute_score(target, parents)\n        return score_cache[(target, p_tuple)]\n        \n    adj = np.zeros((n_vars, n_vars), dtype=int)\n    current_scores = np.zeros(n_vars)\n    for i in range(n_vars):\n        current_scores[i] = get_score(i, [])\n\n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if i == j: continue\n                if adj[i, j] == 1: continue\n                if check_path(adj, j, i): \n                    continue\n                \n                pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                if len(pa_j) >= 6: \n                    continue\n                pa_j.append(i)\n                \n                new_score = get_score(j, pa_j)\n                gain = new_score - current_scores[j]\n                \n                if gain > best_gain:\n                    best_gain = gain\n                    best_edge = (i, j)\n                    best_score_new = new_score\n                    \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 1\n            current_scores[j] = best_score_new\n        else:\n            break\n            \n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if adj[i, j] == 1:\n                    pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                    pa_j.remove(i)\n                    \n                    new_score = get_score(j, pa_j)\n                    gain = new_score - current_scores[j]\n                    \n                    if gain > best_gain:\n                        best_gain = gain\n                        best_edge = (i, j)\n                        best_score_new = new_score\n                        \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 0\n            current_scores[j] = best_score_new\n        else:\n            break\n\n    B = adj.T.astype(float)\n    return B",
7  "op": "replace"
8}

Tool ResultStep 4

OK: Replaced lines 6..101 with 119 line(s) in causal-learn/bench/custom_algorithm.py. Editable range: 3–154.

[Current file: causal-learn/bench/custom_algorithm.py | editable: 3–154 | total: 154 lines]
     1| import numpy as np
     2| 
     3  # =====================================================================
     4  # EDITABLE: implement run_causal_discovery below
     5  # =====================================================================
     6  def run_causal_discovery(X: np.ndarray) -> np.ndarray:
     7      import numpy as np
     8      from sklearn.preprocessing import StandardScaler, SplineTransformer
     9      from sklearn.linear_model import LinearRegression
    10  
    11      n_samples, n_vars = X.shape
    12      
    13      X = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-8)
    14      
    15      def check_path(adj, start, end):
    16          if start == end: return True
    17          visited = set([start])
    18          queue = [start]

AssistantStep 5test

Tool ResultStep 5

[Test #2] (1 test remaining; call submit(n=2) to submit this result)

### ER8-MLP (scripts/eval_er8_mlp.sh)
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/run_eval.py", line 10, in <module>
    from custom_algorithm import run_causal_discovery
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/custom_algorithm.py", line 125
    break
IndentationError: unexpected indent
ERROR conda.cli.main_run:execute(142): `conda run bash /srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/.local_scripts/scripts/eval_er8_mlp.sh` failed. (See above for error)


### ER12-GP (scripts/eval_er12_gp.sh)
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observa

AssistantStep 6edit

Tool Input

1{
2  "end_line": 98,
3  "op": "replace",
4  "content": "def run_causal_discovery(X: np.ndarray) -> np.ndarray:\n    import numpy as np\n    from sklearn.linear_model import LinearRegression\n\n    n_samples, n_vars = X.shape\n    \n    # Standardize\n    X = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-8)\n    \n    def check_path(adj, start, end):\n        if start == end: return True\n        visited = set([start])\n        queue = [start]\n        while queue:\n            node = queue.pop(0)\n            for child in range(n_vars):\n                if adj[node, child] == 1:\n                    if child == end:\n                        return True\n                    if child not in visited:\n                        visited.add(child)\n                        queue.append(child)\n        return False\n\n    def compute_score(target, parents):\n        y = X[:, target]\n        if len(parents) == 0:\n            var = np.var(y)\n            return -n_samples/2 * np.log(max(var, 1e-12))\n            \n        X_pa = X[:, parents]\n        try:\n            from sklearn.preprocessing import SplineTransformer\n            st = SplineTransformer(n_knots=5, degree=3, include_bias=False)\n            X_feats = st.fit_transform(X_pa)\n        except ImportError:\n            from sklearn.preprocessing import PolynomialFeatures\n            pf = PolynomialFeatures(degree=3, include_bias=False)\n            X_feats = pf.fit_transform(X_pa)\n            \n        model = LinearRegression()\n        model.fit(X_feats, y)\n        preds = model.predict(X_feats)\n        mse = np.mean((y - preds)**2)\n        \n        df = X_feats.shape[1]\n        penalty = df * np.log(n_samples) / 2\n        \n        return -n_samples/2 * np.log(max(mse, 1e-12)) - penalty\n\n    score_cache = {}\n    def get_score(target, parents):\n        p_tuple = tuple(sorted(parents))\n        if (target, p_tuple) not in score_cache:\n            score_cache[(target, p_tuple)] = compute_score(target, parents)\n        return score_cache[(target, p_tuple)]\n        \n    adj = np.zeros((n_vars, n_vars), dtype=int)\n    current_scores = np.zeros(n_vars)\n    for i in range(n_vars):\n        current_scores[i] = get_score(i, [])\n\n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if i == j: continue\n                if adj[i, j] == 1: continue\n                if check_path(adj, j, i): \n                    continue\n                \n                pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                if len(pa_j) >= 6: \n                    continue\n                pa_j.append(i)\n                \n                new_score = get_score(j, pa_j)\n                gain = new_score - current_scores[j]\n                \n                if gain > best_gain:\n                    best_gain = gain\n                    best_edge = (i, j)\n                    best_score_new = new_score\n                    \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 1\n            current_scores[j] = best_score_new\n        else:\n            break\n            \n    # Pruning phase\n    while True:\n        best_gain = -1e-6\n        best_edge = None\n        best_score_new = None\n        \n        for i in range(n_vars):\n            for j in range(n_vars):\n                if adj[i, j] == 1:\n                    pa_j = [p for p in range(n_vars) if adj[p, j] == 1]\n                    pa_j.remove(i)\n                    \n                    new_score = get_score(j, pa_j)\n                    gain = new_score - current_scores[j]\n                    \n                    if gain > best_gain:\n                        best_gain = gain\n                        best_edge = (i, j)\n                        best_score_new = new_score\n                        \n        if best_edge is not None and best_gain > 0:\n            i, j = best_edge\n            adj[i, j] = 0\n            current_scores[j] = best_score_new\n        else:\n            break\n\n    # causallearn convention: B[i, j] != 0 means j -> i\n    # Our adj[i, j] == 1 means i -> j. So B = adj.T\n    B = adj.T.astype(float)\n    return B",
5  "after_line": 3,
6  "start_line": 6,
7  "filename": "causal-learn/bench/custom_algorithm.py"
8}

Tool ResultStep 6

OK: Replaced lines 6..98 with 125 line(s) in causal-learn/bench/custom_algorithm.py. Editable range: 3–186.

[Current file: causal-learn/bench/custom_algorithm.py | editable: 3–186 | total: 186 lines]
     1| import numpy as np
     2| 
     3  # =====================================================================
     4  # EDITABLE: implement run_causal_discovery below
     5  # =====================================================================
     6  def run_causal_discovery(X: np.ndarray) -> np.ndarray:
     7      import numpy as np
     8      from sklearn.linear_model import LinearRegression
     9  
    10      n_samples, n_vars = X.shape
    11      
    12      # Standardize
    13      X = (X - np.mean(X, axis=0)) / (np.std(X, axis=0) + 1e-8)
    14      
    15      def check_path(adj, start, end):
    16          if start == end: return True
    17          visited = set([start])
    18          queue = [start]
    19          while queue:
    20              node = qu

AssistantStep 7test

Tool ResultStep 7

[Test #3] (0 tests remaining)

### ER8-MLP (scripts/eval_er8_mlp.sh)
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/run_eval.py", line 10, in <module>
    from custom_algorithm import run_causal_discovery
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/causal-learn/bench/custom_algorithm.py", line 131
    best_edge = None
IndentationError: unexpected indent
ERROR conda.cli.main_run:execute(142): `conda run bash /srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemini-3.1-pro-preview_20260402_183953/.local_scripts/scripts/eval_er8_mlp.sh` failed. (See above for error)


### ER12-GP (scripts/eval_er12_gp.sh)
Traceback (most recent call last):
  File "/srv/home/bohanlyu/MLS-Bench/vendor/workspace/causal-observational-nonlinear/google_gemi