diff --git a/iohblade/problems/__init__.py b/iohblade/problems/__init__.py
index ed16650..14fd362 100644
--- a/iohblade/problems/__init__.py
+++ b/iohblade/problems/__init__.py
@@ -19,6 +19,10 @@
     from .mabbob import MA_BBOB
 except Exception:
     MA_BBOB = None
+try:
+    from .ng_friedman_suite import NG_FriedmanSuite
+except Exception:
+    NG_FriedmanSuite = None
 
 try:
     from .photonics import Photonics
diff --git a/iohblade/problems/ng_friedman_suite.py b/iohblade/problems/ng_friedman_suite.py
new file mode 100644
index 0000000..348f27c
--- /dev/null
+++ b/iohblade/problems/ng_friedman_suite.py
@@ -0,0 +1,288 @@
+import tempfile
+import warnings
+
+import ioh
+import iohinspector
+import nevergrad as ng
+import numpy as np
+import polars as pl
+from nevergrad.optimization.optimizerlib import BFGS, CMA, DE, PSO, Cobyla
+from scipy.stats import rankdata
+
+from ..problem import Problem
+from ..solution import Solution
+
+ALGORITHMS = {
+    "CMA": CMA,
+    "DE": DE,
+    "PSO": PSO,
+    "BFGS": BFGS,
+    "Cobyla": Cobyla,
+}
+
+
+class NG_Evaluator:
+    def __init__(self, optimizer: str, budget: int = 2000):
+        self.alg = optimizer
+        self.budget = budget
+
+    def __call__(self, func):
+        parametrization = ng.p.Array(shape=(func.meta_data.n_variables,)).set_bounds(
+            -5, 5
+        )
+        optimizer_cls = ALGORITHMS[self.alg]
+        optimizer = optimizer_cls(
+            parametrization=parametrization, budget=int(self.budget)
+        )
+        optimizer.minimize(func)
+
+
+def run_benchmark(problems, meta_dims, budget, repeats, log_root):
+    for name, problem in problems.items():
+        dim = meta_dims[name]
+        for alg_name in ALGORITHMS:
+            prob_wrapped = ioh.wrap_problem(
+                problem,
+                name,
+                ioh.ProblemClass.REAL,
+                dim,
+                lb=-5,
+                ub=5,
+            )
+            logger = ioh.logger.Analyzer(
+                root=log_root,
+                folder_name=f"{name}_{alg_name}_logs",
+                algorithm_name=alg_name,
+            )
+            prob_wrapped.attach_logger(logger)
+            optimizer = NG_Evaluator(alg_name, budget)
+            for _ in range(repeats):
+                optimizer(prob_wrapped)
+                prob_wrapped.reset()
+            logger.close()
+
+
+def get_friedman_val(dt_perf):
+    friedman_ranks = []
+
+    for func_name in dt_perf["function_name"].unique():
+        func_data = dt_perf.filter(pl.col("function_name") == func_name)
+        max_runs = (
+            func_data.group_by("algorithm_name")
+            .agg(pl.col("best_y").count())
+            .select(pl.col("best_y").max())[0, 0]
+        )
+        for run_idx in range(max_runs):
+            run_scores = []
+            run_algs = []
+
+            for alg_name in func_data["algorithm_name"].unique():
+                alg_scores = func_data.filter(pl.col("algorithm_name") == alg_name)[
+                    "best_y"
+                ].to_numpy()
+                if run_idx < len(alg_scores):
+                    run_scores.append(alg_scores[run_idx])
+                    run_algs.append(alg_name)
+
+            if not run_scores:
+                continue
+
+            ranks = rankdata(run_scores, method="average")
+
+            for alg_name, rank in zip(run_algs, ranks):
+                friedman_ranks.append(
+                    {
+                        "function_name": func_name,
+                        "algorithm_name": alg_name,
+                        "run": run_idx,
+                        "rank": rank,
+                    }
+                )
+
+    if not friedman_ranks:
+        return float("nan")
+
+    friedman_ranks_df = pl.DataFrame(friedman_ranks)
+    friedman_avg_ranks = friedman_ranks_df.group_by(
+        ["function_name", "algorithm_name"]
+    ).agg(pl.col("rank").mean().alias("rank"))
+    friedman_iqr = (
+        friedman_avg_ranks.group_by("algorithm_name")
+        .agg(
+            pl.col("rank").quantile(0.75).alias("q75"),
+            pl.col("rank").quantile(0.25).alias("q25"),
+        )
+        .with_columns((pl.col("q75") - pl.col("q25")).alias("iqr"))
+    )
+    return friedman_iqr["iqr"].mean()
+
+
+def _validate_suite(problems, meta_dims):
+    if not isinstance(problems, dict) or not isinstance(meta_dims, dict):
+        return "problems and meta_dims must be dictionaries."
+    if len(problems) != 25 or len(meta_dims) != 25:
+        return "problems and meta_dims must each contain 25 entries."
+    problem_keys = set(problems.keys())
+    meta_keys = set(meta_dims.keys())
+    if problem_keys != meta_keys:
+        return "problems and meta_dims must share identical keys."
+    for name, dim in meta_dims.items():
+        if not isinstance(dim, int) or dim <= 0:
+            return f"meta_dims[{name!r}] must be a positive integer."
+    return None
+
+
+class NG_FriedmanSuite(Problem):
+    """
+    Problem class for designing suites that discriminate between optimizers.
+    """
+
+    def __init__(
+        self,
+        logger=None,
+        training_instances=None,
+        test_instances=None,
+        name="NG_FriedmanSuite",
+        eval_timeout=600,
+        budget=5000,
+        repeats=5,
+        dependencies=None,
+        imports=None,
+    ):
+        if dependencies is None:
+            dependencies = [
+                "nevergrad>=1.0.0,<2",
+                "ioh==0.3.22",
+                "iohinspector>=0.3.0,<1",
+                "polars>=1.0.0,<2",
+                "scipy>=1.11.0,<2",
+            ]
+        if imports is None:
+            imports = (
+                "import numpy as np\n"
+                "import ioh\n"
+                "import nevergrad as ng\n"
+                "import polars as pl\n"
+            )
+        super().__init__(
+            logger, training_instances, test_instances, name, eval_timeout, dependencies
+        )
+        self.budget = budget
+        self.repeats = repeats
+        self.imports = imports
+
+        self.func_name = "problems"
+        self.init_inputs = []
+        self.func_inputs = []
+        self.func_outputs = []
+
+        self.task_prompt = f"""
+You are designing a suite of 25 continuous optimization problems. The goal is
+for this suite to strongly discriminate between five optimizers: CMA, DE, PSO,
+BFGS, and Cobyla (Nevergrad implementations). The evaluation maximizes the mean
+interquartile range (IQR) of Friedman ranks across functions, so more
+performance spread across algorithms is better.
+
+Each problem must be a Python callable `f(x)` that accepts a 1D numpy array and
+returns a float. Each function is wrapped with IOH on the domain [-5, 5]^d. You
+must provide a `problems` dictionary with 25 entries and a `meta_dims`
+dictionary with matching keys that provides each function's dimensionality.
+"""
+
+        self.example_prompt = """
+Example structure (you must provide your own problems):
+```python
+import numpy as np
+
+
+def sphere(x):
+    return float(np.sum(x ** 2))
+
+
+def ridge(x):
+    return float(np.sum(np.abs(x)) + 0.1 * np.sum(x ** 2))
+
+
+problems = {
+    "sphere": sphere,
+    "ridge": ridge,
+    # ... add 23 more functions
+}
+
+meta_dims = {
+    "sphere": 5,
+    "ridge": 12,
+    # ... add 23 more dimensions
+}
+```
+"""
+
+        self.format_prompt = """
+Provide your response in the following format:
+
+# Description: <one-line summary of the suite's discriminative strategy>
+# Code:
+```python
+<python code defining `problems` and `meta_dims`>
+```
+"""
+
+    def get_prompt(self):
+        return self.task_prompt + self.example_prompt + self.format_prompt
+
+    def evaluate(self, solution: Solution, test=False):
+        warnings.filterwarnings("ignore", category=Warning)
+
+        local_env = {}
+        try:
+            exec(solution.code, {}, local_env)
+        except Exception as exc:
+            solution.set_scores(
+                -np.inf,
+                feedback="Failed to execute suite definition code.",
+                error=str(exc),
+            )
+            return solution
+
+        problems = local_env.get("problems")
+        meta_dims = local_env.get("meta_dims")
+
+        error = _validate_suite(problems, meta_dims)
+        if error:
+            solution.set_scores(
+                -np.inf,
+                feedback=error,
+                error=error,
+            )
+            return solution
+
+        with tempfile.TemporaryDirectory() as log_root:
+            try:
+                run_benchmark(
+                    problems,
+                    meta_dims,
+                    self.budget,
+                    self.repeats,
+                    log_root,
+                )
+                dm = iohinspector.DataManager()
+                dm.add_folder(log_root)
+                dt_perf = dm.overview[["algorithm_name", "function_name", "best_y"]]
+                friedman_val = get_friedman_val(dt_perf)
+            except Exception as exc:
+                solution.set_scores(
+                    -np.inf,
+                    feedback="Evaluation failed during benchmarking.",
+                    error=str(exc),
+                )
+                return solution
+
+        solution.add_metadata("friedman_iqr", friedman_val)
+        solution.set_scores(
+            friedman_val,
+            "Suite scored on mean IQR of Friedman ranks (higher is better).",
+        )
+        return solution
+
+    def test(self, solution: Solution, ioh_dir=""):
+        return self.evaluate(solution, True)