diff --git a/benchmarks/DBPEDIA_MANIFEST.json b/benchmarks/DBPEDIA_MANIFEST.json
new file mode 100644
index 00000000..d596abcf
--- /dev/null
+++ b/benchmarks/DBPEDIA_MANIFEST.json
@@ -0,0 +1,21 @@
+{
+  "dataset": "openai-1536-angular",
+  "source": "SuperKMeans benchmark data_openai.bin and data_openai_test.bin",
+  "metric": "inner_product",
+  "normalization": "unit-normalized source values preserved exactly",
+  "dimension": 1536,
+  "base_count": 999000,
+  "query_count": 1000,
+  "groundtruth_k": 100,
+  "files": {
+    "base.fvecs": {
+      "bytes": 6141852000
+    },
+    "queries.fvecs": {
+      "bytes": 6148000
+    },
+    "queries.ivecs": {
+      "bytes": 404000
+    }
+  }
+}
diff --git a/benchmarks/README.md b/benchmarks/README.md
new file mode 100644
index 00000000..85d2cf07
--- /dev/null
+++ b/benchmarks/README.md
@@ -0,0 +1,24 @@
+# Benchmarks
+
+See the [benchmark overview](../docs/docs/benchmarks/bench.md) for datasets,
+installation, measurement definitions, file locations, and reproduction options.
+Results: [IVF](../docs/docs/benchmarks/ivf.md),
+[graphs](../docs/docs/benchmarks/graphs.md), and
+[k-means](../docs/docs/benchmarks/kmeans.md).
+
+From the repository root, after installing the documented system prerequisites
+and activating a Python 3.13 environment:
+
+```bash
+python benchmarks/install_baselines.py --jobs 12
+source benchmarks/env.sh
+python benchmarks/reproduce.py
+```
+
+For a subset or a command preview:
+
+```bash
+python benchmarks/reproduce.py --benchmarks ivf --datasets gist1m
+python benchmarks/reproduce.py --datasets dbpedia --methods qgkmeans
+python benchmarks/reproduce.py --dry-run
+```
diff --git a/benchmarks/build_indexes.py b/benchmarks/build_indexes.py
new file mode 100644
index 00000000..15c19c9d
--- /dev/null
+++ b/benchmarks/build_indexes.py
@@ -0,0 +1,199 @@
+"""Build ANN indexes and verify search results before parameter sweeps."""
+
+import argparse
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+RUNNER = ROOT / "benchmarks" / "run.py"
+
+DATASETS = {
+    "gist1m": (
+        "data/gist/gist_base.fvecs",
+        "data/gist/gist_query.fvecs",
+        "data/gist/gist_groundtruth.ivecs",
+        "l2",
+    ),
+    "dbpedia": (
+        "data/dbpedia/base.fvecs",
+        "data/dbpedia/queries.fvecs",
+        "data/dbpedia/queries.ivecs",
+        "ip",
+    ),
+}
+DATASET_SHAPES = {"gist1m": (1000000, 960), "dbpedia": (999000, 1536)}
+
+CASES = (
+    ("rabitq-ivf-raw", "rabitq-ivf", ("--bits", "32")),
+    ("rabitq-ivf-1p4", "rabitq-ivf", ("--bits", "5")),
+    ("rabitq-ivf-1p8", "rabitq-ivf", ("--bits", "9")),
+    ("faiss-rabitqfs-raw", "faiss-ivf-rabitq-fs", ("--bits", "1")),
+    ("faiss-rabitqfs-1p4", "faiss-ivf-rabitq-fs", ("--bits", "5")),
+    ("faiss-rabitqfs-1p8", "faiss-ivf-rabitq-fs", ("--bits", "9")),
+    ("faiss-pqfs", "faiss-ivf-pqfs", ()),
+    ("faiss-opq-pqfs", "faiss-ivf-pqfs", ("--opq",)),
+    ("rabitq-symqg-raw", "rabitq-symqg", ("--quantization-bits", "0")),
+    ("rabitq-symqg-4", "rabitq-symqg", ("--quantization-bits", "4")),
+    ("rabitq-symqg-8", "rabitq-symqg", ("--quantization-bits", "8")),
+    ("rabitq-hnsw-1p4", "rabitq-hnsw", ("--bits", "5")),
+    ("rabitq-hnsw-1p8", "rabitq-hnsw", ("--bits", "9")),
+    ("faiss-hnsw-flat", "faiss-hnsw-flat", ()),
+    ("faiss-hnsw-sq4", "faiss-hnsw-sq4", ()),
+    ("faiss-hnsw-sq8", "faiss-hnsw-sq8", ()),
+)
+
+LABELS = (
+    "RaBitQ IVF raw",
+    "RaBitQ IVF 1+4",
+    "RaBitQ IVF 1+8",
+    "Faiss IVF RaBitQfs raw + RFlat",
+    "Faiss IVF RaBitQfs 1+4",
+    "Faiss IVF RaBitQfs 1+8",
+    "Faiss IVF PQfs + RFlat",
+    "Faiss OPQ-IVF PQfs + RFlat",
+    "RaBitQ SymphonyQG raw",
+    "RaBitQ SymphonyQG 4-bit",
+    "RaBitQ SymphonyQG 8-bit",
+    "RaBitQ HNSW 1+4",
+    "RaBitQ HNSW 1+8",
+    "Faiss HNSW Flat",
+    "Faiss HNSW SQ4",
+    "Faiss HNSW SQ8",
+)
+
+
+def validate_record(record, dataset, name):
+    """Reject results that do not match the published ANN configuration."""
+    method, extra = next((m, e) for n, m, e in CASES if n == name)
+    base, queries, truth, metric = DATASETS[dataset]
+    n, dim = DATASET_SHAPES[dataset]
+    expected = {
+        "method": method,
+        "metric": metric,
+        "base_count": n,
+        "query_count": 1000,
+        "base_limit": None,
+        "query_limit": None,
+        "threads": 48,
+        "socket": 0,
+        "k": 10,
+    }
+    if "ivf" in method:
+        expected.update(nlist=4096, opq="--opq" in extra)
+    if "hnsw" in method:
+        expected.update(m=16, ef_construction=200)
+        if method == "rabitq-hnsw":
+            expected["hnsw_clusters"] = 16
+    if method == "rabitq-symqg":
+        expected.update(degree=32, ef_construction=200)
+    for option in ("--bits", "--quantization-bits"):
+        if option in extra:
+            expected[option[2:].replace("-", "_")] = int(extra[extra.index(option) + 1])
+    cfg = record["config"]
+    if (
+        any(cfg.get(key) != value for key, value in expected.items())
+        or record["data"]["dimension"] != dim
+        or any(
+            Path(cfg[key]).resolve() != (ROOT / path).resolve()
+            for key, path in (
+                ("base", base),
+                ("queries", queries),
+                ("groundtruth", truth),
+            )
+        )
+    ):
+        raise ValueError(f"unexpected ANN configuration: {dataset}/{name}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--datasets", nargs="+", choices=DATASETS, default=list(DATASETS)
+    )
+    parser.add_argument(
+        "--output-dir", type=Path, default=Path("results/benchmarks/ann")
+    )
+    parser.add_argument("--methods", nargs="+", choices=[case[0] for case in CASES])
+    parser.add_argument("--threads", type=int, default=48)
+    args = parser.parse_args()
+
+    failed = []
+    for dataset in args.datasets:
+        base, queries, truth, metric = DATASETS[dataset]
+        for name, method, extra in CASES:
+            if args.methods is not None and name not in args.methods:
+                continue
+            output = args.output_dir / dataset / f"{name}.json"
+            index_dir = args.output_dir / "indexes" / dataset / name
+            log = args.output_dir / "logs" / dataset / f"{name}.log"
+            if output.exists():
+                validate_record(json.loads(output.read_text()), dataset, name)
+                print(f"SKIP {dataset} {name}: result exists", flush=True)
+                continue
+            if index_dir.exists():
+                failed.append(f"{dataset}/{name}: incomplete index directory exists")
+                print(f"SKIP {failed[-1]}", flush=True)
+                continue
+            output.parent.mkdir(parents=True, exist_ok=True)
+            log.parent.mkdir(parents=True, exist_ok=True)
+            command = [
+                sys.executable,
+                str(RUNNER),
+                "--base",
+                base,
+                "--queries",
+                queries,
+                "--groundtruth",
+                truth,
+                "--metric",
+                metric,
+                "--method",
+                method,
+                "--nlist",
+                "4096",
+                "--nprobe",
+                "64",
+                "--m",
+                "16",
+                "--degree",
+                "32",
+                "--hnsw-clusters",
+                "16",
+                "--ef-construction",
+                "200",
+                "--ef-search",
+                "128",
+                "--k-factor",
+                "1",
+                "--threads",
+                str(args.threads),
+                "--socket",
+                "0",
+                "--k",
+                "10",
+                "--index-dir",
+                str(index_dir),
+                "--output",
+                str(output),
+                *extra,
+            ]
+            print(f"START {dataset} {name}", flush=True)
+            with log.open("w") as stream:
+                stream.write(" ".join(command) + "\n")
+                stream.flush()
+                result = subprocess.run(command, cwd=ROOT, stdout=stream, stderr=stream)
+            if result.returncode:
+                failed.append(f"{dataset}/{name}: exit {result.returncode}")
+                print(f"FAIL {failed[-1]} (see {log})", flush=True)
+            else:
+                print(f"DONE {dataset} {name}", flush=True)
+    if failed:
+        report = args.output_dir / "build_failures.json"
+        report.write_text(json.dumps(failed, indent=2) + "\n")
+        raise SystemExit(f"{len(failed)} index builds failed; see {report}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/download_datasets.py b/benchmarks/download_datasets.py
new file mode 100644
index 00000000..f8ddbfb3
--- /dev/null
+++ b/benchmarks/download_datasets.py
@@ -0,0 +1,244 @@
+"""Download and prepare GIST1M and OpenAI-1536."""
+
+import argparse
+import json
+import shutil
+import subprocess
+import tarfile
+import tempfile
+from pathlib import Path
+
+import h5py
+import numpy as np
+
+if __package__:
+    from .run import load_vectors
+else:
+    from run import load_vectors
+
+ROOT = Path(__file__).resolve().parents[1]
+GIST_URL = "ftp://ftp.irisa.fr/local/texmex/corpus/gist.tar.gz"
+OPENAI_ID = "1cwIY76n_HEbbZAANJVWiVkjBsIy3b9jB"
+GIST_FILES = {
+    "gist_base.fvecs": {
+        "bytes": 3844000000,
+    },
+    "gist_query.fvecs": {
+        "bytes": 3844000,
+    },
+    "gist_groundtruth.ivecs": {
+        "bytes": 404000,
+    },
+}
+
+
+def write_fvecs(stream, vectors):
+    rows = np.empty((len(vectors), vectors.shape[1] + 1), dtype="<i4")
+    rows[:, 0] = vectors.shape[1]
+    rows[:, 1:] = vectors.view("<i4")
+    rows.tofile(stream)
+
+
+def write_ivecs(stream, ids):
+    rows = np.empty((len(ids), ids.shape[1] + 1), dtype="<i4")
+    rows[:, 0] = ids.shape[1]
+    rows[:, 1:] = ids.astype("<i4", copy=False)
+    rows.tofile(stream)
+
+
+def exact_groundtruth(base_path, query_path, output_path, k, threads):
+    import faiss
+
+    base, _ = load_vectors(base_path)
+    queries, _ = load_vectors(query_path)
+    faiss.omp_set_num_threads(threads)
+    index = faiss.IndexFlatIP(base.shape[1])
+    index.add(base)
+    with output_path.open("xb") as stream:
+        for start in range(0, len(queries), 256):
+            ids = index.search(queries[start : start + 256], k)[1]
+            write_ivecs(stream, ids)
+
+
+def check_file(path, expected):
+    if not path.exists():
+        return False
+    if path.stat().st_size != expected["bytes"]:
+        raise ValueError(f"unexpected file size; refusing to overwrite {path}")
+    print(f"Checked {path}", flush=True)
+    return True
+
+
+def download(cache, name, size, *, url=None, drive_id=None):
+    cache.mkdir(parents=True, exist_ok=True)
+    path = cache / name
+    if path.exists():
+        if path.stat().st_size != size:
+            raise ValueError(f"unexpected download size: {path}")
+        return path
+    partial = path.with_name(path.name + ".part")
+    print(f"Downloading {name}", flush=True)
+    if drive_id:
+        import gdown
+
+        gdown.download(
+            id=drive_id,
+            output=str(partial),
+            resume=True,
+            use_cookies=False,
+            timeout=30,
+            retries=2,
+        )
+    else:
+        subprocess.run(
+            [
+                "curl",
+                "--fail",
+                "--location",
+                "--retry",
+                "2",
+                "--connect-timeout",
+                "30",
+                "--continue-at",
+                "-",
+                "--output",
+                str(partial),
+                url,
+            ],
+            check=True,
+        )
+    if not partial.is_file() or partial.stat().st_size != size:
+        raise ValueError(f"incomplete download: {partial}")
+    partial.replace(path)
+    return path
+
+
+def unpack_gist(archive, destination, expected):
+    # Extract only the three known regular files; never trust archive paths.
+    remaining = {f"gist/{name}": name for name in expected}
+    with tarfile.open(archive, "r|gz") as source:
+        for member in source:
+            if member.name not in remaining:
+                continue
+            name = remaining.pop(member.name)
+            metadata = expected[name]
+            if not member.isfile() or member.size != metadata["bytes"]:
+                raise ValueError(f"unexpected GIST archive member: {name}")
+            with (
+                source.extractfile(member) as src,
+                (destination / name).open("xb") as dst,
+            ):
+                shutil.copyfileobj(src, dst, 1024 * 1024)
+            check_file(destination / name, metadata)
+            if not remaining:
+                break
+    if remaining:
+        raise ValueError(f"missing GIST archive members: {sorted(remaining)}")
+
+
+def convert_openai(source, destination, files, base_count=999000, dimension=1536):
+    with h5py.File(source, "r") as data:
+        for key, name, count in (
+            ("train", "base.fvecs", base_count),
+            ("test", "queries.fvecs", 1000),
+        ):
+            if name not in files:
+                continue
+            if data[key].shape != (count, dimension):
+                raise ValueError(f"unexpected {key} shape in {source}")
+            with (destination / name).open("xb") as stream:
+                for start in range(0, count, 4096):
+                    # Match SuperKMeans: cast to float32 without renormalizing or shuffling.
+                    batch = np.asarray(data[key][start : start + 4096], dtype="<f4")
+                    if not np.isfinite(batch).all():
+                        raise ValueError(f"non-finite vectors in {source}")
+                    write_fvecs(stream, batch)
+            check_file(destination / name, files[name])
+
+
+def prepare(dataset, data_dir, cache, threads):
+    if dataset == "gist1m":
+        destination = data_dir / "gist"
+        files = GIST_FILES
+    else:
+        destination = data_dir / "dbpedia"
+        manifest = json.loads((ROOT / "benchmarks/DBPEDIA_MANIFEST.json").read_text())
+        files = dict(manifest["files"])
+    destination.mkdir(parents=True, exist_ok=True)
+    missing = {
+        name: metadata
+        for name, metadata in files.items()
+        if not check_file(destination / name, metadata)
+    }
+    if not missing:
+        return
+    # Validate all new outputs before publishing them alongside existing files.
+    with tempfile.TemporaryDirectory(prefix=".prepare-", dir=destination) as tmp:
+        staging = Path(tmp)
+        if dataset == "gist1m":
+            archive = download(cache, "gist.tar.gz", 2740172684, url=GIST_URL)
+            unpack_gist(archive, staging, missing)
+        else:
+            if any(name.endswith(".fvecs") for name in missing):
+                source = download(
+                    cache, "openai-1536-angular.hdf5", 12288006144, drive_id=OPENAI_ID
+                )
+                convert_openai(source, staging, missing)
+            if "queries.ivecs" in missing:
+                print(
+                    "Computing exact OpenAI inner-product top-100 ground truth",
+                    flush=True,
+                )
+                exact_groundtruth(
+                    (staging if "base.fvecs" in missing else destination)
+                    / "base.fvecs",
+                    (staging if "queries.fvecs" in missing else destination)
+                    / "queries.fvecs",
+                    staging / "queries.ivecs",
+                    100,
+                    threads,
+                )
+        for name in missing:
+            (staging / name).replace(destination / name)
+    output = {
+        "dataset": dataset,
+        "source": GIST_URL
+        if dataset == "gist1m"
+        else f"https://drive.google.com/file/d/{OPENAI_ID}/view",
+        "metric": "l2" if dataset == "gist1m" else "inner_product",
+        "groundtruth_k": 100,
+        "files": {
+            name: {
+                "bytes": (destination / name).stat().st_size,
+            }
+            for name in files
+        },
+    }
+    (destination / "download-manifest.json").write_text(
+        json.dumps(output, indent=2) + "\n"
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--datasets",
+        nargs="+",
+        choices=("gist1m", "dbpedia"),
+        default=["gist1m", "dbpedia"],
+    )
+    parser.add_argument("--data-dir", type=Path, default=ROOT / "data")
+    parser.add_argument(
+        "--cache-dir", type=Path, default=ROOT / "data/benchmark-downloads"
+    )
+    parser.add_argument("--threads", type=int, default=48)
+    args = parser.parse_args()
+    if args.threads <= 0:
+        parser.error("--threads must be positive")
+    for dataset in args.datasets:
+        prepare(dataset, args.data_dir, args.cache_dir, args.threads)
+    print("Datasets ready. K-means computes its separate L2 ground truth when needed.")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/env.sh b/benchmarks/env.sh
new file mode 100644
index 00000000..90becfda
--- /dev/null
+++ b/benchmarks/env.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+# Source this file after activating the Python environment used for installation.
+rabitq_benchmark_lib="$(python -c 'import sys; print(sys.prefix + "/lib")')" || return
+export MKL_THREADING_LAYER=GNU
+export LD_LIBRARY_PATH="${rabitq_benchmark_lib}${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}"
+unset rabitq_benchmark_lib
diff --git a/benchmarks/install_baselines.py b/benchmarks/install_baselines.py
new file mode 100644
index 00000000..ed6317f3
--- /dev/null
+++ b/benchmarks/install_baselines.py
@@ -0,0 +1,279 @@
+"""Install the benchmark libraries in the current Python 3.13 environment (Ubuntu x86-64)."""
+
+import argparse
+import json
+import os
+import platform
+import shlex
+import shutil
+import subprocess
+import sys
+import tarfile
+import tempfile
+import tomllib
+import urllib.request
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+PYTHON_PACKAGES = (
+    "cmake==3.28.3",
+    "ninja",
+    "numpy==2.5.2",
+    "scikit-build-core==1.0.3",
+    "pybind11==3.1.0",
+    "swig==4.5.0",
+    "packaging",
+    "matplotlib==3.11.2",
+    "gdown==6.4.1",
+    "h5py==3.16.0",
+)
+SOURCES = {
+    "faiss-1.15.0": (
+        "https://codeload.github.com/facebookresearch/faiss/tar.gz/refs/tags/v1.15.0"
+    ),
+    "superkmeans-0.2.0": (
+        "https://files.pythonhosted.org/packages/b4/0b/ed67af34f0ed106226538fd62c540400f6bb44fdbe5a4e1d6a9a603ace53/superkmeans-0.2.0.tar.gz"
+    ),
+}
+
+VERIFY = r"""
+import importlib
+import importlib.metadata
+import json
+from pathlib import Path
+import subprocess
+import sys
+
+expected = json.loads(sys.argv[1])
+installed = {}
+for package, module_name, library in (
+    ("rabitqlib", "rabitqlib._rabitqlib", None),
+    ("faiss-cpu", "faiss", "libfaiss.so"),
+    ("superkmeans", "superkmeans._superkmeans", None),
+):
+    version = importlib.metadata.version(package)
+    if version != expected[package]:
+        raise RuntimeError(f"unexpected {package} version: {version}")
+    module = importlib.import_module(module_name)
+    path = Path(module.__file__).resolve()
+    if not path.is_relative_to(Path(sys.prefix).resolve()):
+        raise RuntimeError(f"{package} imported outside the current environment: {path}")
+    binary = path.parent / library if library else path
+    linked = subprocess.check_output(["ldd", str(binary)], text=True)
+    if "not found" in linked or "libgomp.so" not in linked:
+        raise RuntimeError(f"missing dependency or GNU OpenMP for {binary}:\n{linked}")
+    if package != "rabitqlib" and "libmkl_rt.so" not in linked:
+        raise RuntimeError(f"{package} is not linked to MKL")
+    installed[package] = {"version": version, "path": str(binary), "ldd": linked}
+print(json.dumps(installed, indent=2))
+"""
+
+
+def preflight(compiler):
+    if sys.platform != "linux" or platform.machine() != "x86_64":
+        raise RuntimeError("this benchmark installer requires Linux x86-64")
+    if sys.version_info[:2] != (3, 13):
+        raise RuntimeError("use the benchmark's existing Python 3.13 environment")
+    if sys.prefix == sys.base_prefix and not (Path(sys.prefix) / "conda-meta").is_dir():
+        raise RuntimeError("run inside an existing virtualenv or Conda environment")
+    missing = [
+        tool
+        for tool in (compiler, "git", "numactl", "ldd", "curl")
+        if not shutil.which(tool)
+    ]
+    missing += [
+        str(path)
+        for path in (
+            Path("/usr/include/mkl/mkl.h"),
+            Path("/usr/lib/x86_64-linux-gnu/libmkl_rt.so"),
+            Path("/usr/lib/x86_64-linux-gnu/libmkl_gnu_thread.so"),
+        )
+        if not path.exists()
+    ]
+    if missing:
+        raise RuntimeError(
+            "Missing prerequisites: "
+            + ", ".join(missing)
+            + "\nOn Ubuntu, run:\n  sudo apt-get update\n"
+            "  sudo apt-get install build-essential g++-13 git numactl curl "
+            "libmkl-dev libmkl-rt libmkl-gnu-thread\n"
+            "See the benchmark installation guide for package-source requirements."
+        )
+    return shutil.which(compiler)
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--jobs", type=int, default=12)
+    parser.add_argument("--compiler", default="g++-13")
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="check prerequisites and print steps without installing",
+    )
+    args = parser.parse_args()
+    if args.jobs < 1:
+        parser.error("job count must be positive")
+    try:
+        compiler = preflight(args.compiler)
+    except RuntimeError as error:
+        parser.error(str(error))
+    work = ROOT / "build/benchmark-baselines"
+    if not args.dry_run:
+        for name in ("sources", "wheels", "logs"):
+            (work / name).mkdir(parents=True, exist_ok=True)
+    env = dict(os.environ)
+    env["PATH"] = str(Path(sys.executable).parent) + os.pathsep + env["PATH"]
+    env["CMAKE_BUILD_PARALLEL_LEVEL"] = str(args.jobs)
+    env["MKL_THREADING_LAYER"] = "GNU"
+    env["LD_LIBRARY_PATH"] = os.pathsep.join(
+        p for p in (str(Path(sys.prefix) / "lib"), env.get("LD_LIBRARY_PATH")) if p
+    )
+    env["PYTHONNOUSERSITE"] = "1"
+
+    def run(command, log=None):
+        print(shlex.join(map(str, command)), flush=True)
+        if args.dry_run:
+            return
+        if log is None:
+            subprocess.run(command, cwd=ROOT, env=env, check=True)
+        else:
+            print(f"Build log: {log}", flush=True)
+            with log.open("w") as stream:
+                try:
+                    subprocess.run(
+                        command,
+                        cwd=ROOT,
+                        env=env,
+                        stdout=stream,
+                        stderr=stream,
+                        check=True,
+                    )
+                except subprocess.CalledProcessError as error:
+                    raise RuntimeError(f"build failed; see {log}") from error
+
+    run([sys.executable, "-m", "pip", "install", *PYTHON_PACKAGES])
+    run(
+        [
+            sys.executable,
+            "-m",
+            "pip",
+            "install",
+            "-r",
+            str(ROOT / "docs/requirements.txt"),
+        ]
+    )
+    version = tomllib.loads((ROOT / "pyproject.toml").read_text())["project"]["version"]
+    rabitq_name = f"rabitqlib-{version}"
+    definitions = {
+        rabitq_name: {"RABITQ_ENABLE_NATIVE_OPTIMIZATION": "ON"},
+        "faiss-1.15.0": {
+            "FAISS_ENABLE_MKL": "ON",
+            "BLA_VENDOR": "Intel10_64_dyn",
+            "BLA_VENDOR_THREADING": "gnu",
+            "FAISS_USE_LTO": "OFF",
+        },
+        "superkmeans-0.2.0": {
+            "MKL_DIR": str(ROOT / "benchmarks/mkl"),
+            "SKMEANS_PORTABLE": "OFF",
+            "FETCHCONTENT_UPDATES_DISCONNECTED": "ON",
+            "SKMEANS_MARCH": "native",
+            # Match the published wheel's rotation backend; this sdist omits FindFFTW.cmake.
+            "SKMEANS_SKIP_FFTW": "ON",
+        },
+    }
+    records = []
+    wheels = []
+    for name in definitions:
+        if name == rabitq_name:
+            source = ROOT
+            provenance = {}
+        else:
+            url = SOURCES[name]
+            source = work / "sources" / name
+            provenance = {"url": url}
+            print(f"Source: {url}", flush=True)
+            if not args.dry_run:
+                archive = work / "sources" / f"{name}.tar.gz"
+                if not archive.exists():
+                    with urllib.request.urlopen(url, timeout=60) as response:
+                        data = response.read()
+                    archive.write_bytes(data)
+                if not source.exists():
+                    with tarfile.open(archive) as package:
+                        package.extractall(work / "sources", filter="data")
+        short_name = name.split("-")[0]
+        flags = {
+            "CMAKE_CXX_COMPILER": compiler,
+            "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
+            "CMAKE_BUILD_TYPE": "Release",
+            **definitions[name],
+        }
+        command = [
+            sys.executable,
+            "-m",
+            "pip",
+            "wheel",
+            str(source),
+            "--no-build-isolation",
+            "--no-deps",
+            f"-Cbuild-dir={source / 'build/benchmark-baselines' / (short_name + '-build')}",
+            *[f"-Ccmake.define.{key}={value}" for key, value in flags.items()],
+            "-v",
+        ]
+        log = work / "logs" / f"{short_name}-build.log"
+        if args.dry_run:
+            run([*command, "-w", str(work / "wheels" / name)], log)
+            wheels.append(str(work / "wheels" / f"<{name} wheel>"))
+            continue
+        # Select only this build's wheel, never an older cached version or Python ABI.
+        with tempfile.TemporaryDirectory(dir=work / "wheels", prefix=name + "-") as tmp:
+            run([*command, "-w", tmp], log)
+            built = list(Path(tmp).glob("*.whl"))
+            if len(built) != 1:
+                raise RuntimeError(f"expected one wheel from {name}, got {len(built)}")
+            wheel = work / "wheels" / built[0].name
+            shutil.copyfile(built[0], wheel)
+        wheels.append(str(wheel))
+        records.append(
+            {
+                "source": name,
+                **provenance,
+                "cmake": flags,
+                "compiler": subprocess.check_output([compiler, "--version"], text=True),
+                "wheel": str(wheel),
+            }
+        )
+    run(
+        [
+            sys.executable,
+            "-m",
+            "pip",
+            "install",
+            "--no-deps",
+            "--force-reinstall",
+            *wheels,
+        ]
+    )
+    if args.dry_run:
+        print(
+            "Then verify versions, environment paths, MKL/GNU OpenMP links, and write builds.json and installed.json."
+        )
+        return
+    (work / "builds.json").write_text(json.dumps(records, indent=2) + "\n")
+    expected = {"rabitqlib": version, "faiss-cpu": "1.15.0", "superkmeans": "0.2.0"}
+    verified = subprocess.check_output(
+        [sys.executable, "-c", VERIFY, json.dumps(expected)],
+        cwd=ROOT,
+        env=env,
+        text=True,
+    )
+    (work / "installed.json").write_text(verified)
+    activation = ROOT / "benchmarks/env.sh"
+    print(
+        f"Installed and verified all three libraries in {sys.prefix}.\nBefore running benchmarks: source {shlex.quote(str(activation))}"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/mkl/MKLConfig.cmake b/benchmarks/mkl/MKLConfig.cmake
new file mode 100644
index 00000000..e65b0028
--- /dev/null
+++ b/benchmarks/mkl/MKLConfig.cmake
@@ -0,0 +1,12 @@
+# MKL runtime package for local Linux/GCC Python baseline builds.
+# Set MKL_THREADING_LAYER=GNU at runtime to share the caller's OpenMP runtime.
+find_path(MKL_INCLUDE_DIR mkl.h PATH_SUFFIXES mkl REQUIRED)
+find_library(MKL_RUNTIME_LIBRARY NAMES mkl_rt REQUIRED)
+if(NOT TARGET MKL::MKL)
+    add_library(MKL::MKL INTERFACE IMPORTED)
+    set_target_properties(MKL::MKL PROPERTIES
+        INTERFACE_INCLUDE_DIRECTORIES "${MKL_INCLUDE_DIR}"
+        INTERFACE_LINK_LIBRARIES "${MKL_RUNTIME_LIBRARY}"
+    )
+endif()
+set(MKL_FOUND TRUE)
diff --git a/benchmarks/plot_results.py b/benchmarks/plot_results.py
new file mode 100644
index 00000000..c0300bbc
--- /dev/null
+++ b/benchmarks/plot_results.py
@@ -0,0 +1,219 @@
+"""Plot QPS-recall curves and export measurements from single-query-set sweeps."""
+
+import argparse
+import csv
+import json
+import math
+from itertools import product
+from pathlib import Path
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+
+if __package__:
+    from .build_indexes import CASES, DATASETS, LABELS, validate_record
+    from .query_index import settings
+else:
+    from build_indexes import CASES, DATASETS, LABELS, validate_record
+    from query_index import settings
+
+
+def frontier(points):
+    """Keep measured points not dominated in both recall and throughput."""
+    result = []
+    best_qps = -math.inf
+    for recall, qps in sorted(set(points), reverse=True):
+        if qps > best_qps:
+            result.append((recall, qps))
+            best_qps = qps
+    return list(reversed(result))
+
+
+def observations(record):
+    if record.get("status") != "single_query_set":
+        raise ValueError("expected a single_query_set sweep record")
+    for point in record["query_points"]:
+        yield (
+            point["setting"],
+            point["recall"],
+            record["query_count"] / point["one_query_service_s"],
+        )
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--input-dir", type=Path, required=True)
+    parser.add_argument(
+        "--output-dir", type=Path, default=Path("docs/docs/benchmarks/figures")
+    )
+    parser.add_argument(
+        "--datasets", nargs="+", choices=DATASETS, default=list(DATASETS)
+    )
+    parser.add_argument(
+        "--methods",
+        nargs="+",
+        choices=[case[0] for case in CASES],
+        default=[case[0] for case in CASES],
+    )
+    args = parser.parse_args()
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    measurements = {}
+    csv_path = args.output_dir / "qps-recall-points.csv"
+    if csv_path.exists():
+        with csv_path.open(newline="") as previous:
+            for row in csv.DictReader(previous):
+                measurements.setdefault((row["dataset"], row["method"]), []).append(
+                    (
+                        json.loads(row["setting"]),
+                        float(row["recall_at_10"]),
+                        float(row["qps"]),
+                    )
+                )
+    for dataset, name in product(args.datasets, args.methods):
+        initial = json.loads((args.input_dir / dataset / f"{name}.json").read_text())
+        validate_record(initial, dataset, name)
+        record = json.loads(
+            (args.input_dir / "sweeps" / dataset / f"{name}.json").read_text()
+        )
+        if (
+            record["dataset"] != dataset
+            or record["query_count"] != initial["config"]["query_count"]
+            or [p["setting"] for p in record["query_points"]]
+            != settings(initial["config"])
+            or any(
+                record["thread_environment"].get(key) != "1"
+                for key in (
+                    "OMP_NUM_THREADS",
+                    "MKL_NUM_THREADS",
+                    "OPENBLAS_NUM_THREADS",
+                )
+            )
+            or record["cpu_affinity"]
+            != initial["build"]["cpu_placement"]["allowed_cpus"]
+        ):
+            raise ValueError(f"sweep does not match its index build: {dataset}/{name}")
+        measurements[dataset, name] = list(observations(record))
+    colors = list(plt.get_cmap("tab10").colors)
+    markers = ("o", "s", "^", "D", "v", "P")
+    rows = []
+    plt.rcParams.update({"font.size": 10, "svg.fonttype": "none"})
+
+    for dataset, family in product(args.datasets, ("ivf", "graphs")):
+        if not any(
+            name in args.methods and ("ivf" if "ivf" in method else "graphs") == family
+            for name, method, _ in CASES
+        ):
+            continue
+        fig, ax = plt.subplots(figsize=(12, 9))
+        fig.subplots_adjust(top=0.85, bottom=0.32)
+        title = {"gist1m": "GIST1M", "dbpedia": "DBpedia"}[dataset]
+        family_title = "IVF indexes" if family == "ivf" else "Graph indexes"
+        fig.suptitle(
+            f"{title} · {family_title}  |  Recall@10 vs QPS", fontsize=20, y=0.975
+        )
+        fig.text(
+            0.5,
+            0.92,
+            "One Intel Xeon Gold 6418H CPU · one query per call",
+            ha="center",
+            color="#444444",
+        )
+        handles = []
+        recalls = []
+        for ordinal, ((name, method, _), label) in enumerate(
+            (case, label)
+            for case, label in zip(CASES, LABELS, strict=True)
+            if ("ivf" if "ivf" in case[1] else "graphs") == family
+        ):
+            if (dataset, name) not in measurements:
+                continue
+            points = []
+            for setting, recall, qps in measurements[dataset, name]:
+                if not (0 <= recall <= 1 and math.isfinite(qps) and qps > 0):
+                    raise ValueError(f"invalid measurement: {dataset}/{name}")
+                points.append((recall, qps))
+            recalls.extend(recall for recall, _ in points)
+            color = colors[ordinal]
+            marker = markers[ordinal % len(markers)]
+            style = "-" if name.startswith("rabitq-") else "--"
+            handles.append(
+                Line2D([], [], color=color, marker=marker, linestyle=style, label=label)
+            )
+            if points:
+                x, y = zip(*points)
+                ax.scatter(x, y, color=color, marker=marker, s=22, alpha=0.45)
+                x, y = zip(*frontier(points))
+                ax.plot(
+                    x,
+                    y,
+                    color=color,
+                    marker=marker,
+                    markersize=4,
+                    linewidth=1.4,
+                    linestyle=style,
+                )
+        ax.set_title(
+            "Query set · full sweep · 1 search thread · one timing pass", fontsize=11
+        )
+        ax.set_xlabel("Recall@10 (measured)")
+        ax.set_ylabel("Queries / second (log scale)")
+        recall_min = max(0, math.floor((min(recalls) - 0.005) * 20) / 20)
+        ax.set_xlim(recall_min, 1.005)
+        ax.set_yscale("log")
+        ax.grid(True, which="major", alpha=0.2)
+        ax.spines[["top", "right"]].set_visible(False)
+        fig.legend(
+            handles=handles,
+            loc="lower center",
+            bbox_to_anchor=(0.5, 0.09),
+            ncol=2,
+            frameon=False,
+            fontsize=9,
+        )
+        fig.text(
+            0.5,
+            0.046,
+            "Faint markers: all measured settings. Lines: per-method Pareto frontier; no fitted or extrapolated points.",
+            ha="center",
+            fontsize=10,
+            color="#444444",
+        )
+        path = args.output_dir / f"{family}-{dataset}-qps-recall.svg"
+        fig.savefig(path)
+        path.write_text(
+            "\n".join(line.rstrip() for line in path.read_text().splitlines()) + "\n"
+        )
+        plt.close(fig)
+        print(f"Wrote {path}")
+    for dataset in DATASETS:
+        for name, _, _ in CASES:
+            for setting, recall, qps in measurements.get((dataset, name), []):
+                rows.append(
+                    (
+                        dataset,
+                        name,
+                        json.dumps(setting, sort_keys=True),
+                        recall,
+                        qps,
+                    )
+                )
+    with csv_path.open("w", newline="") as stream:
+        writer = csv.writer(stream, lineterminator="\n")
+        writer.writerow(
+            (
+                "dataset",
+                "method",
+                "setting",
+                "recall_at_10",
+                "qps",
+            )
+        )
+        writer.writerows(rows)
+    print(f"Exported {len(rows)} measurements")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/query_index.py b/benchmarks/query_index.py
new file mode 100644
index 00000000..5b936f46
--- /dev/null
+++ b/benchmarks/query_index.py
@@ -0,0 +1,145 @@
+"""Sweep a retained index with one search thread on one fixed query set."""
+
+import argparse
+import json
+import os
+import time
+from pathlib import Path
+
+import numpy as np
+
+if __package__:
+    from .build_indexes import DATASETS
+    from .run import (
+        load_index,
+        load_vectors,
+        read_matrix,
+        recall_at_k,
+        search,
+        validate_results,
+    )
+else:
+    from build_indexes import DATASETS
+    from run import (
+        load_index,
+        load_vectors,
+        read_matrix,
+        recall_at_k,
+        search,
+        validate_results,
+    )
+
+
+def query_set(dataset, k):
+    _, query_path, truth_path, _ = DATASETS[dataset]
+    queries, _ = load_vectors(query_path)
+    truth, _ = read_matrix(truth_path)
+    if len(queries) != len(truth) or truth.shape[1] < k:
+        raise ValueError("query and ground-truth shapes differ")
+    return queries, truth[:, :k]
+
+
+def settings(cfg):
+    method = cfg["method"]
+    k = cfg["k"]
+    if method in ("rabitq-ivf", "faiss-ivf-rabitq-fs", "faiss-ivf-pqfs"):
+        probes = (1, 2, 4, 8, 16, 32, 64, 128, 256, 512)
+        factors = (
+            (1, 2, 5, 10, 20)
+            if cfg["bits"] == 1 or method == "faiss-ivf-pqfs"
+            else (1,)
+        )
+        if method == "rabitq-ivf":
+            factors = (1,)
+        return [
+            {"nprobe": probe, "k_factor": factor}
+            for probe in probes
+            for factor in factors
+        ]
+    return [{"ef_search": ef} for ef in (16, 32, 64, 128, 256, 512, 1024) if ef >= k]
+
+
+def configure(index, cfg, setting):
+    selected = cfg | setting
+    method = cfg["method"]
+    if method.startswith("faiss-ivf-"):
+        import faiss
+
+        faiss.ParameterSpace().set_index_parameter(index, "nprobe", selected["nprobe"])
+        if hasattr(index, "k_factor"):
+            index.k_factor = selected["k_factor"]
+    elif method.startswith("faiss-hnsw-"):
+        index.hnsw.efSearch = selected["ef_search"]
+    return selected
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("initial_record", type=Path)
+    parser.add_argument("--dataset", choices=DATASETS, required=True)
+    parser.add_argument("--output", type=Path, required=True)
+    args = parser.parse_args()
+    if args.output.exists():
+        parser.error(f"output already exists: {args.output}")
+    initial = json.loads(args.initial_record.read_text())
+    cfg = initial["config"]
+    if cfg["socket"] != 0 or os.sched_getaffinity(0) != set(
+        initial["search"]["cpu_placement"]["allowed_cpus"]
+    ):
+        parser.error("run the sweep with numactl --cpunodebind=0 --membind=0")
+    if Path(cfg["base"]).resolve() != Path(DATASETS[args.dataset][0]).resolve():
+        parser.error(
+            "index uses a different dataset; rebuild for the current query protocol"
+        )
+    k = cfg["k"]
+    queries, truth = query_set(args.dataset, k)
+    if len(queries) != cfg["query_count"]:
+        parser.error("query count differs from the index build record")
+    index = load_index(cfg, Path(cfg["index_dir"]) / "index.bin")
+    points = []
+    for setting in settings(cfg):
+        selected = configure(index, cfg, setting)
+        for query in queries[:20]:
+            search(index, selected, query.reshape(1, -1), 1)
+        results = []
+        started = time.perf_counter()
+        for query in queries:
+            results.append(search(index, selected, query.reshape(1, -1), 1))
+        service_s = time.perf_counter() - started
+        ids = np.concatenate([result[0] for result in results])
+        distances = np.concatenate([result[1] for result in results])
+        validate_results(ids, distances, cfg["base_count"], k)
+        recall = recall_at_k(ids, truth)
+        points.append(
+            {
+                "setting": setting,
+                "recall": recall,
+                "one_query_service_s": service_s,
+            }
+        )
+        print(f"SWEEP {setting} recall={recall:.4f}", flush=True)
+
+    result = {
+        "status": "single_query_set",
+        "dataset": args.dataset,
+        "initial_record": str(args.initial_record),
+        "query_count": len(queries),
+        "query_points": points,
+        "recall_mode": "one_query_per_call",
+        "cpu_affinity": sorted(os.sched_getaffinity(0)),
+        "thread_environment": {
+            name: os.environ.get(name)
+            for name in (
+                "OMP_NUM_THREADS",
+                "MKL_NUM_THREADS",
+                "OPENBLAS_NUM_THREADS",
+                "MKL_THREADING_LAYER",
+            )
+        },
+    }
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(json.dumps(result, indent=2) + "\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/reproduce.py b/benchmarks/reproduce.py
new file mode 100644
index 00000000..e97ca23c
--- /dev/null
+++ b/benchmarks/reproduce.py
@@ -0,0 +1,192 @@
+"""Reproduce all benchmarks or selected datasets, families, methods, and stages."""
+
+import argparse
+import os
+import shlex
+import subprocess
+import sys
+from pathlib import Path
+
+if __package__:
+    from .build_indexes import CASES, DATASETS, ROOT
+    from .run_kmeans import METHODS as KMEANS_METHODS
+else:
+    from build_indexes import CASES, DATASETS, ROOT
+    from run_kmeans import METHODS as KMEANS_METHODS
+
+
+def main():
+    families = {
+        name: "ivf" if "ivf" in method else "graphs" for name, method, _ in CASES
+    }
+    families.update(dict.fromkeys(KMEANS_METHODS, "kmeans"))
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--datasets",
+        nargs="+",
+        choices=DATASETS,
+        default=list(DATASETS),
+        help="default: GIST1M and DBpedia for all families",
+    )
+    parser.add_argument(
+        "--benchmarks",
+        nargs="+",
+        choices=("ivf", "graphs", "kmeans"),
+        default=["ivf", "graphs", "kmeans"],
+    )
+    parser.add_argument(
+        "--methods",
+        nargs="+",
+        choices=families,
+        help="configuration names; omit to run all methods in the selected families",
+    )
+    parser.add_argument(
+        "--stage",
+        choices=("all", "build", "query", "export"),
+        default="all",
+        help="build includes k-means fitting; query reuses saved ANN indexes",
+    )
+    parser.add_argument("--output-dir", type=Path, default=ROOT / "results/benchmarks")
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="print commands without running or writing results",
+    )
+    args = parser.parse_args()
+    selected = [
+        name
+        for name, family in families.items()
+        if family in args.benchmarks and (args.methods is None or name in args.methods)
+    ]
+    if args.methods is not None and set(args.methods) - set(selected):
+        parser.error("--methods includes configurations outside --benchmarks")
+    ann = [name for name in selected if families[name] != "kmeans"]
+    kmeans = [name for name in selected if families[name] == "kmeans"]
+    if args.stage == "query" and not ann:
+        parser.error("query stage requires an IVF or graph method")
+    if args.stage == "query":
+        kmeans = []
+    ann_datasets = args.datasets if ann else []
+    kmeans_datasets = args.datasets if kmeans else []
+    output = args.output_dir.resolve()
+    env = dict(
+        os.environ,
+        MKL_THREADING_LAYER="GNU",
+        OMP_NUM_THREADS="48",
+        MKL_NUM_THREADS="48",
+        OPENBLAS_NUM_THREADS="48",
+    )
+    env["LD_LIBRARY_PATH"] = os.pathsep.join(
+        p for p in (str(Path(sys.prefix) / "lib"), env.get("LD_LIBRARY_PATH")) if p
+    )
+    placement = ["numactl", "--cpunodebind=0", "--membind=0"]
+    commands = []
+    if args.stage != "export":
+        commands.append(
+            [
+                *placement,
+                sys.executable,
+                str(ROOT / "benchmarks/download_datasets.py"),
+                "--datasets",
+                *args.datasets,
+            ]
+        )
+    ann_selection = ["--datasets", *ann_datasets, "--methods", *ann]
+    kmeans_selection = ["--datasets", *kmeans_datasets, "--methods", *kmeans]
+    if args.stage in ("all", "build"):
+        if ann:
+            commands.append(
+                [
+                    *placement,
+                    sys.executable,
+                    str(ROOT / "benchmarks/build_indexes.py"),
+                    "--output-dir",
+                    str(output / "ann"),
+                    "--threads",
+                    "48",
+                    *ann_selection,
+                ]
+            )
+        if kmeans:
+            commands.append(
+                [
+                    *placement,
+                    sys.executable,
+                    str(ROOT / "benchmarks/run_kmeans.py"),
+                    "--output-dir",
+                    str(output / "kmeans"),
+                    "--no-publish",
+                    *kmeans_selection,
+                ]
+            )
+    if args.stage in ("all", "query") and ann:
+        commands.append(
+            [
+                *placement,
+                sys.executable,
+                str(ROOT / "benchmarks/run_sweeps.py"),
+                "--output-dir",
+                str(output / "ann"),
+                *ann_selection,
+            ]
+        )
+    if args.stage in ("all", "export"):
+        if ann:
+            commands.append(
+                [
+                    sys.executable,
+                    str(ROOT / "benchmarks/update_indexing.py"),
+                    "--indexing-dir",
+                    str(output / "ann"),
+                    *ann_selection,
+                ]
+            )
+            commands.append(
+                [
+                    sys.executable,
+                    str(ROOT / "benchmarks/plot_results.py"),
+                    "--input-dir",
+                    str(output / "ann"),
+                    *ann_selection,
+                ]
+            )
+        if kmeans:
+            commands.append(
+                [
+                    sys.executable,
+                    str(ROOT / "benchmarks/run_kmeans.py"),
+                    "--output-dir",
+                    str(output / "kmeans"),
+                    "--export-only",
+                    *kmeans_selection,
+                ]
+            )
+        commands.append(
+            [
+                sys.executable,
+                "-m",
+                "mkdocs",
+                "build",
+                "--strict",
+                "--config-file",
+                str(ROOT / "docs/mkdocs.yml"),
+            ]
+        )
+    if not args.dry_run and args.stage == "export":
+        required = [
+            output / folder / dataset / f"{name}.json"
+            for name in ann + kmeans
+            for dataset in (kmeans_datasets if name in kmeans else ann_datasets)
+            for folder in (("kmeans",) if name in kmeans else ("ann", "ann/sweeps"))
+        ]
+        missing = [str(path) for path in required if not path.is_file()]
+        if missing:
+            parser.error("selected results are missing: " + ", ".join(missing))
+    for command in commands:
+        print(shlex.join(command), flush=True)
+        if not args.dry_run:
+            subprocess.run(command, cwd=ROOT, env=env, check=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/run.py b/benchmarks/run.py
new file mode 100644
index 00000000..de98f467
--- /dev/null
+++ b/benchmarks/run.py
@@ -0,0 +1,539 @@
+"""Build and check one CPU ANN configuration in isolated build/search processes."""
+
+import argparse
+import importlib
+import importlib.metadata
+import json
+import os
+import platform
+import shlex
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+from pathlib import Path
+
+import numpy as np
+
+METHODS = (
+    "rabitq-ivf",
+    "rabitq-hnsw",
+    "rabitq-symqg",
+    "faiss-ivf-rabitq-fs",
+    "faiss-ivf-pqfs",
+    "faiss-hnsw-flat",
+    "faiss-hnsw-sq4",
+    "faiss-hnsw-sq8",
+)
+
+
+def import_search_library(method):
+    if method.startswith("faiss-"):
+        importlib.import_module("faiss")
+    elif method.startswith("rabitq-"):
+        importlib.import_module("rabitqlib")
+    else:
+        raise ValueError(f"unsupported benchmark method: {method}")
+
+
+def read_matrix(path, limit=None):
+    """Read a row-major fvecs/ivecs matrix, checking every selected row header."""
+    path = Path(path)
+    first = np.fromfile(path, dtype="<i4", count=1)
+    if len(first) != 1 or first[0] <= 0:
+        raise ValueError(f"invalid vector dimension in {path}")
+    dim = int(first[0])
+    row_bytes = (dim + 1) * 4
+    size = path.stat().st_size
+    if size % row_bytes:
+        raise ValueError(f"incomplete vector row in {path}")
+    rows = size // row_bytes
+    count = rows if limit is None else min(rows, limit)
+    if count == 0:
+        raise ValueError(f"no vectors in {path}")
+    matrix = np.memmap(path, dtype="<i4", mode="r", shape=(rows, dim + 1))
+    if not np.all(matrix[:count, 0] == dim):
+        raise ValueError(f"inconsistent vector dimensions in {path}")
+    return np.ascontiguousarray(matrix[:count, 1:]).copy(), rows
+
+
+def load_vectors(path, limit=None):
+    values, rows = read_matrix(path, limit)
+    return values.view("<f4"), rows
+
+
+def exact_neighbors(base, queries, k, metric, threads):
+    import faiss
+
+    faiss.omp_set_num_threads(threads)
+    index = (
+        faiss.IndexFlatL2(base.shape[1])
+        if metric == "l2"
+        else faiss.IndexFlatIP(base.shape[1])
+    )
+    index.add(base)
+    return index.search(queries, k)[1]
+
+
+def build_index(cfg, base, path):
+    method = cfg["method"]
+    n, dim = base.shape
+    started = time.perf_counter()
+    training_s = 0.0
+    if method.startswith("faiss-"):
+        import faiss
+
+        faiss.omp_set_num_threads(cfg["threads"])
+        if method == "faiss-ivf-rabitq-fs":
+            spec = f"HR{dim},IVF{cfg['nlist']},RaBitQfs{cfg['bits']}"
+            if cfg["bits"] == 1:
+                spec += ",RFlat"
+        elif method == "faiss-ivf-pqfs":
+            pq = f"PQ{dim // 2}x4fs"
+            spec = f"IVF{cfg['nlist']},{pq},RFlat"
+            if cfg["opq"]:
+                spec = f"OPQ{dim // 2},{spec}"
+        else:
+            suffix = {
+                "faiss-hnsw-flat": "",
+                "faiss-hnsw-sq4": "_SQ4",
+                "faiss-hnsw-sq8": "_SQ8",
+            }[method]
+            spec = f"HNSW{cfg['m']}{suffix}"
+        metric = (
+            faiss.METRIC_L2 if cfg["metric"] == "l2" else faiss.METRIC_INNER_PRODUCT
+        )
+        index = faiss.index_factory(dim, spec, metric)
+        if method.startswith("faiss-hnsw-"):
+            index.hnsw.efConstruction = cfg["ef_construction"]
+        stage_start = time.perf_counter()
+        index.train(base)
+        training_s = time.perf_counter() - stage_start
+        stage_start = time.perf_counter()
+        index.add(base)
+        indexing_s = time.perf_counter() - stage_start
+        if hasattr(index, "k_factor"):
+            index.k_factor = cfg["k_factor"]
+        details = {"faiss_factory": spec}
+    elif method in ("rabitq-ivf", "rabitq-hnsw"):
+        from rabitqlib import FinalAssignmentMode, IvfIndex, RaBitQKMeans
+
+        cluster_count = cfg["nlist"] if method == "rabitq-ivf" else cfg["hnsw_clusters"]
+        kmeans = RaBitQKMeans(
+            dim,
+            cluster_count,
+            niter=10,
+            num_threads=cfg["threads"],
+            spherical=cfg["metric"] == "ip",
+            seed=42,
+            min_points_per_centroid=1,
+            final_assignment=FinalAssignmentMode.Exact,
+        )
+        stage_start = time.perf_counter()
+        kmeans.train(base)
+        training_s = time.perf_counter() - stage_start
+        stage_start = time.perf_counter()
+        if method == "rabitq-ivf":
+            index = IvfIndex(dim, n, cluster_count, cfg["bits"], cfg["metric"])
+        else:
+            from rabitqlib import HnswIndex
+
+            index = HnswIndex(
+                dim,
+                n,
+                cfg["m"],
+                cfg["ef_construction"],
+                cfg["bits"],
+                cfg["metric"],
+                random_seed=42,
+            )
+        index.build(
+            base,
+            kmeans.centroids,
+            kmeans.assignments,
+            num_threads=cfg["threads"],
+        )
+        indexing_s = time.perf_counter() - stage_start
+        details = {
+            "clustering": "RaBitQKMeans",
+            "clustering_seed": 42,
+            "cluster_count": cluster_count,
+            "final_assignment": "exact",
+        }
+    elif method == "rabitq-symqg":
+        from rabitqlib import SymqgIndex
+
+        index = SymqgIndex(dim, cfg["degree"], cfg["metric"], cfg["quantization_bits"])
+        stage_start = time.perf_counter()
+        index.build(
+            base,
+            ef_construction=cfg["ef_construction"],
+            num_threads=cfg["threads"],
+            init="pipnn",
+        )
+        indexing_s = time.perf_counter() - stage_start
+        details = {"initialization": "pipnn", "refinement_iterations": 1}
+    else:
+        raise ValueError(f"unsupported benchmark method: {method}")
+    save_start = time.perf_counter()
+    if method.startswith("faiss-"):
+        faiss.write_index(index, str(path))
+    else:
+        index.save(str(path))
+    finished = time.perf_counter()
+    return {
+        "training_s": training_s,
+        "indexing_s": indexing_s,
+        "build_s": save_start - started,
+        "save_s": finished - save_start,
+        "build_and_save_s": finished - started,
+        **details,
+    }
+
+
+def load_index(cfg, path):
+    method = cfg["method"]
+    if method.startswith("faiss-"):
+        import faiss
+
+        index = faiss.read_index(str(path))
+        if method.startswith("faiss-ivf-"):
+            faiss.ParameterSpace().set_index_parameter(index, "nprobe", cfg["nprobe"])
+        else:
+            index.hnsw.efSearch = cfg["ef_search"]
+        if hasattr(index, "k_factor"):
+            index.k_factor = cfg["k_factor"]
+        return index
+    if method == "rabitq-ivf":
+        from rabitqlib import IvfIndex
+
+        return IvfIndex.load(str(path))
+    if method == "rabitq-hnsw":
+        from rabitqlib import HnswIndex
+
+        return HnswIndex.load(str(path))
+    if method == "rabitq-symqg":
+        from rabitqlib import SymqgIndex
+
+        return SymqgIndex.load(str(path))
+    raise ValueError(f"unsupported benchmark method: {method}")
+
+
+def search(index, cfg, queries, threads):
+    method = cfg["method"]
+    k = cfg["k"]
+    if method.startswith("faiss-"):
+        import faiss
+
+        faiss.omp_set_num_threads(threads)
+        if method in ("faiss-ivf-rabitq-fs", "faiss-ivf-pqfs") and len(queries) > 100:
+            batches = [
+                index.search(queries[start : start + 100], k)
+                for start in range(0, len(queries), 100)
+            ]
+            distances = np.concatenate([batch[0] for batch in batches])
+            ids = np.concatenate([batch[1] for batch in batches])
+        else:
+            distances, ids = index.search(queries, k)
+        return ids, distances
+    if method == "rabitq-ivf":
+        return index.search(queries, k=k, nprobe=cfg["nprobe"], num_threads=threads)
+    if method in ("rabitq-hnsw", "rabitq-symqg"):
+        return index.search(queries, k=k, ef=cfg["ef_search"], num_threads=threads)
+    raise ValueError(f"unsupported benchmark method: {method}")
+
+
+def validate_results(ids, distances, count, k):
+    if ids.ndim != 2 or ids.shape[1] != k or distances.shape != ids.shape:
+        raise ValueError("index returned an incorrect result shape")
+    if np.any(ids < 0) or np.any(ids >= count):
+        raise ValueError("index returned an invalid point ID")
+    if any(len(set(row)) != k for row in ids.tolist()):
+        raise ValueError("index returned duplicate point IDs")
+    if not np.all(np.isfinite(distances)):
+        raise ValueError("index returned a nonfinite distance")
+
+
+def recall_at_k(ids, groundtruth):
+    if (
+        ids.ndim != 2
+        or groundtruth.ndim != 2
+        or len(ids) != len(groundtruth)
+        or groundtruth.shape[1] < ids.shape[1]
+    ):
+        raise ValueError("result and ground-truth shapes differ")
+    k = ids.shape[1]
+    return float(
+        np.mean(
+            [len(set(row) & set(truth[:k])) / k for row, truth in zip(ids, groundtruth)]
+        )
+    )
+
+
+def cpu_placement(socket):
+    if socket is None:
+        return None
+    allowed = sorted(os.sched_getaffinity(0))
+    sockets = sorted(
+        {
+            int(
+                Path(
+                    f"/sys/devices/system/cpu/cpu{cpu}/topology/physical_package_id"
+                ).read_text()
+            )
+            for cpu in allowed
+        }
+    )
+    if sockets != [socket]:
+        raise RuntimeError(
+            f"expected CPU socket {socket}, but process affinity spans sockets {sockets}"
+        )
+    return {"socket": socket, "allowed_cpus": allowed}
+
+
+def worker(stage, directory):
+    directory = Path(directory)
+    cfg = json.loads((directory / "config.json").read_text())
+    placement = cpu_placement(cfg["socket"])
+    if placement is not None and cfg["threads"] > len(placement["allowed_cpus"]):
+        raise RuntimeError("thread count exceeds CPUs available on the selected socket")
+    index_directory = (
+        Path(cfg["index_dir"]) if cfg["index_dir"] else directory / "index"
+    )
+    path = index_directory / "index.bin"
+    if stage == "_build":
+        base = np.load(directory / "base.npy", mmap_mode="r")
+        import_search_library(cfg["method"])
+        details = build_index(cfg, base, path)
+        details["cpu_placement"] = placement
+        (directory / "build.json").write_text(json.dumps(details))
+        return
+    queries = np.load(directory / "queries.npy")
+    groundtruth = np.load(directory / "groundtruth.npy")
+    import_search_library(cfg["method"])
+    load_start = time.perf_counter()
+    index = load_index(cfg, path)
+    load_s = time.perf_counter() - load_start
+    search(index, cfg, queries[: min(len(queries), 20)], 1)
+    ids, distances = search(index, cfg, queries, 1)
+    validate_results(ids, distances, cfg["base_count"], cfg["k"])
+    recall = recall_at_k(ids, groundtruth)
+    (directory / "search.json").write_text(
+        json.dumps(
+            {
+                "recall_at_k": recall,
+                "load_s": load_s,
+                "cpu_placement": placement,
+            }
+        )
+    )
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--base", required=True, help="base vectors in fvecs format")
+    parser.add_argument(
+        "--queries", required=True, help="query vectors in fvecs format"
+    )
+    parser.add_argument(
+        "--groundtruth", help="ivecs file; only valid with the full base"
+    )
+    parser.add_argument("--method", required=True, choices=METHODS)
+    parser.add_argument("--metric", choices=("l2", "ip"), default="l2")
+    parser.add_argument("--base-limit", type=int)
+    parser.add_argument("--query-limit", type=int)
+    parser.add_argument("--nlist", type=int, default=4096)
+    parser.add_argument("--nprobe", type=int, default=64)
+    parser.add_argument("--bits", type=int, default=5)
+    parser.add_argument("--quantization-bits", type=int, default=0)
+    parser.add_argument("--hnsw-clusters", type=int, default=16)
+    parser.add_argument("--degree", type=int, default=32)
+    parser.add_argument("--k-factor", type=int, default=1)
+    parser.add_argument("--opq", action="store_true")
+    parser.add_argument("--m", type=int, default=16)
+    parser.add_argument("--ef-construction", type=int, default=200)
+    parser.add_argument("--ef-search", type=int, default=128)
+    parser.add_argument("--threads", type=int, default=48)
+    parser.add_argument(
+        "--socket",
+        type=int,
+        help="bind build and search CPUs and memory to this Linux NUMA socket",
+    )
+    parser.add_argument("--k", type=int, default=10)
+    parser.add_argument(
+        "--index-dir",
+        type=Path,
+        help="persist the built index here for later search-parameter sweeps",
+    )
+    parser.add_argument("--output", required=True, type=Path)
+    args = parser.parse_args()
+    for name in (
+        "nlist",
+        "nprobe",
+        "bits",
+        "hnsw_clusters",
+        "degree",
+        "k_factor",
+        "m",
+        "ef_construction",
+        "ef_search",
+        "threads",
+        "k",
+    ):
+        if getattr(args, name) <= 0:
+            parser.error(f"--{name.replace('_', '-')} must be positive")
+    if args.socket is not None:
+        if args.socket < 0:
+            parser.error("--socket must be nonnegative")
+        if shutil.which("numactl") is None:
+            parser.error("--socket requires numactl")
+    for name in ("base_limit", "query_limit"):
+        if getattr(args, name) is not None and getattr(args, name) <= 0:
+            parser.error(f"--{name.replace('_', '-')} must be positive")
+    if (
+        args.method
+        in (
+            "rabitq-hnsw",
+            "rabitq-symqg",
+            "faiss-hnsw-flat",
+            "faiss-hnsw-sq4",
+            "faiss-hnsw-sq8",
+        )
+        and args.ef_search < args.k
+    ):
+        parser.error("--ef-search must be at least k for graph indexes")
+    if args.nprobe > args.nlist:
+        parser.error("--nprobe must be at most --nlist")
+    if args.method == "rabitq-ivf" and args.bits not in (*range(1, 10), 32):
+        parser.error("RaBitQ-Library IVF bits must be 1 through 9 or 32")
+    if args.method == "rabitq-hnsw" and args.bits not in range(1, 10):
+        parser.error("HNSW-RaBitQ bits must be 1 through 9")
+    if args.method == "rabitq-symqg" and args.quantization_bits not in (0, 4, 8):
+        parser.error("SymphonyQG quantization bits must be 0, 4, or 8")
+    if args.method == "faiss-ivf-rabitq-fs" and args.bits not in range(1, 10):
+        parser.error("Faiss RaBitQ FastScan bits must be 1 through 9")
+    if args.output.exists():
+        parser.error(f"output already exists: {args.output}")
+    if args.index_dir is not None and args.index_dir.exists():
+        parser.error(f"index directory already exists: {args.index_dir}")
+    base, total_base = load_vectors(args.base, args.base_limit)
+    queries, _ = load_vectors(args.queries, args.query_limit)
+    if base.shape[1] != queries.shape[1]:
+        parser.error("base/query dimensions differ")
+    if args.k > len(base):
+        parser.error("k exceeds the base vector count")
+    if args.nlist >= len(base):
+        parser.error("nlist must be smaller than the base vector count")
+    if args.method == "rabitq-ivf" and args.metric == "ip":
+        norms = np.linalg.norm(base, axis=1)
+        if not np.allclose(norms, 1, atol=1e-4):
+            parser.error(
+                "RaBitQKMeans spherical training requires normalized base vectors"
+            )
+    if args.groundtruth:
+        if len(base) != total_base:
+            parser.error("supplied ground truth is invalid for a truncated base")
+        gt, _ = read_matrix(args.groundtruth, len(queries))
+        if (
+            gt.shape[0] != len(queries)
+            or gt.shape[1] < args.k
+            or np.any(gt[:, : args.k] < 0)
+            or np.any(gt[:, : args.k] >= len(base))
+        ):
+            parser.error("ground truth shape or IDs are incompatible")
+        gt = gt[:, : args.k].copy()
+        gt_source = str(args.groundtruth)
+    else:
+        gt = exact_neighbors(base, queries, args.k, args.metric, args.threads)
+        gt_source = "Faiss IndexFlat on selected base and queries"
+    cfg = vars(args).copy()
+    cfg["output"] = str(args.output)
+    cfg["index_dir"] = str(args.index_dir.resolve()) if args.index_dir else None
+    cfg["base_count"] = len(base)
+    cfg["query_count"] = len(queries)
+    with tempfile.TemporaryDirectory(prefix="rabitq-benchmark-") as tmp:
+        directory = Path(tmp)
+        index_directory = (
+            Path(cfg["index_dir"]) if cfg["index_dir"] else directory / "index"
+        )
+        index_directory.mkdir(parents=True)
+        np.save(directory / "base.npy", base)
+        np.save(directory / "queries.npy", queries)
+        np.save(directory / "groundtruth.npy", gt)
+        (directory / "config.json").write_text(json.dumps(cfg))
+        for stage in ("_build", "_search"):
+            command = [
+                sys.executable,
+                str(Path(__file__).resolve()),
+                stage,
+                str(directory),
+            ]
+            if args.socket is not None:
+                command = [
+                    "numactl",
+                    f"--cpunodebind={args.socket}",
+                    f"--membind={args.socket}",
+                    *command,
+                ]
+            subprocess.run(
+                command,
+                check=True,
+            )
+        build = json.loads((directory / "build.json").read_text())
+        measured = json.loads((directory / "search.json").read_text())
+        artifact_bytes = sum(
+            p.stat().st_size for p in index_directory.rglob("*") if p.is_file()
+        )
+    packages = {
+        name: importlib.metadata.version(name) for name in ("rabitqlib", "faiss-cpu")
+    }
+    cpuinfo = Path("/proc/cpuinfo")
+    model_lines = (
+        [
+            line
+            for line in cpuinfo.read_text().splitlines()
+            if line.startswith("model name")
+        ]
+        if cpuinfo.exists()
+        else []
+    )
+    cpu_model = (
+        model_lines[0].split(":", 1)[1].strip() if model_lines else platform.processor()
+    )
+    record = {
+        "schema_version": 3,
+        "status": "built",
+        "packages": packages,
+        "hardware": {
+            "machine": platform.machine(),
+            "cpu": cpu_model,
+            "os": platform.platform(),
+        },
+        "config": cfg,
+        "data": {
+            "groundtruth_source": gt_source,
+            "dimension": base.shape[1],
+            "total_base_rows": total_base,
+        },
+        "build": build,
+        "search": measured,
+        "serialized_index_bytes": artifact_bytes,
+        "reproduction_command": shlex.join(
+            [sys.executable, str(Path(__file__).resolve()), *sys.argv[1:]]
+        ),
+    }
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    with args.output.open("x") as out:
+        json.dump(record, out, indent=2)
+        out.write("\n")
+    print(
+        f"{args.method}: recall@{args.k}={measured['recall_at_k']:.4f}; {args.output}"
+    )
+
+
+if __name__ == "__main__":
+    if len(sys.argv) == 3 and sys.argv[1] in ("_build", "_search"):
+        worker(sys.argv[1], sys.argv[2])
+    else:
+        main()
diff --git a/benchmarks/run_kmeans.py b/benchmarks/run_kmeans.py
new file mode 100644
index 00000000..f956f7f6
--- /dev/null
+++ b/benchmarks/run_kmeans.py
@@ -0,0 +1,564 @@
+"""Compare flat float32 k-means with a 25-iteration limit and normal early stopping."""
+
+import argparse
+import csv
+import importlib.metadata
+import io
+import json
+import math
+import os
+import platform
+import shutil
+import subprocess
+import sys
+import tempfile
+import time
+from datetime import datetime, timezone
+from pathlib import Path
+
+import numpy as np
+
+if __package__:
+    from .build_indexes import DATASET_SHAPES
+    from .build_indexes import DATASETS as ANN_DATASETS
+    from .run import (
+        cpu_placement,
+        exact_neighbors,
+        load_vectors,
+        read_matrix,
+    )
+else:
+    from build_indexes import DATASET_SHAPES
+    from build_indexes import DATASETS as ANN_DATASETS
+    from run import (
+        cpu_placement,
+        exact_neighbors,
+        load_vectors,
+        read_matrix,
+    )
+
+ROOT = Path(__file__).resolve().parents[1]
+METHODS = {
+    "qgkmeans": "QGKMeans",
+    "faiss": "Faiss K-means",
+    "superkmeans": "SuperKMeans",
+}
+DATASETS = {
+    # Base path, query path, base count, dimensions, clusters.
+    name: (*paths[:2], *DATASET_SHAPES[name], 4096)
+    for name, paths in ANN_DATASETS.items()
+}
+
+
+def write_json(path, value):
+    temporary = path.with_suffix(path.suffix + ".tmp")
+    temporary.write_text(json.dumps(value, indent=2, allow_nan=False) + "\n")
+    temporary.replace(path)
+
+
+def load_base(cfg):
+    return np.memmap(
+        cfg["base_file"], mode="r", dtype="<f4", shape=(cfg["n"], cfg["d"])
+    )
+
+
+def train_python(method, x, k, threads, seed):
+    if method == "qgkmeans":
+        from rabitqlib import FinalAssignmentMode, QGKMeans
+
+        start = time.perf_counter()
+        model = QGKMeans(
+            x.shape[1],
+            k,
+            niter=25,
+            num_threads=threads,
+            seed=seed,
+            spherical=False,
+            quantization_bits=0,
+            graph_degree=32,
+            ef_build=240,
+            ef_search=16,
+            graph_build_iterations=1,
+            early_stop_threshold=0.0,
+            final_assignment=FinalAssignmentMode.Exact,
+        )
+        model.train(x)
+        total = time.perf_counter() - start
+        return (
+            model.centroids,
+            model.assignments,
+            {
+                "total_s": total,
+                "training_s": None,
+                "assignment_s": None,
+                "actual_iterations": len(model.iteration_stats),
+                "timing_boundary": "train includes exact final assignment",
+            },
+        )
+    if method == "superkmeans":
+        from superkmeans import SuperKMeans
+
+        start = time.perf_counter()
+        model = SuperKMeans(
+            k,
+            x.shape[1],
+            hierarchical=False,
+            quantizer="f32",
+            iters=25,
+            sampling_fraction=1.0,
+            n_threads=threads,
+            seed=seed,
+            use_blas_only=False,
+            early_termination=True,
+            tol=1e-4,
+            sample_queries=False,
+            angular=False,
+        )
+        centroids = model.train(x)
+        trained = time.perf_counter()
+        labels = model.assign(x, centroids)
+        assigned = time.perf_counter()
+        return (
+            centroids,
+            labels,
+            {
+                "total_s": assigned - start,
+                "training_s": trained - start,
+                "assignment_s": assigned - trained,
+                "actual_iterations": len(model.iteration_stats),
+                "timing_boundary": "train plus exhaustive final assignment",
+            },
+        )
+    if method != "faiss":
+        raise ValueError(f"unknown method: {method}")
+    import faiss
+
+    faiss.omp_set_num_threads(threads)
+    start = time.perf_counter()
+    model = faiss.Kmeans(
+        x.shape[1],
+        k,
+        niter=25,
+        nredo=1,
+        seed=seed,
+        spherical=False,
+        max_points_per_centroid=len(x),
+        early_stop_threshold=0.0,
+        gpu=False,
+    )
+    model.train(x)
+    trained = time.perf_counter()
+    # The Kmeans index contains the final centroids and performs exhaustive L2 search.
+    labels = model.index.search(x, 1)[1][:, 0]
+    assigned = time.perf_counter()
+    return (
+        model.centroids,
+        labels,
+        {
+            "total_s": assigned - start,
+            "training_s": trained - start,
+            "assignment_s": assigned - trained,
+            "actual_iterations": len(model.iteration_stats),
+            "timing_boundary": "train plus exhaustive final assignment",
+        },
+    )
+
+
+def quality_metrics(base, centroids, labels, queries, truth, threads):
+    """Final WCSS and SuperKMeans-style cluster coverage of exact top-10 neighbors."""
+    import faiss
+
+    k, d = centroids.shape
+    nprobe = max(1, int(0.01 * k))
+    if labels.shape != (len(base),) or np.any(labels < 0) or np.any(labels >= k):
+        raise ValueError("invalid final assignments")
+    if not np.isfinite(centroids).all():
+        raise ValueError("nonfinite centroids")
+    if (
+        truth.shape != (len(queries), 10)
+        or np.any(truth < 0)
+        or np.any(truth >= len(base))
+    ):
+        raise ValueError("ground truth must contain ten valid neighbors per query")
+    wcss = 0.0
+    for start in range(0, len(base), 8192):
+        stop = start + 8192
+        residual = base[start:stop].astype(np.float64) - centroids[
+            labels[start:stop]
+        ].astype(np.float64)
+        wcss += float(np.einsum("ij,ij->", residual, residual))
+    # Independent direct-float64 audit, outside timing, including evenly spaced vectors.
+    max_gap = 0.0
+    c64 = centroids.astype(np.float64)
+    for i in np.linspace(0, len(base) - 1, min(128, len(base)), dtype=int):
+        residual = c64 - base[i].astype(np.float64)
+        distances = np.einsum("ij,ij->i", residual, residual)
+        best = float(distances.min())
+        gap = float(distances[labels[i]]) - best
+        max_gap = max(max_gap, gap)
+        if gap > 1e-5 * max(1.0, best):
+            raise ValueError(f"final assignment audit failed at vector {i}: gap={gap}")
+    faiss.omp_set_num_threads(threads)
+    index = faiss.IndexFlatL2(d)
+    index.add(centroids)
+    hits = 0
+    for start in range(0, len(queries), 256):
+        distances, ids = index.search(queries[start : start + 256], k)
+        # Sort all centroid distances to make equal-distance cutoff ties deterministic.
+        order = np.lexsort((ids, distances), axis=1)[:, :nprobe]
+        probes = np.take_along_axis(ids, order, axis=1)
+        neighbor_labels = labels[truth[start : start + len(probes)]]
+        hits += int(
+            (neighbor_labels[:, :, None] == probes[:, None, :]).any(axis=2).sum()
+        )
+    return {
+        "wcss": wcss,
+        "recall_at_10": hits / (10 * len(queries)),
+        "nprobe": nprobe,
+        "assignment_audit_max_l2_gap": max_gap,
+    }
+
+
+def worker(stage, config_path):
+    cfg = json.loads(config_path.read_text())
+    cpu_placement(cfg["socket"])
+    x = load_base(cfg)
+    folder = Path(cfg["work_dir"])
+    if stage == "_prepare":
+        queries = np.load(cfg["query_file"])
+        truth = exact_neighbors(x, queries, 10, "l2", cfg["threads"])
+        np.save(cfg["truth_file"], truth)
+    elif stage == "_train":
+        centroids, labels, timing = train_python(
+            cfg["method"], x, cfg["k"], cfg["threads"], cfg["seed"]
+        )
+        centroids.astype("<f4").tofile(folder / "centroids.f32")
+        labels.astype("<u4").tofile(folder / "assignments.u32")
+        write_json(folder / "timing.json", timing)
+    elif stage == "_evaluate":
+        centroids = np.fromfile(folder / "centroids.f32", dtype="<f4").reshape(
+            cfg["k"], cfg["d"]
+        )
+        labels = np.fromfile(folder / "assignments.u32", dtype="<u4")
+        quality = quality_metrics(
+            x,
+            centroids,
+            labels,
+            np.load(cfg["query_file"]),
+            np.load(cfg["truth_file"]),
+            cfg["threads"],
+        )
+        write_json(folder / "quality.json", quality)
+    else:
+        raise ValueError(f"unknown stage: {stage}")
+
+
+def publish(output, docs=ROOT / "docs/docs/benchmarks", datasets=None, methods=None):
+    datasets = list(DATASETS) if datasets is None else datasets
+    methods = list(METHODS) if methods is None else methods
+    path = docs / "kmeans.md"
+    text = path.read_text()
+    csv_path = docs / "figures/kmeans.csv"
+    rows = {}
+    if csv_path.exists():
+        with csv_path.open(newline="") as previous:
+            rows = {
+                (row["dataset"], row["method"]): row for row in csv.DictReader(previous)
+            }
+    for dataset in datasets:
+        for method in methods:
+            result = output / dataset / f"{method}.json"
+            record = json.loads(result.read_text())
+            cfg = record["config"]
+            n, d, clusters = DATASETS[dataset][2:]
+            if (
+                cfg["dataset"] != dataset
+                or cfg["method"] != method
+                or cfg["metric"] != "l2"
+                or cfg["seed"] != 42
+                or cfg["socket"] != 0
+                or cfg["k"] != clusters
+                or cfg["threads"] != 48
+                or cfg["niter"] != 25
+                or cfg["nq"] != 1000
+                or cfg["n"] != n
+                or cfg["d"] != d
+                or record["quality"]["nprobe"] != int(0.01 * clusters)
+            ):
+                raise ValueError(f"unexpected benchmark configuration: {result}")
+            timing, quality = record["timing"], record["quality"]
+            if (
+                not 1 <= timing["actual_iterations"] <= 25
+                or not math.isfinite(timing["total_s"])
+                or timing["total_s"] <= 0
+                or not math.isfinite(quality["wcss"])
+                or quality["wcss"] < 0
+                or not 0 <= quality["recall_at_10"] <= 1
+            ):
+                raise ValueError(f"invalid k-means measurements: {result}")
+            rows[dataset, method] = {
+                "dataset": dataset,
+                "method": method,
+                "clusters": clusters,
+                "threads": 48,
+                "iteration_limit": 25,
+                **timing,
+                **quality,
+            }
+    for dataset in datasets:
+        nprobe = int(0.01 * DATASETS[dataset][4])
+        table = [
+            f"| Method | Total time (s) | WCSS | Recall@10 ({nprobe} probes) | Iterations |",
+            "| --- | ---: | ---: | ---: | ---: |",
+        ]
+        for method, label in METHODS.items():
+            row = rows.get((dataset, method))
+            if row is None:
+                table.append(f"| {label} | Pending | — | — | — |")
+            else:
+                table.append(
+                    f"| {label} | {float(row['total_s']):.2f} | {float(row['wcss']):.9g} | {float(row['recall_at_10']):.6f} | {row['actual_iterations']} |"
+                )
+        text = replace_block(text, f"kmeans:{dataset}", "\n".join(table))
+    path.write_text(text)
+    fields = [
+        "dataset",
+        "method",
+        "clusters",
+        "threads",
+        "iteration_limit",
+        "actual_iterations",
+        "total_s",
+        "training_s",
+        "assignment_s",
+        "wcss",
+        "recall_at_10",
+        "nprobe",
+    ]
+    stream = io.StringIO(newline="")
+    writer = csv.DictWriter(
+        stream, fieldnames=fields, extrasaction="ignore", lineterminator="\n"
+    )
+    writer.writeheader()
+    for dataset in DATASETS:
+        for method in METHODS:
+            if (dataset, method) in rows:
+                writer.writerow(
+                    {**rows[dataset, method], "clusters": DATASETS[dataset][4]}
+                )
+    csv_path.write_text(stream.getvalue())
+
+
+def replace_block(text, name, body):
+    start, end = f"<!-- {name}:start -->", f"<!-- {name}:end -->"
+    if text.count(start) != 1 or text.count(end) != 1:
+        raise ValueError(f"missing or repeated result marker: {name}")
+    before, rest = text.split(start)
+    _, after = rest.split(end)
+    return before + start + "\n" + body + "\n" + end + after
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--smoke", action="store_true")
+    parser.add_argument("--output-dir", type=Path)
+    parser.add_argument("--methods", nargs="+", choices=METHODS, default=list(METHODS))
+    parser.add_argument(
+        "--export-only",
+        action="store_true",
+        help="export saved results without running experiments",
+    )
+    parser.add_argument(
+        "--no-publish", action="store_true", help="save results without updating docs"
+    )
+    parser.add_argument(
+        "--datasets", nargs="+", choices=DATASETS, default=list(DATASETS)
+    )
+    args = parser.parse_args()
+    output = (
+        args.output_dir
+        or Path(
+            "results/benchmarks-smoke/kmeans"
+            if args.smoke
+            else "results/benchmarks/kmeans"
+        )
+    ).resolve()
+    if args.export_only:
+        if args.smoke:
+            parser.error("smoke results cannot be published")
+        publish(output, datasets=args.datasets, methods=args.methods)
+        return
+    output.mkdir(parents=True, exist_ok=True)
+    placement = cpu_placement(None if args.smoke else 0)
+    if not args.smoke:
+        cores = {
+            Path(f"/sys/devices/system/cpu/cpu{c}/topology/core_id").read_text().strip()
+            for c in placement["allowed_cpus"]
+        }
+        if len(cores) != 24 or len(placement["allowed_cpus"]) != 48:
+            raise RuntimeError(
+                "benchmark requires 24 physical cores and 48 logical CPUs"
+            )
+    threads = 2 if args.smoke else 48
+    env = {
+        **os.environ,
+        "OMP_NUM_THREADS": str(threads),
+        "OPENBLAS_NUM_THREADS": str(threads),
+        "MKL_NUM_THREADS": str(threads),
+        "MKL_THREADING_LAYER": "GNU",
+        # Let MKL resolve the same GNU OpenMP library as the Python environment.
+        "LD_LIBRARY_PATH": os.pathsep.join(
+            p
+            for p in (str(Path(sys.prefix) / "lib"), os.environ.get("LD_LIBRARY_PATH"))
+            if p
+        ),
+    }
+    metadata = {
+        "rabitqlib_version": importlib.metadata.version("rabitqlib"),
+        "faiss_version": importlib.metadata.version("faiss-cpu"),
+        "superkmeans_version": importlib.metadata.version("superkmeans"),
+        "cpu_placement": placement,
+        "platform": platform.platform(),
+    }
+    failures = []
+    for dataset in ("smoke",) if args.smoke else args.datasets:
+        pending = [
+            m for m in args.methods if not (output / dataset / f"{m}.json").exists()
+        ]
+        if not pending:
+            continue
+        print(f"PREPARE {dataset}", flush=True)
+        with tempfile.TemporaryDirectory(prefix="rabitq-kmeans-") as tmp:
+            staging = Path(tmp)
+            if args.smoke:
+                rng = np.random.default_rng(42)
+                base = rng.standard_normal((2048, 192)).astype("<f4")
+                queries = rng.standard_normal((20, 192)).astype("<f4")
+            else:
+                base, _ = load_vectors(DATASETS[dataset][0])
+                queries, _ = load_vectors(DATASETS[dataset][1])
+                n, d, _ = DATASETS[dataset][2:]
+                if base.shape != (n, d) or queries.shape != (1000, d):
+                    raise ValueError(f"unexpected dataset shape: {dataset}")
+            cfg = {
+                "dataset": dataset,
+                "n": len(base),
+                "d": base.shape[1],
+                "nq": len(queries),
+                "k": 288 if args.smoke else DATASETS[dataset][4],
+                "niter": 25,
+                "seed": 42,
+                "threads": threads,
+                "socket": None if args.smoke else 0,
+                "metric": "l2",
+                "base_file": str(staging / "base.f32"),
+                "query_file": str(staging / "queries.npy"),
+                "truth_file": str(staging / "truth.npy"),
+                "work_dir": str(staging),
+            }
+            base.tofile(cfg["base_file"])
+            np.save(cfg["query_file"], queries)
+            del base, queries
+            write_json(staging / "config.json", cfg)
+            if dataset == "gist1m":
+                truth, _ = read_matrix("data/gist/gist_groundtruth.ivecs")
+                np.save(cfg["truth_file"], truth[:, :10].astype(np.int64))
+            else:
+                subprocess.run(
+                    [
+                        sys.executable,
+                        str(Path(__file__).resolve()),
+                        "_prepare",
+                        str(staging / "config.json"),
+                    ],
+                    env=env,
+                    check=True,
+                )
+            for method in pending:
+                work = staging / method
+                work.mkdir()
+                cfg.update(method=method, work_dir=str(work))
+                write_json(work / "config.json", cfg)
+                result = output / dataset / f"{method}.json"
+                result.parent.mkdir(parents=True, exist_ok=True)
+                log = output / "logs" / dataset / f"{method}.log"
+                log.parent.mkdir(parents=True, exist_ok=True)
+                print(
+                    f"START {dataset}/{method}: k={cfg['k']}, niter=25, threads={threads}",
+                    flush=True,
+                )
+                started = datetime.now(timezone.utc).isoformat()
+                try:
+                    with log.open("w") as stream:
+                        command = [
+                            sys.executable,
+                            str(Path(__file__).resolve()),
+                            "_train",
+                            str(work / "config.json"),
+                        ]
+                        subprocess.run(
+                            command, env=env, stdout=stream, stderr=stream, check=True
+                        )
+                        timing = json.loads((work / "timing.json").read_text())
+                        if not 1 <= timing["actual_iterations"] <= 25:
+                            raise ValueError("unexpected training iteration count")
+                        subprocess.run(
+                            [
+                                sys.executable,
+                                str(Path(__file__).resolve()),
+                                "_evaluate",
+                                str(work / "config.json"),
+                            ],
+                            env=env,
+                            stdout=stream,
+                            stderr=stream,
+                            check=True,
+                        )
+                    quality = json.loads((work / "quality.json").read_text())
+                    # Retain compact outputs for later checks; the staged input is temporary.
+                    for name in ("centroids.f32", "assignments.u32"):
+                        shutil.copyfile(work / name, result.with_suffix("." + name))
+                    write_json(
+                        result,
+                        {
+                            "config": cfg.copy(),
+                            "metadata": metadata,
+                            "started_at_utc": started,
+                            "completed_at_utc": datetime.now(timezone.utc).isoformat(),
+                            "timing": timing,
+                            "quality": quality,
+                        },
+                    )
+                    print(
+                        f"DONE {dataset}/{method}: {timing['total_s']:.2f}s, WCSS={quality['wcss']:.9g}, recall={quality['recall_at_10']:.6f}, iterations={timing['actual_iterations']}",
+                        flush=True,
+                    )
+                except (subprocess.CalledProcessError, ValueError) as error:
+                    failures.append(f"{dataset}/{method}: {error}; see {log}")
+                    print(f"FAIL {failures[-1]}", flush=True)
+    write_json(output / "failures.json", failures)
+    if failures:
+        raise SystemExit(f"{len(failures)} failed measurements")
+    if not args.smoke and not args.no_publish:
+        publish(output, datasets=args.datasets, methods=args.methods)
+        subprocess.run(
+            [
+                sys.executable,
+                "-m",
+                "mkdocs",
+                "build",
+                "--strict",
+                "--config-file",
+                "docs/mkdocs.yml",
+            ],
+            cwd=ROOT,
+            check=True,
+        )
+    print("All requested k-means measurements completed.", flush=True)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) == 3 and sys.argv[1].startswith("_"):
+        worker(sys.argv[1], Path(sys.argv[2]))
+    else:
+        main()
diff --git a/benchmarks/run_sweeps.py b/benchmarks/run_sweeps.py
new file mode 100644
index 00000000..1b76fcc0
--- /dev/null
+++ b/benchmarks/run_sweeps.py
@@ -0,0 +1,77 @@
+"""Sweep search parameters on one fixed query set per dataset."""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+from build_indexes import CASES, DATASETS, ROOT
+
+SWEEPER = ROOT / "benchmarks" / "query_index.py"
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--datasets", nargs="+", choices=DATASETS, default=list(DATASETS)
+    )
+    parser.add_argument(
+        "--output-dir", type=Path, default=Path("results/benchmarks/ann")
+    )
+    parser.add_argument("--methods", nargs="+", choices=[case[0] for case in CASES])
+    args = parser.parse_args()
+    # One native search thread, including BLAS transforms.
+    env = dict(
+        os.environ, OMP_NUM_THREADS="1", MKL_NUM_THREADS="1", OPENBLAS_NUM_THREADS="1"
+    )
+    failed = []
+    for dataset in args.datasets:
+        for name, _, _ in CASES:
+            if args.methods is not None and name not in args.methods:
+                continue
+            initial = args.output_dir / dataset / f"{name}.json"
+            output = args.output_dir / "sweeps" / dataset / f"{name}.json"
+            log = args.output_dir / "logs" / dataset / f"{name}-sweep.log"
+            if output.exists():
+                print(f"SKIP {dataset} {name}: sweep exists", flush=True)
+                continue
+            if not initial.exists():
+                failed.append(f"{dataset}/{name}: initial record missing")
+                print(f"SKIP {failed[-1]}", flush=True)
+                continue
+            output.parent.mkdir(parents=True, exist_ok=True)
+            log.parent.mkdir(parents=True, exist_ok=True)
+            command = [
+                "numactl",
+                "--cpunodebind=0",
+                "--membind=0",
+                sys.executable,
+                str(SWEEPER),
+                str(initial),
+                "--dataset",
+                dataset,
+                "--output",
+                str(output),
+            ]
+            print(f"START SWEEP {dataset} {name}", flush=True)
+            with log.open("w") as stream:
+                stream.write(" ".join(command) + "\n")
+                stream.flush()
+                result = subprocess.run(
+                    command, cwd=ROOT, env=env, stdout=stream, stderr=stream
+                )
+            if result.returncode:
+                failed.append(f"{dataset}/{name}: exit {result.returncode}")
+                print(f"FAIL {failed[-1]} (see {log})", flush=True)
+            else:
+                print(f"DONE SWEEP {dataset} {name}", flush=True)
+    report = args.output_dir / "sweep_failures.json"
+    report.write_text(json.dumps(failed, indent=2) + "\n")
+    if failed:
+        raise SystemExit(f"{len(failed)} sweeps failed; see {report}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/update_indexing.py b/benchmarks/update_indexing.py
new file mode 100644
index 00000000..7ad228a0
--- /dev/null
+++ b/benchmarks/update_indexing.py
@@ -0,0 +1,116 @@
+"""Export indexing tables and CSV from completed 48-thread builds."""
+
+import argparse
+import csv
+import io
+import json
+import math
+from pathlib import Path
+
+if __package__:
+    from .build_indexes import CASES, DATASETS, LABELS, validate_record
+else:
+    from build_indexes import CASES, DATASETS, LABELS, validate_record
+
+FIELDS = (
+    "dataset",
+    "family",
+    "method",
+    "training_s",
+    "indexing_s",
+    "build_s",
+    "save_s",
+    "serialized_index_bytes",
+    "construction_threads",
+)
+
+
+def replace_block(text, name, body):
+    start = f"<!-- {name}:start -->"
+    end = f"<!-- {name}:end -->"
+    if text.count(start) != 1 or text.count(end) != 1:
+        raise ValueError(f"expected one generated block: {name}")
+    before, rest = text.split(start)
+    _, after = rest.split(end)
+    return before + start + "\n" + body + "\n" + end + after
+
+
+def update(indexing_dir, docs_dir, datasets=None, methods=None):
+    datasets = list(DATASETS) if datasets is None else datasets
+    cases = [case for case in CASES if methods is None or case[0] in methods]
+    rows = {}
+    csv_path = docs_dir / "figures/indexing.csv"
+    if csv_path.exists():
+        with csv_path.open(newline="") as previous:
+            rows = {
+                (row["dataset"], row["method"]): tuple(row[key] for key in FIELDS)
+                for row in csv.DictReader(previous)
+            }
+    for dataset in datasets:
+        for name, method, _ in cases:
+            path = indexing_dir / dataset / f"{name}.json"
+            record = json.loads(path.read_text())
+            validate_record(record, dataset, name)
+            build = record["build"]
+            values = [build[key] for key in FIELDS[3:7]]
+            values.append(record["serialized_index_bytes"])
+            if not all(math.isfinite(value) and value >= 0 for value in values):
+                raise ValueError(f"invalid indexing measurements: {path}")
+            family = "ivf" if "ivf" in method else "graphs"
+            rows[dataset, name] = (dataset, family, name, *values, 48)
+
+    pages = {}
+    for family in ("ivf", "graphs"):
+        if not any(
+            ("ivf" if "ivf" in method else "graphs") == family for _, method, _ in cases
+        ):
+            continue
+        path = docs_dir / f"{family}.md"
+        text = path.read_text()
+        for dataset in datasets:
+            table = [
+                "| Method | Train s | Construct s | Total s | Save s | Index GiB |",
+                "| --- | ---: | ---: | ---: | ---: | ---: |",
+            ]
+            for (name, method, _), label in zip(CASES, LABELS, strict=True):
+                if ("ivf" if "ivf" in method else "graphs") != family:
+                    continue
+                row = rows.get((dataset, name))
+                if row is None:
+                    cells = ["Pending", *["—"] * 4]
+                else:
+                    values = [
+                        *(float(value) for value in row[3:7]),
+                        float(row[7]) / 2**30,
+                    ]
+                    cells = [f"{value:.2f}" for value in values]
+                table.append("| " + label + " | " + " | ".join(cells) + " |")
+            text = replace_block(text, f"indexing:{dataset}", "\n".join(table))
+        pages[path] = text
+
+    stream = io.StringIO(newline="")
+    writer = csv.writer(stream, lineterminator="\n")
+    writer.writerow(FIELDS)
+    writer.writerows(
+        rows[key] for key in ((d, c[0]) for d in DATASETS for c in CASES) if key in rows
+    )
+    pages[csv_path] = stream.getvalue()
+    for path, text in pages.items():
+        temporary = path.with_suffix(path.suffix + ".tmp")
+        temporary.write_text(text)
+        temporary.replace(path)
+    print(f"Updated {len(cases) * len(datasets)} indexing results.")
+
+
+def main():
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--indexing-dir", type=Path, required=True)
+    parser.add_argument("--docs-dir", type=Path, default=Path("docs/docs/benchmarks"))
+    parser.add_argument("--datasets", nargs="+", choices=DATASETS)
+    parser.add_argument("--methods", nargs="+", choices=[case[0] for case in CASES])
+    args = parser.parse_args()
+    update(args.indexing_dir, args.docs_dir, args.datasets, args.methods)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/docs/docs/benchmarks/bench.md b/docs/docs/benchmarks/bench.md
new file mode 100644
index 00000000..d04adfd7
--- /dev/null
+++ b/docs/docs/benchmarks/bench.md
@@ -0,0 +1,136 @@
+# Benchmark overview
+
+Compare RaBitQ-Library, Faiss, and SuperKMeans on one Intel Xeon Gold 6418H:
+[IVF indexes](ivf.md), [graph indexes](graphs.md), and [k-means](kmeans.md).
+
+## Datasets and ground truth
+
+| Dataset | Base vectors | Dimensions | ANN metric | Queries |
+| --- | ---: | ---: | --- | ---: |
+| GIST1M | 1,000,000 | 960 | L2 | 1,000 |
+| DBpedia / OpenAI-1536 | 999,000 | 1,536 | Inner product | 1,000 |
+
+GIST1M uses the standard TexMex split and supplied L2 ground truth. OpenAI-1536
+uses [SuperKMeans' dataset](https://github.com/cwida/SuperKMeans/blob/main/BENCHMARKING.md),
+preserving its split, row order, and normalized float32 values. ANN ground truth
+for OpenAI is computed by exhaustive inner-product search.
+
+All methods share one fixed query set per dataset. Queries are used only for
+evaluation. [K-means](kmeans.md) uses L2 for both datasets and computes separate
+L2 ground truth for OpenAI.
+
+## Hardware and software
+
+| Setting | Value |
+| --- | --- |
+| CPU | One Intel Xeon Gold 6418H, 24 cores, 48 hardware threads |
+| Construction / clustering | 48 threads |
+| Query throughput | 1 process, 1 search thread, 1 query per call |
+| Platform | Ubuntu 22.04.5 LTS, Linux 6.8.0-84-generic, x86-64 |
+| Python / NumPy | 3.13.15 / 2.5.2 |
+| Construction and clustering compiler / CMake | GCC 13.1.0 / 3.28.3, Release |
+| RaBitQ-Library | 0.5.2 |
+| Baselines | Faiss 1.15.0, SuperKMeans 0.2.0; Intel MKL 2020.4, GNU OpenMP |
+
+## Package installation
+
+Use a Python 3.13 environment on Ubuntu x86-64. Ubuntu 22.04 needs a package
+source providing `g++-13` and the `multiverse` component for MKL.
+
+```bash
+sudo apt-get update
+sudo apt-get install build-essential g++-13 git numactl curl \
+  libmkl-dev libmkl-rt libmkl-gnu-thread
+
+python benchmarks/install_baselines.py --jobs 12
+source benchmarks/env.sh
+```
+
+The installer builds RaBitQ from the checkout, Faiss 1.15.0, and SuperKMeans 0.2.0
+in the active environment. Both baselines link to MKL with GNU OpenMP. Build logs
+and verification records are saved in `build/benchmark-baselines/`.
+Use `--dry-run` to preview installation commands.
+
+## Construction and search settings
+
+IVF uses 4,096 clusters. HNSW uses `M=16` and `efConstruction=200`;
+SymphonyQG uses degree 32. Family pages list all method settings.
+
+| Family | Search grid |
+| --- | --- |
+| IVF | `nprobe=1,2,4,8,16,32,64,128,256,512` |
+| Faiss IVF with RFlat | Above × `k_factor=1,2,5,10,20` |
+| Graph | `ef=16,32,64,128,256,512,1024` |
+
+Each setting searches all 1,000 queries at `k=10`, after warm-up. QPS is queries
+divided by elapsed search time, including Python call overhead and excluding
+index loading. Each setting has one timing pass.
+
+## Reading the figures
+
+Recall@10 is the mean fraction of exact top-10 neighbors returned. Faint markers
+show all measured settings; lines connect each method's Pareto frontier. QPS uses
+a logarithmic axis. The CSV contains every measured point.
+
+## Reading the indexing tables
+
+| Column | Meaning |
+| --- | --- |
+| Train | Training or centroid clustering time |
+| Construct | Encoding and index construction after training |
+| Total | Construction including setup and training, excluding saving |
+| Save | Serialization time |
+| Index GiB | Serialized files, including sidecars and retained raw vectors |
+
+Times are seconds. Data preparation, ground-truth generation, and correctness
+checks are outside construction timing.
+
+## Scope and limitations
+
+Each method has one build or fit per dataset; results do not measure run-to-run
+variation.
+
+## Data and reproduction
+
+Run from the repository root after installation:
+
+```bash
+# Prepare data, run all benchmarks, and export results
+python benchmarks/reproduce.py
+
+# Prepare data only
+python benchmarks/download_datasets.py
+
+# Selected results
+python benchmarks/reproduce.py --benchmarks ivf --datasets gist1m
+python benchmarks/reproduce.py --datasets dbpedia --methods qgkmeans
+```
+
+Downloads preserve the published splits and check file sizes and vector shapes. GIST and
+OpenAI need about 26 GB for downloads and prepared data; indexes and temporary
+workspace require additional space.
+
+| Content | Default location |
+| --- | --- |
+| Download cache | `data/benchmark-downloads/` |
+| GIST vectors and ground truth | `data/gist/` |
+| OpenAI vectors and ANN ground truth | `data/dbpedia/` |
+| ANN build records / query records | `results/benchmarks/ann/<dataset>/` / `results/benchmarks/ann/sweeps/<dataset>/` |
+| Saved indexes | `results/benchmarks/ann/indexes/` |
+| K-means records, centroids, assignments | `results/benchmarks/kmeans/<dataset>/` |
+| Logs | `results/benchmarks/ann/logs/`, `results/benchmarks/kmeans/logs/` |
+| Tables / CSVs and SVGs / website | `docs/docs/benchmarks/` / `docs/docs/benchmarks/figures/` / `docs/site/` |
+
+Existing results are skipped. Use a fresh `--output-dir` to repeat measurements;
+this changes raw result and log paths, while documentation exports retain the
+paths above. Partial exports preserve unselected results.
+
+Use `--stage build`, `--stage query`, or `--stage export` for one stage. Querying
+reuses saved indexes; export runs no experiments. `--dry-run` previews commands;
+`--help` lists methods. Both datasets are selected by default.
+
+`data/` and `results/` are ignored by Git. The downloader accepts `--datasets`,
+`--data-dir`, and `--cache-dir`; runners expect the default data paths.
+
+[Query CSV](figures/qps-recall-points.csv) · [Indexing CSV](figures/indexing.csv) ·
+[K-means CSV](figures/kmeans.csv)
diff --git a/docs/docs/benchmarks/figures/graphs-dbpedia-qps-recall.svg b/docs/docs/benchmarks/figures/graphs-dbpedia-qps-recall.svg
new file mode 100644
index 00000000..eb25c644
--- /dev/null
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+dbpedia,faiss-hnsw-flat,"{""ef_search"": 64}",0.9645,1649.1643632445584
+dbpedia,faiss-hnsw-flat,"{""ef_search"": 128}",0.9824,928.6216566213442
+dbpedia,faiss-hnsw-flat,"{""ef_search"": 256}",0.9907,532.9354364134833
+dbpedia,faiss-hnsw-flat,"{""ef_search"": 512}",0.9964,286.02655488384795
+dbpedia,faiss-hnsw-flat,"{""ef_search"": 1024}",0.9984,153.97998040359198
+dbpedia,faiss-hnsw-sq4,"{""ef_search"": 16}",0.8098000000000001,4590.418383835556
+dbpedia,faiss-hnsw-sq4,"{""ef_search"": 32}",0.8811000000000001,2924.588471485705
+dbpedia,faiss-hnsw-sq4,"{""ef_search"": 64}",0.9127000000000001,1708.2005676533308
+dbpedia,faiss-hnsw-sq4,"{""ef_search"": 128}",0.9244000000000001,981.9610767098859
+dbpedia,faiss-hnsw-sq4,"{""ef_search"": 256}",0.9315000000000001,537.2605936900457
+dbpedia,faiss-hnsw-sq4,"{""ef_search"": 512}",0.9361000000000002,299.70730382770734
+dbpedia,faiss-hnsw-sq4,"{""ef_search"": 1024}",0.9388000000000002,154.95071054288263
+dbpedia,faiss-hnsw-sq8,"{""ef_search"": 16}",0.8485,4621.21956533345
+dbpedia,faiss-hnsw-sq8,"{""ef_search"": 32}",0.9218000000000001,2778.3851396221876
+dbpedia,faiss-hnsw-sq8,"{""ef_search"": 64}",0.9610000000000001,1727.917067022132
+dbpedia,faiss-hnsw-sq8,"{""ef_search"": 128}",0.9778000000000001,992.8289386887817
+dbpedia,faiss-hnsw-sq8,"{""ef_search"": 256}",0.9862000000000001,547.8647705358404
+dbpedia,faiss-hnsw-sq8,"{""ef_search"": 512}",0.9919,294.52508051061756
+dbpedia,faiss-hnsw-sq8,"{""ef_search"": 1024}",0.9937,157.9149542784322
diff --git a/docs/docs/benchmarks/graphs.md b/docs/docs/benchmarks/graphs.md
new file mode 100644
index 00000000..ec1ecc2a
--- /dev/null
+++ b/docs/docs/benchmarks/graphs.md
@@ -0,0 +1,78 @@
+# Graph benchmark
+
+Settings and results for eight configurations. See the [overview](bench.md) for
+hardware, datasets, and measurement definitions.
+
+## Index configurations
+
+SymphonyQG uses maximum degree 32, `ef_construction=200`, `init="pipnn"`, and one
+refinement iteration. Both HNSW implementations use `M=16` and
+`efConstruction=200`, giving a base-layer degree bound of 32.
+
+| Method | Stored representation | Additional construction settings |
+| --- | --- | --- |
+| RaBitQ SymphonyQG raw | `quantization_bits=0`, raw vectors | Degree 32, PIPNN initialization |
+| RaBitQ SymphonyQG 4-bit | `quantization_bits=4` | Degree 32, PIPNN initialization |
+| RaBitQ SymphonyQG 8-bit | `quantization_bits=8` | Degree 32, PIPNN initialization |
+| RaBitQ HNSW 1+4 | `nbits=5` | `M=16`, seed 42, 16 residual centroids |
+| RaBitQ HNSW 1+8 | `nbits=9` | `M=16`, seed 42, 16 residual centroids |
+| Faiss HNSW Flat | `HNSW16` | Float32 vectors |
+| Faiss HNSW SQ4 | `HNSW16_SQ4` | Four-bit scalar quantization |
+| Faiss HNSW SQ8 | `HNSW16_SQ8` | Eight-bit scalar quantization |
+
+RaBitQ HNSW trains 16 centroids with RaBitQKMeans (up to 10 iterations, seed 42,
+exact final assignment, spherical training for DBpedia's inner-product metric).
+Training is included in construction time. SymphonyQG 4/8-bit and HNSW
+1+4/1+8 use different encodings; serialized sizes include their full storage costs.
+
+All graph methods sweep `ef=16,32,64,128,256,512,1024` (Faiss calls this
+`efSearch`).
+
+## Reading the results
+
+Curves use one search thread and one query per call over all 1,000 queries.
+Indexes are built with 48 threads and reused for the query sweeps.
+See [figure definitions](bench.md#reading-the-figures) and
+[indexing metrics](bench.md#reading-the-indexing-tables).
+
+## GIST1M
+
+![GIST1M graphs QPS–recall trade-off](figures/graphs-gist1m-qps-recall.svg)
+
+### Indexing time and size
+
+<!-- indexing:gist1m:start -->
+| Method | Train s | Construct s | Total s | Save s | Index GiB |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| RaBitQ SymphonyQG raw | 0.00 | 38.74 | 38.74 | 3.27 | 7.51 |
+| RaBitQ SymphonyQG 4-bit | 0.00 | 38.41 | 38.41 | 1.86 | 4.39 |
+| RaBitQ SymphonyQG 8-bit | 0.00 | 38.42 | 38.42 | 2.04 | 4.84 |
+| RaBitQ HNSW 1+4 | 1.24 | 70.57 | 71.81 | 0.35 | 0.72 |
+| RaBitQ HNSW 1+8 | 1.39 | 76.69 | 78.09 | 0.55 | 1.16 |
+| Faiss HNSW Flat | 0.00 | 78.94 | 78.94 | 1.61 | 3.71 |
+| Faiss HNSW SQ4 | 1.06 | 55.11 | 56.17 | 0.24 | 0.58 |
+| Faiss HNSW SQ8 | 1.08 | 47.12 | 48.20 | 0.42 | 1.03 |
+<!-- indexing:gist1m:end -->
+
+## DBpedia
+
+![DBpedia graphs QPS–recall trade-off](figures/graphs-dbpedia-qps-recall.svg)
+
+### Indexing time and size
+
+<!-- indexing:dbpedia:start -->
+| Method | Train s | Construct s | Total s | Save s | Index GiB |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| RaBitQ SymphonyQG raw | 0.00 | 59.40 | 59.40 | 5.06 | 11.79 |
+| RaBitQ SymphonyQG 4-bit | 0.00 | 57.92 | 57.92 | 2.85 | 6.80 |
+| RaBitQ SymphonyQG 8-bit | 0.00 | 57.17 | 57.17 | 3.35 | 7.51 |
+| RaBitQ HNSW 1+4 | 1.59 | 111.02 | 112.61 | 0.57 | 1.05 |
+| RaBitQ HNSW 1+8 | 1.53 | 122.57 | 124.10 | 0.83 | 1.76 |
+| Faiss HNSW Flat | 0.00 | 147.53 | 147.53 | 2.69 | 5.85 |
+| Faiss HNSW SQ4 | 1.74 | 101.82 | 103.57 | 0.36 | 0.85 |
+| Faiss HNSW SQ8 | 1.79 | 80.66 | 82.45 | 0.64 | 1.56 |
+<!-- indexing:dbpedia:end -->
+
+## Download measurements
+
+[QPS–recall CSV](figures/qps-recall-points.csv) · [Indexing CSV](figures/indexing.csv)
diff --git a/docs/docs/benchmarks/ivf.md b/docs/docs/benchmarks/ivf.md
new file mode 100644
index 00000000..9df7b91f
--- /dev/null
+++ b/docs/docs/benchmarks/ivf.md
@@ -0,0 +1,80 @@
+# IVF benchmark
+
+Settings and results for eight configurations. See the [overview](bench.md) for
+hardware, datasets, and measurement definitions.
+
+## Index configurations
+
+Every IVF index uses **4,096 clusters**. `d` is the dataset dimension and
+`m=d/2` for PQ: 480 on GIST1M and 768 on DBpedia. PQ subquantizers
+use four bits. RaBitQ clustering uses up to 10 iterations, seed 42, exact final
+assignment, and spherical training for DBpedia's inner-product metric. Faiss uses its
+factory training defaults. Training is included in construction time.
+
+| Method | Construction specification | Search parameters |
+| --- | --- | --- |
+| RaBitQ IVF raw | `IvfIndex(nbits=32)`; one-bit filter with original float32 vectors | `nprobe` |
+| RaBitQ IVF 1+4 | `IvfIndex(nbits=5)` | `nprobe` |
+| RaBitQ IVF 1+8 | `IvfIndex(nbits=9)` | `nprobe` |
+| Faiss IVF RaBitQfs raw + RFlat | `HRd,IVF4096,RaBitQfs1,RFlat` | `nprobe`, `k_factor` |
+| Faiss IVF RaBitQfs 1+4 | `HRd,IVF4096,RaBitQfs5` | `nprobe` |
+| Faiss IVF RaBitQfs 1+8 | `HRd,IVF4096,RaBitQfs9` | `nprobe` |
+| Faiss IVF PQfs + RFlat | `IVF4096,PQ{m}x4fs,RFlat` | `nprobe`, `k_factor` |
+| Faiss OPQ-IVF PQfs + RFlat | `OPQ{m},IVF4096,PQ{m}x4fs,RFlat` | `nprobe`, `k_factor` |
+
+`1+4` and `1+8` denote one sign bit plus four or eight extra bits.
+`nbits=32` retains raw float32 vectors in RaBitQ-Library; `RFlat` retains them
+for Faiss reranking. Index size and QPS include retained vectors and reranking.
+
+The search grid is `nprobe=1,2,4,8,16,32,64,128,256,512`, crossed with
+`k_factor=1,2,5,10,20` for the `RFlat` methods. RaBitQ quantized IVF uses its
+default HACC search mode for total bit widths 5 and 9.
+
+## Reading the results
+
+Curves use one search thread and one query per call over all 1,000 queries.
+Indexes are built with 48 threads and reused for the query sweeps.
+See [figure definitions](bench.md#reading-the-figures) and
+[indexing metrics](bench.md#reading-the-indexing-tables).
+
+## GIST1M
+
+![GIST1M ivf QPS–recall trade-off](figures/ivf-gist1m-qps-recall.svg)
+
+### Indexing time and size
+
+<!-- indexing:gist1m:start -->
+| Method | Train s | Construct s | Total s | Save s | Index GiB |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| RaBitQ IVF raw | 14.70 | 0.66 | 15.37 | 1.55 | 3.73 |
+| RaBitQ IVF 1+4 | 14.84 | 1.71 | 16.56 | 0.25 | 0.60 |
+| RaBitQ IVF 1+8 | 14.54 | 7.03 | 21.57 | 0.45 | 1.05 |
+| Faiss IVF RaBitQfs raw + RFlat | 41.61 | 6.76 | 48.37 | 1.58 | 3.73 |
+| Faiss IVF RaBitQfs 1+4 | 41.24 | 13.16 | 54.40 | 0.29 | 0.68 |
+| Faiss IVF RaBitQfs 1+8 | 41.38 | 39.82 | 81.20 | 0.52 | 1.19 |
+| Faiss IVF PQfs + RFlat | 34.52 | 6.59 | 41.11 | 1.59 | 3.84 |
+| Faiss OPQ-IVF PQfs + RFlat | 232.45 | 7.51 | 239.97 | 1.63 | 3.84 |
+<!-- indexing:gist1m:end -->
+
+## DBpedia
+
+![DBpedia ivf QPS–recall trade-off](figures/ivf-dbpedia-qps-recall.svg)
+
+### Indexing time and size
+
+<!-- indexing:dbpedia:start -->
+| Method | Train s | Construct s | Total s | Save s | Index GiB |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| RaBitQ IVF raw | 24.29 | 1.89 | 26.18 | 2.58 | 5.95 |
+| RaBitQ IVF 1+4 | 24.33 | 2.89 | 27.22 | 0.40 | 0.95 |
+| RaBitQ IVF 1+8 | 24.36 | 12.42 | 36.77 | 0.70 | 1.67 |
+| Faiss IVF RaBitQfs raw + RFlat | 85.29 | 12.97 | 98.26 | 2.71 | 6.02 |
+| Faiss IVF RaBitQfs 1+4 | 86.94 | 28.05 | 114.99 | 0.60 | 1.33 |
+| Faiss IVF RaBitQfs 1+8 | 119.86 | 91.11 | 210.97 | 1.00 | 2.34 |
+| Faiss IVF PQfs + RFlat | 99.82 | 15.40 | 115.22 | 2.56 | 6.13 |
+| Faiss OPQ-IVF PQfs + RFlat | 1414.28 | 13.65 | 1427.93 | 2.73 | 6.14 |
+<!-- indexing:dbpedia:end -->
+
+## Download measurements
+
+[QPS–recall CSV](figures/qps-recall-points.csv) · [Indexing CSV](figures/indexing.csv)
diff --git a/docs/docs/benchmarks/kmeans.md b/docs/docs/benchmarks/kmeans.md
new file mode 100644
index 00000000..a62923c3
--- /dev/null
+++ b/docs/docs/benchmarks/kmeans.md
@@ -0,0 +1,84 @@
+# K-means benchmark
+
+Compare **QGKMeans**, **Faiss K-means**, and **SuperKMeans** using their Python APIs.
+Metrics are total time, WCSS, and recall@10 with 1% of clusters probed.
+
+## Settings
+
+| Setting | Value |
+| --- | --- |
+| Threads | 48; methods run sequentially in separate processes |
+| Clustering | Flat L2, 4,096 clusters |
+| Training | All raw float32 base vectors; no sampling, quantization, or hierarchy |
+| Iterations | At most 25; stopping thresholds below |
+| Final assignment | Exact nearest centroid after the last update |
+| Seed / fits | 42 / one fit per method and dataset |
+| Queries | One fixed set of 1,000 per dataset; never used for training or stopping |
+
+Both [datasets](bench.md#datasets-and-ground-truth) use the full base set and
+1,000 queries. GIST uses supplied L2 ground truth; OpenAI uses exhaustive L2
+search. Centroids are not normalized.
+
+| Method | Additional settings |
+| --- | --- |
+| `rabitqlib.QGKMeans` | `quantization_bits=0`, degree 32, `ef_build=240`, `ef_search=16`, one graph refinement, `early_stop_threshold=0`, `FinalAssignmentMode.Exact` |
+| `faiss.Kmeans` | CPU, `nredo=1`, `max_points_per_centroid=N`, `spherical=False`, `early_stop_threshold=0`; exact final `index.search(x, 1)` |
+| `superkmeans.SuperKMeans` | v0.2.0, `quantizer="f32"`, `hierarchical=False`, `sampling_fraction=1`, `angular=False`, `use_blas_only=False`, `early_termination=True`, `tol=1e-4`; exact final `assign(x, centroids)` |
+
+`N` is the full base count. Methods retain their own training assignment algorithms
+and initializers; the same seed does not guarantee identical starting centroids.
+
+## Metrics
+
+- **Time:** training plus exact final assignment, including initialization and
+  internal transformations. QGKMeans includes assignment inside `train()`;
+  Faiss and SuperKMeans report separate components in the CSV. Loading, evaluation,
+  and saving are excluded.
+- **WCSS:** sum of squared L2 distances to final assigned centroids, recomputed
+  with float64 accumulation. Lower is better.
+- **Recall@10:** fraction of exact top-10 neighbors in the nearest
+  `floor(clusters × 1%)` clusters, averaged over queries: 40 probes for both
+  datasets, following SuperKMeans.
+  This is the recall available from exact scanning of those lists.
+
+Exact assignments are checked on 128 base vectors using float64 distances,
+outside timing.
+
+## Results
+
+### GIST1M
+
+<!-- kmeans:gist1m:start -->
+| Method | Total time (s) | WCSS | Recall@10 (40 probes) | Iterations |
+| --- | ---: | ---: | ---: | ---: |
+| QGKMeans | 16.00 | 1034787.87 | 0.819600 | 25 |
+| Faiss K-means | 84.17 | 1034624.88 | 0.815700 | 25 |
+| SuperKMeans | 28.14 | 1034648.39 | 0.820700 | 25 |
+<!-- kmeans:gist1m:end -->
+
+### DBpedia
+
+<!-- kmeans:dbpedia:start -->
+| Method | Total time (s) | WCSS | Recall@10 (40 probes) | Iterations |
+| --- | ---: | ---: | ---: | ---: |
+| QGKMeans | 24.08 | 620378.132 | 0.893900 | 25 |
+| Faiss K-means | 171.03 | 620737.534 | 0.894700 | 25 |
+| SuperKMeans | 35.23 | 620268.628 | 0.896800 | 24 |
+<!-- kmeans:dbpedia:end -->
+
+[Download measurements (CSV)](figures/kmeans.csv)
+
+## Reproduction
+
+Follow the [installation instructions](bench.md#package-installation), then run:
+
+```bash
+python benchmarks/reproduce.py --benchmarks kmeans
+```
+
+Results, centroids, and assignments are saved in `results/benchmarks/kmeans/`.
+Use `--datasets gist1m` or `--methods qgkmeans` for a subset, and a fresh
+`--output-dir` to repeat measurements. See the [reproduction options](bench.md#data-and-reproduction).
+
+Protocol references: SuperKMeans' [benchmark guide](https://github.com/cwida/SuperKMeans/blob/main/BENCHMARKING.md)
+and [recall evaluator](https://github.com/cwida/SuperKMeans/blob/main/benchmarks/bench_utils.h).
diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml
index 894d3ac6..830be558 100644
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -25,6 +25,11 @@ nav:
   - Integrations:
       - Zvec: integrations/zvec.md
   - Clustering: clustering.md
+  - Benchmarks:
+      - Overview: benchmarks/bench.md
+      - IVF Indexes: benchmarks/ivf.md
+      - Graph Indexes: benchmarks/graphs.md
+      - K-means: benchmarks/kmeans.md
 
 theme:
   name: material
diff --git a/tests/python/test_benchmark_protocol.py b/tests/python/test_benchmark_protocol.py
new file mode 100644
index 00000000..18792270
--- /dev/null
+++ b/tests/python/test_benchmark_protocol.py
@@ -0,0 +1,188 @@
+"""Benchmark protocol checks without external datasets or baseline packages."""
+
+import importlib
+import json
+import sys
+from pathlib import Path
+
+import numpy as np
+import pytest
+
+# Wheel tests may invoke pytest outside the checkout.
+ROOT = Path(__file__).resolve().parents[2]
+sys.path.insert(0, str(ROOT))
+build_indexes = importlib.import_module("benchmarks.build_indexes")
+query_index = importlib.import_module("benchmarks.query_index")
+reproduce = importlib.import_module("benchmarks.reproduce")
+
+
+@pytest.fixture
+def sweep(tmp_path, monkeypatch):
+    queries = np.arange(12, dtype="float32").reshape(3, 4)
+    truth = np.array([[0], [1], [2]], dtype="int32")
+    record = {
+        "config": {
+            "base": build_indexes.DATASETS["gist1m"][0],
+            "socket": 0,
+            "method": "rabitq-ivf",
+            "bits": 5,
+            "k": 1,
+            "base_count": 8,
+            "query_count": 3,
+            "index_dir": str(tmp_path),
+        },
+        "search": {"cpu_placement": {"allowed_cpus": [0]}},
+    }
+    initial = tmp_path / "initial.json"
+    initial.write_text(json.dumps(record))
+    output = tmp_path / "sweep.json"
+    monkeypatch.setattr(
+        query_index.os, "sched_getaffinity", lambda _: {0}, raising=False
+    )
+    monkeypatch.setattr(query_index, "query_set", lambda *_: (queries, truth))
+    monkeypatch.setattr(query_index, "load_index", lambda *_: object())
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        [
+            "query_index.py",
+            str(initial),
+            "--dataset",
+            "gist1m",
+            "--output",
+            str(output),
+        ],
+    )
+    return initial, output
+
+
+def test_recall_uses_timed_single_query_results(sweep, monkeypatch):
+    _, output = sweep
+    calls = []
+
+    def search(index, config, queries, threads):
+        # Batch and single-query paths deliberately return different neighbors.
+        calls.append((len(queries), threads))
+        ids = (
+            (queries[:, :1] / 4).astype("int32")
+            if len(queries) == 1
+            else np.full((len(queries), 1), 7)
+        )
+        return ids, np.zeros_like(ids, dtype="float32")
+
+    monkeypatch.setattr(query_index, "search", search)
+    query_index.main()
+    record = json.loads(output.read_text())
+    assert set(calls) == {(1, 1)}
+    assert record["recall_mode"] == "one_query_per_call"
+    assert len(record["query_points"]) == 10
+    assert all(point["recall"] == 1 for point in record["query_points"])
+    assert all(point["one_query_service_s"] > 0 for point in record["query_points"])
+
+
+def test_sweep_rejects_wrong_query_count_before_search(sweep, monkeypatch):
+    initial, output = sweep
+    record = json.loads(initial.read_text())
+    record["config"]["query_count"] = 4
+    initial.write_text(json.dumps(record))
+    monkeypatch.setattr(
+        query_index, "load_index", lambda *_: pytest.fail("loaded index")
+    )
+    with pytest.raises(SystemExit) as error:
+        query_index.main()
+    assert error.value.code == 2
+    assert not output.exists()
+
+
+@pytest.mark.parametrize(
+    "key,value",
+    [("bits", 9), ("base_count", 980000), ("threads", 16), ("metric", "ip")],
+)
+def test_ann_record_rejects_wrong_settings(key, value):
+    base, queries, truth, metric = build_indexes.DATASETS["gist1m"]
+    cfg = dict(
+        method="rabitq-ivf",
+        metric=metric,
+        base=str(ROOT / base),
+        queries=str(ROOT / queries),
+        groundtruth=str(ROOT / truth),
+        base_count=1000000,
+        query_count=1000,
+        base_limit=None,
+        query_limit=None,
+        threads=48,
+        socket=0,
+        k=10,
+        nlist=4096,
+        opq=False,
+        bits=5,
+    )
+    record = {"config": cfg, "data": {"dimension": 960}}
+    build_indexes.validate_record(record, "gist1m", "rabitq-ivf-1p4")
+    cfg[key] = value
+    with pytest.raises(ValueError, match="unexpected ANN configuration"):
+        build_indexes.validate_record(record, "gist1m", "rabitq-ivf-1p4")
+
+
+@pytest.mark.parametrize("stage", ["all", "build", "query", "export"])
+def test_partial_reproduction_selects_only_requested_work(monkeypatch, capsys, stage):
+    monkeypatch.setattr(
+        sys,
+        "argv",
+        [
+            "reproduce.py",
+            "--datasets",
+            "dbpedia",
+            "--methods",
+            "faiss-pqfs",
+            "--stage",
+            stage,
+            "--dry-run",
+        ],
+    )
+    reproduce.main()
+    commands = capsys.readouterr().out
+    assert "gist1m" not in commands
+    assert "run_kmeans.py" not in commands
+    assert ("download_datasets.py" in commands) == (stage != "export")
+    assert ("build_indexes.py" in commands) == (stage in ("all", "build"))
+    assert ("run_sweeps.py" in commands) == (stage in ("all", "query"))
+    assert ("plot_results.py" in commands) == (stage in ("all", "export"))
+
+
+def test_recall_rejects_missing_query_results():
+    from benchmarks.run import recall_at_k
+
+    with pytest.raises(ValueError, match="shapes differ"):
+        recall_at_k(np.array([[0]]), np.array([[0], [1]]))
+
+
+def test_kmeans_export_rejects_wrong_metric_before_writing(tmp_path):
+    from benchmarks.run_kmeans import publish
+
+    docs = tmp_path / "docs"
+    docs.mkdir()
+    page = docs / "kmeans.md"
+    page.write_text("original content")
+    results = tmp_path / "results/gist1m"
+    results.mkdir(parents=True)
+    record = {
+        "config": {
+            "dataset": "gist1m",
+            "method": "faiss",
+            "metric": "ip",
+            "seed": 42,
+            "socket": 0,
+            "k": 4096,
+            "threads": 48,
+            "niter": 25,
+            "nq": 1000,
+            "n": 1000000,
+            "d": 960,
+        },
+        "quality": {"nprobe": 40},
+    }
+    (results / "faiss.json").write_text(json.dumps(record))
+    with pytest.raises(ValueError, match="unexpected benchmark configuration"):
+        publish(tmp_path / "results", docs, datasets=["gist1m"], methods=["faiss"])
+    assert page.read_text() == "original content"