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tasks/act-tensor-sparse-panel-imputation-r2/environment/Dockerfile

@@ -0,0 +1,44 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+RUN set -eu; \
+    if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \
+      apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \
+    fi; \
+    rm -f /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1; \
+    rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/*
+
+
+ENTRYPOINT []
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=1
+
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends git=1:2.39.5-0+deb12u3 \
+    && git config --system user.email "agent@localhost" \
+    && git config --system user.name "autoresearch agent" \
+    && rm -rf /var/lib/apt/lists/*
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \
+    && rm /tmp/requirements.txt
+
+WORKDIR /app
+COPY ./app /app
+
+RUN mkdir -p /app/solution \
+    && chown -R agent:agent /app \
+    && find /app -exec touch -d @1700000000 {} +
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1
+
+USER agent

tasks/act-tensor-sparse-panel-imputation-r2/environment/requirements.txt

@@ -0,0 +1,3 @@
+numpy==2.5.2
+scipy==1.18.0
+scikit-learn==1.9.0

tasks/act-tensor-sparse-panel-imputation-r2/hints/hint.md

@@ -0,0 +1 @@
+Map the ranks to normal scores before any conditioning, then combine low-rank fits along all three modes rather than one unfolding, and push the rank far higher than seems reasonable. Model the residuals afterwards: an autoregressive smoother over observed neighbours, a cross-characteristic ridge, then a short-window and a global cross-time ridge, all augmented with the missingness mask. Low-rank estimates are shrunk toward zero, so refit a scale against observed entries before clipping.

tasks/act-tensor-sparse-panel-imputation-r2/tests/Dockerfile

@@ -0,0 +1,35 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+ENTRYPOINT []
+ENV PYTHONPATH=
+ENV PYTHONSAFEPATH=1
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=1 \
+    OPENBLAS_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1 \
+    NUMEXPR_NUM_THREADS=1
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir \
+        -r /tmp/requirements.txt -r /tmp/requirements-verifier.txt \
+    && rm /tmp/requirements.txt /tmp/requirements-verifier.txt
+
+RUN mkdir -p /app/solution /logs/verifier
+
+COPY . /tests
+
+RUN chown -R root:root /tests \
+    && chmod 700 /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \
+    && chmod +x /tests/test.sh /tests/intermediate.sh \
+    && find /tests -exec touch -d @1700000000 {} +
+
+WORKDIR /tests
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1

tasks/act-tensor-sparse-panel-imputation-r2/tests/compute_reward.py

@@ -0,0 +1,17 @@
+import json
+import math
+from pathlib import Path
+
+REWARD_PATH = Path("/logs/verifier/reward.txt")
+
+try:
+    REWARD_PATH.parent.mkdir(parents=True, exist_ok=True)
+    value = float(json.loads(Path("/logs/verifier/metric.json").read_text())["reward"])
+    if not math.isfinite(value):
+        raise ValueError("non-finite reward")
+    reward = f"{min(1.0, max(0.0, value)):.12g}"
+    REWARD_PATH.write_text(reward + "\n")
+    print(f"reward={reward}")
+except Exception as exc:
+    REWARD_PATH.write_text("0\n")
+    print(f"reward=0 (fallback: {exc})")

tasks/act-tensor-sparse-panel-imputation-r2/tests/grader_core.py

@@ -0,0 +1,60 @@
+import math
+
+import numpy as np
+
+VALUE_LO = -1.0
+VALUE_HI = 1.0
+PER_INSTANCE_TIMEOUT_SEC = 240.0
+MAX_OUTPUT_BYTES = 64_000_000
+
+M0 = 0.0
+BASELINE_SCORE = 0.7047
+HIGHER_IS_BETTER = True
+
+
+def r2_imp(pred_at_eval, truth_at_eval):
+    """R^2_imp pooled over one instance's held-out cells.
+
+    1 - sum_m (x_m - xtilde_m)^2 / sum_m (x_m - xbar)^2, with xbar the mean of the
+    held-out TRUE values.
+    """
+    truth = np.asarray(truth_at_eval, dtype=np.float64)
+    pred = np.asarray(pred_at_eval, dtype=np.float64)
+    denom = float(np.sum((truth - truth.mean()) ** 2))
+    if not math.isfinite(denom) or denom <= 0.0:
+        raise ValueError("degenerate held-out set: zero variance")
+    resid = float(np.sum((truth - pred) ** 2))
+    return 1.0 - resid / denom
+
+
+def validate_output(arr, expected_shape):
+    """Return (ok, reason).  Mirrors the contract stated in instruction.md."""
+    if arr is None:
+        return False, "no output produced"
+    if not isinstance(arr, np.ndarray):
+        return False, f"output is {type(arr).__name__}, not a numpy array"
+    if arr.dtype.kind != "f":
+        return False, f"output dtype {arr.dtype} is not a real floating dtype"
+    if arr.shape != expected_shape:
+        return False, f"output shape {arr.shape} != {expected_shape}"
+    values = arr.astype(np.float64, copy=False)
+    if not np.all(np.isfinite(values)):
+        return False, "output contains NaN or inf"
+    if float(values.min()) < VALUE_LO or float(values.max()) > VALUE_HI:
+        return False, f"output outside [{VALUE_LO}, {VALUE_HI}]"
+    return True, ""
+
+
+def graded_reward(metric, m0=M0, baseline_score=BASELINE_SCORE,
+                  higher_is_better=HIGHER_IS_BETTER,
+                  valid=True):
+    """rational_squash: 0 at the trivial baseline, 0.5 at the baseline score, asymptotic
+    to 1, monotone, never negative, with no cutoff."""
+    if not valid or metric is None or not math.isfinite(metric):
+        return 0.0
+    sigma = 1.0 if higher_is_better else -1.0
+    x = sigma * (metric - m0)
+    x_ref = sigma * (baseline_score - m0)
+    assert x_ref > 0, "malformed task: the baseline score does not beat the trivial baseline"
+    u = max(0.0, x) / x_ref
+    return u / (1.0 + u)

tasks/act-tensor-sparse-panel-imputation-r2/tests/intermediate.sh

@@ -0,0 +1,33 @@
+#!/bin/bash
+set -u
+
+trap 'if [ ! -s /logs/verifier/reward.txt ]; then mkdir -p /logs/verifier; printf "0\n" > /logs/verifier/reward.txt; fi' EXIT
+
+mkdir -p /logs/verifier
+chown root:root /logs/verifier 2>/dev/null || true
+chmod 755 /logs/verifier 2>/dev/null || true
+rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json
+
+chmod -R go-rwx /tests
+
+cd /tests
+export GRADER_SPLIT=intermediate
+/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json \
+    /tests/test_state.py -rA > /logs/verifier/intermediate_pytest.log 2>&1
+/usr/local/bin/python3 /tests/compute_reward.py >> /logs/verifier/intermediate_pytest.log 2>&1
+
+/usr/local/bin/python3 - <<'PY'
+import json
+from pathlib import Path
+try:
+    d = json.loads(Path("/logs/verifier/metric.json").read_text())
+    print(f"panels_scored={d['n_instances']} ran_without_error={d['n_scored_ok']}")
+    print(f"mean_R2_imp={d['metric']:+.4f}")
+    if not d["submission_ok"]:
+        print(f"submission_rejected: {d['submission_note']}")
+except Exception:
+    print("panels_scored=0 ran_without_error=0")
+    print("mean_R2_imp=n/a (the submission produced no gradeable result)")
+PY
+
+exit 0

tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements-verifier.txt

@@ -0,0 +1,2 @@
+pytest==8.3.5
+pytest-json-ctrf==0.3.5

tasks/act-tensor-sparse-panel-imputation-r2/tests/requirements.txt

@@ -0,0 +1,3 @@
+numpy==2.5.2
+scipy==1.18.0
+scikit-learn==1.9.0

tasks/act-tensor-sparse-panel-imputation-r2/tests/run_candidate.py

@@ -0,0 +1,23 @@
+import sys
+from pathlib import Path
+
+import numpy as np
+
+
+def main():
+    cand_dir, panel_path, seed, out_path = sys.argv[1:5]
+    sys.path.insert(0, cand_dir)
+
+    import importlib.util
+    spec = importlib.util.spec_from_file_location(
+        "candidate_impute", str(Path(cand_dir) / "impute.py"))
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+
+    panel = np.load(panel_path, allow_pickle=False)
+    result = module.impute(panel, int(seed))
+    np.save(out_path, np.asarray(result), allow_pickle=False)
+
+
+if __name__ == "__main__":
+    main()

tasks/act-tensor-sparse-panel-imputation-r2/tests/test.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+set -u
+
+trap 'if [ ! -s /logs/verifier/reward.txt ]; then mkdir -p /logs/verifier; printf "0\n" > /logs/verifier/reward.txt; fi' EXIT
+
+mkdir -p /logs/verifier
+chown root:root /logs/verifier 2>/dev/null || true
+chmod 755 /logs/verifier 2>/dev/null || true
+rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json
+
+chmod -R go-rwx /tests
+
+cd /tests
+export GRADER_SPLIT=final
+/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA
+/usr/local/bin/python3 /tests/compute_reward.py
+
+exit 0

tasks/act-tensor-sparse-panel-imputation-r2/tests/test_state.py

@@ -0,0 +1,199 @@
+import json
+import os
+import shutil
+import signal
+import stat
+import subprocess
+import sys
+import tempfile
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+import grader_core as gc
+
+TESTS_DIR = Path(__file__).resolve().parent
+SUBMISSION_DIR = Path("/app/solution")
+ENTRY_POINT = "impute.py"
+METRIC_PATH = Path("/logs/verifier/metric.json")
+SPLIT = os.environ.get("GRADER_SPLIT", "final")
+HIDDEN_DIR = TESTS_DIR / "hidden_data" / SPLIT
+RUNUSER = shutil.which("runuser") or "/usr/sbin/runuser"
+AGENT_UID, AGENT_GID = 1001, 1001
+MAX_CONCURRENT = 4
+PYTHON = sys.executable
+
+
+def _copy_submission(dest):
+    """Copy the submitted directory, treating every submitted path as hostile.
+
+    Any symlink or multiply-linked regular file anywhere in the tree rejects the whole
+    submission: a privileged copy that followed `impute.py -> /tests/hidden_data/...`
+    would materialise verifier-owned files into the directory the score is computed from.
+    """
+    if not SUBMISSION_DIR.is_dir() or SUBMISSION_DIR.is_symlink():
+        return False, f"{SUBMISSION_DIR} is missing or is a symlink"
+    for path in [SUBMISSION_DIR, *SUBMISSION_DIR.rglob("*")]:
+        info = path.lstat()
+        if stat.S_ISLNK(info.st_mode):
+            return False, f"submitted tree contains a symlink: {path}"
+        if stat.S_ISREG(info.st_mode) and info.st_nlink > 1:
+            return False, f"submitted tree contains a hard link: {path}"
+    shutil.copytree(SUBMISSION_DIR, dest, symlinks=True, dirs_exist_ok=True)
+    subprocess.run(["chown", "-R", "root:root", str(dest)], check=False)
+    subprocess.run(["chmod", "-R", "a-w,a+rX", str(dest)], check=False)
+    if not (Path(dest) / ENTRY_POINT).is_file():
+        return False, f"no {ENTRY_POINT} in the submitted directory"
+    return True, ""
+
+
+def _run_one(cand_dir, npz_path):
+    """Execute the candidate on one sealed panel and score it here, in the root process.
+
+    Returns (metric, note).  Every failure mode -- missing entry point, import error,
+    exception, timeout, no output, unparseable output, wrong shape, wrong dtype,
+    non-finite value, out-of-range value -- returns the trivial-baseline metric.
+    """
+    with np.load(npz_path, allow_pickle=False) as data:
+        panel = np.ascontiguousarray(data["panel"], dtype=np.float64)
+        eval_mask = data["eval_mask"]
+        eval_true = data["eval_true"]
+        seed = int(data["seed"])
+
+    work = Path(tempfile.mkdtemp(prefix="grade_"))
+    try:
+        os.chmod(work, 0o755)
+        runner = work / "run_candidate.py"
+        shutil.copy(TESTS_DIR / "run_candidate.py", runner)
+        panel_path = work / "panel.npy"
+        np.save(panel_path, panel, allow_pickle=False)
+        os.chmod(panel_path, 0o444)
+        os.chmod(runner, 0o444)
+
+        scratch = work / "scratch"
+        scratch.mkdir()
+        os.chown(scratch, AGENT_UID, AGENT_GID)
+        os.chmod(scratch, 0o755)
+        out_path = scratch / "out.npy"
+
+        env = {
+            "PATH": "/usr/local/bin:/usr/bin:/bin",
+            "HOME": str(scratch),
+            "TMPDIR": str(scratch),
+            "PYTHONHASHSEED": "0",
+            "PYTHONSAFEPATH": "1",
+            "PYTHONDONTWRITEBYTECODE": "1",
+            "OMP_NUM_THREADS": "1",
+            "OPENBLAS_NUM_THREADS": "1",
+            "MKL_NUM_THREADS": "1",
+            "NUMEXPR_NUM_THREADS": "1",
+        }
+        cmd = [RUNUSER, "-u", "agent", "--", PYTHON, str(runner),
+               str(cand_dir), str(panel_path), str(seed), str(out_path)]
+        chatter_path = work / "candidate_stdio.log"
+        with open(chatter_path, "wb") as sink:
+            proc = subprocess.Popen(cmd, env=env, cwd=str(scratch),
+                                    stdout=sink, stderr=subprocess.STDOUT,
+                                    stdin=subprocess.DEVNULL, start_new_session=True)
+            try:
+                proc.wait(timeout=gc.PER_INSTANCE_TIMEOUT_SEC)
+            except subprocess.TimeoutExpired:
+                try:
+                    os.killpg(os.getpgid(proc.pid), signal.SIGKILL)
+                except OSError:
+                    proc.kill()
+                proc.wait()
+                return gc.M0, "timed out"
+        if proc.returncode != 0:
+            tail = chatter_path.read_bytes()[-4000:].decode("utf-8", "replace")
+            lines = tail.strip().splitlines()
+            return gc.M0, f"candidate exited {proc.returncode}: {lines[-1][:200] if lines else ''}"
+
+        if not out_path.exists():
+            return gc.M0, "no output file"
+        info = out_path.lstat()
+        if stat.S_ISLNK(info.st_mode) or not stat.S_ISREG(info.st_mode):
+            return gc.M0, "output path is not a regular file"
+        if info.st_size > gc.MAX_OUTPUT_BYTES:
+            return gc.M0, "output file too large"
+        try:
+            fd = os.open(out_path, os.O_RDONLY | os.O_NOFOLLOW)
+            with os.fdopen(fd, "rb") as handle:
+                arr = np.load(handle, allow_pickle=False)
+        except Exception as exc:
+            return gc.M0, f"unreadable output: {type(exc).__name__}"
+
+        ok, reason = gc.validate_output(arr, panel.shape)
+        if not ok:
+            return gc.M0, reason
+        return gc.r2_imp(arr.astype(np.float64, copy=False)[eval_mask], eval_true), "ok"
+    finally:
+        shutil.rmtree(work, ignore_errors=True)
+
+
+def _reap_agent_processes():
+    """Kill anything still running as uid 1001.
+
+    A candidate can detach itself from the process group the per-panel timeout kills.  It
+    could not forge a score even so -- /logs/verifier is root-owned 0755 -- but nothing
+    agent-authored should outlive the graded run.  Done by walking /proc rather than with
+    pkill, which the slim base does not ship.
+    """
+    for entry in Path("/proc").iterdir():
+        if not entry.name.isdigit():
+            continue
+        try:
+            if entry.stat().st_uid == AGENT_UID:
+                os.kill(int(entry.name), signal.SIGKILL)
+        except (OSError, ProcessLookupError):
+            pass
+
+
+def test_imputation_r2():
+    """Grade the submitted imputer on the sealed panels of this split.
+
+    WHAT: executes /app/solution/impute.py once per sealed panel and recomputes
+    R^2_imp on the held-out cells from verifier-owned ground truth.
+    WHY: the score must come from running the agent's code on panels it has never seen,
+    never from a file it wrote or a number it reported.
+    """
+    instances = sorted(HIDDEN_DIR.glob("instance_*.npz"))
+    assert instances, f"sealed split {HIDDEN_DIR} is empty"
+
+    cand_root = Path(tempfile.mkdtemp(prefix="cand_"))
+    os.chmod(cand_root, 0o755)
+    cand_dir = cand_root / "solution"
+    ok, reason = _copy_submission(cand_dir)
+    if ok:
+        with ThreadPoolExecutor(max_workers=MAX_CONCURRENT) as pool:
+            results = list(pool.map(lambda p: _run_one(cand_dir, p), instances))
+    else:
+        results = [(gc.M0, reason)] * len(instances)
+    shutil.rmtree(cand_root, ignore_errors=True)
+    _reap_agent_processes()
+
+    metrics = [m for m, _ in results]
+    notes = [n for _, n in results]
+    mean_metric = float(np.mean(metrics))
+    reward = gc.graded_reward(mean_metric)
+
+    METRIC_PATH.parent.mkdir(parents=True, exist_ok=True)
+    METRIC_PATH.write_text(json.dumps({
+        "metric_name": "mean_out_of_sample_R2_imp_block_regime",
+        "split": SPLIT,
+        "n_instances": len(instances),
+        "n_scored_ok": sum(1 for n in notes if n == "ok"),
+        "metric": mean_metric,
+        "reward": reward,
+        "submission_ok": ok,
+        "submission_note": reason if not ok else "ok",
+    }, indent=2) + "\n")
+
+    if SPLIT == "final":
+        for path, (m, note) in zip(instances, results):
+            print(f"{path.name}: R2_imp={m:+.4f} ({note})")
+    print(f"split={SPLIT} instances={len(instances)} "
+          f"scored_ok={sum(1 for n in notes if n == 'ok')} "
+          f"mean_R2_imp={mean_metric:+.4f} reward={reward:.4f}")

tasks/carps-star-discrepancy-subset-select/environment/Dockerfile

@@ -0,0 +1,41 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+RUN set -eu; \
+    if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \
+      apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \
+    fi; \
+    rm -f /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1 git=1:2.39.5-0+deb12u3; \
+    rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/*
+
+
+ENTRYPOINT []
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=1
+
+RUN git config --system user.email "agent@localhost" \
+    && git config --system user.name "autoresearch agent"
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \
+    && rm /tmp/requirements.txt
+
+WORKDIR /app
+COPY ./app /app
+
+RUN mkdir -p /app/output \
+    && chown -R agent:agent /app \
+    && find /app -exec touch -d @1700000000 {} +
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1
+
+USER agent

tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/Dockerfile

@@ -0,0 +1,42 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+RUN set -eu; \
+    if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \
+      apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \
+    fi; \
+    rm -f /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1 git=1:2.39.5-0+deb12u3; \
+    rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/*
+
+
+ENTRYPOINT []
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=1
+
+RUN git config --system user.email "agent@localhost" \
+    && git config --system user.name "agent"
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \
+    && rm /tmp/requirements.txt
+
+WORKDIR /app
+COPY ./app /app
+
+RUN mkdir -p /app/output \
+    && chown -R agent:agent /app \
+    && chown -R root:root /app/data \
+    && find /app -exec touch -d @1700000000 {} +
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1
+
+USER agent

tasks/causalpfn-cate-pehe-ihdp-surfaceb/environment/requirements.txt

@@ -0,0 +1,17 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+filelock==3.32.3
+fsspec==2026.7.0
+Jinja2==3.1.6
+joblib==1.5.3
+MarkupSafe==3.0.3
+mpmath==1.3.0
+narwhals==2.24.0
+networkx==3.6.1
+numpy==2.5.2
+scikit-learn==1.9.0
+scipy==1.18.0
+setuptools==84.0.0
+sympy==1.14.0
+threadpoolctl==3.6.0
+torch==2.13.0+cpu
+typing_extensions==4.16.0

tasks/causalpfn-cate-pehe-ihdp-surfaceb/hints/hint.md

@@ -0,0 +1 @@
+The generator uses one sparse coefficient vector shared by both arms, drawn from a small discrete grid, and the treated cohort is the same fixed set of rows in every dataset with its average effect pinned to an exact constant. Impose that constant as a hard constraint, then search the grid by profiled coordinate descent with pair and triple escape moves, using a smooth likelihood to search and the exact one only to re-rank near-optimal candidates. Aggregate candidates with a weighted geometric median, since the metric averages per-dataset RMSE.

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/Dockerfile

@@ -0,0 +1,34 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+ENTRYPOINT []
+ENV PYTHONPATH=
+ENV PYTHONSAFEPATH=1
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1 \
+    OPENBLAS_NUM_THREADS=1
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir \
+        -r /tmp/requirements.txt -r /tmp/requirements-verifier.txt \
+    && rm /tmp/requirements.txt /tmp/requirements-verifier.txt
+
+RUN mkdir -p /app/output /logs/verifier
+
+COPY . /tests
+
+RUN chown -R root:root /tests \
+    && chmod 700 /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \
+    && chmod +x /tests/test.sh /tests/intermediate.sh \
+    && find /tests -exec touch -d @1700000000 {} +
+
+WORKDIR /tests
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/compute_reward.py

@@ -0,0 +1,19 @@
+import json
+import math
+from pathlib import Path
+
+REWARD_PATH = Path("/logs/verifier/reward.txt")
+METRIC_PATH = Path("/logs/verifier/metric.json")
+
+try:
+    REWARD_PATH.parent.mkdir(parents=True, exist_ok=True)
+    value = float(json.loads(METRIC_PATH.read_text())["reward"])
+    if not math.isfinite(value):
+        raise ValueError("non-finite reward")
+    reward = f"{min(1.0, max(0.0, value)):.12g}"
+    REWARD_PATH.write_text(reward + "\n")
+    print(f"reward={reward}")
+except Exception as exc:
+    REWARD_PATH.parent.mkdir(parents=True, exist_ok=True)
+    REWARD_PATH.write_text("0\n")
+    print(f"reward=0 (fallback: {exc})")

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/grader_core.py

@@ -0,0 +1,170 @@
+from __future__ import annotations
+
+import math
+import os
+import shutil
+import stat
+from pathlib import Path
+
+import numpy as np
+
+N_TRAIN = 672
+N_TEST = 75
+N_COVARIATES = 25
+
+GRADED_RUN_BUDGET_SEC = 1200.0
+
+M0 = 5.40
+BASELINE_SCORE = 0.58
+
+STATUS_OK = 0
+STATUS_RAISED = 1
+STATUS_BAD_SHAPE = 2
+STATUS_NON_FINITE = 3
+
+
+def load_sealed_panel(panel_path: str | os.PathLike) -> tuple[list[dict], np.ndarray]:
+    """Return (inputs, taus). `inputs` carries only what a candidate may see."""
+    with np.load(panel_path, allow_pickle=False) as f:
+        n = int(f["n_realizations"])
+        inputs, taus = [], []
+        for k in range(n):
+            inputs.append({
+                "X_train": f[f"X_train_{k:03d}"].astype(np.float64),
+                "t_train": f[f"t_train_{k:03d}"].astype(np.float64),
+                "y_train": f[f"y_train_{k:03d}"].astype(np.float64),
+                "X_test": f[f"X_test_{k:03d}"].astype(np.float64),
+            })
+            taus.append(f[f"tau_test_{k:03d}"].astype(np.float64))
+    for d, tau in zip(inputs, taus):
+        assert d["X_train"].shape == (N_TRAIN, N_COVARIATES), "sealed panel geometry changed"
+        assert d["X_test"].shape == (N_TEST, N_COVARIATES), "sealed panel geometry changed"
+        assert tau.shape == (N_TEST,), "sealed panel geometry changed"
+    return inputs, np.asarray(taus, dtype=np.float64)
+
+
+def pehe(tau_true: np.ndarray, tau_hat: np.ndarray) -> float:
+    """Root-mean-squared error of the estimated effect over one realization's test units."""
+    return float(np.sqrt(np.mean((np.asarray(tau_true, dtype=np.float64)
+                                  - np.asarray(tau_hat, dtype=np.float64)) ** 2)))
+
+
+def constant_baseline_tau(one_input: dict) -> float:
+    """The no-covariate constant effect: mean(y | t=1) - mean(y | t=0) on the training rows.
+
+    Recomputed here by the verifier from trusted bytes. A call that fails is scored as if it
+    had returned this constant, which is exactly what a submission gets by *returning* the
+    constant, so a failed call can never beat an honest one.
+    """
+    t = one_input["t_train"]
+    y = one_input["y_train"]
+    treated, control = y[t == 1.0], y[t == 0.0]
+    if treated.size == 0 or control.size == 0:
+        return 0.0
+    return float(treated.mean() - control.mean())
+
+
+def score_panel(inputs: list[dict], taus: np.ndarray, preds: np.ndarray,
+                status: np.ndarray) -> dict:
+    """Per-realization PEHE with the constant-baseline fallback, then the panel mean.
+
+    `preds` and `status` come from the candidate's process and are treated as untrusted
+    data: shape, dtype and finiteness are re-checked here, and any realization that fails
+    that re-check is scored by the fallback regardless of what status it claims.
+    """
+    n = len(inputs)
+    per, n_ok, n_fallback = [], 0, 0
+    for k in range(n):
+        row = preds[k] if (preds.ndim == 2 and preds.shape[0] > k) else None
+        ok = (row is not None and row.shape == (N_TEST,) and np.all(np.isfinite(row))
+              and int(status[k]) == STATUS_OK)
+        if ok:
+            per.append(pehe(taus[k], row))
+            n_ok += 1
+        else:
+            fallback = np.full(N_TEST, constant_baseline_tau(inputs[k]))
+            per.append(pehe(taus[k], fallback))
+            n_fallback += 1
+    per_arr = np.asarray(per, dtype=np.float64)
+    return {
+        "metric": float(per_arr.mean()),
+        "n_realizations": n,
+        "n_ok": n_ok,
+        "n_fallback": n_fallback,
+        "pehe_median": float(np.median(per_arr)),
+        "pehe_p90": float(np.quantile(per_arr, 0.9)),
+        "pehe_max": float(per_arr.max()),
+    }
+
+
+def graded_reward(metric: float | None, valid: bool) -> float:
+    """Continuous reward in [0, 1]; lower mean PEHE is strictly better, with no cutoff.
+
+    Log-ratio improvement in baseline-score units, squashed by u/(1+u):
+
+        u = max(0, log(m0 / m) / log(m0 / baseline_score)),   reward = u / (1 + u)
+
+    m >= m0 (the strongest trivial predictor) -> 0.  m == baseline_score -> 0.5.
+    m -> 0 -> 1. PEHE is ratio-scale with a true zero and improves multiplicatively, so the
+    improvement is measured as a log ratio.
+    """
+    if not valid or metric is None:
+        return 0.0
+    m = float(metric)
+    if not math.isfinite(m) or m < 0.0:
+        return 0.0
+    scale = math.log(M0 / BASELINE_SCORE)
+    assert scale > 0.0, "malformed task: the baseline anchor does not beat the trivial baseline"
+    if m <= 0.0:
+        return 1.0
+    u = max(0.0, math.log(M0 / m) / scale)
+    return u / (1.0 + u)
+
+
+def copy_solution_tree(src: str | os.PathLike, dst: str | os.PathLike) -> dict:
+    """Copy the submitted tree into a root-owned, agent-read-only sandbox.
+
+    Every submitted path is treated as hostile. Symlinks, hard links and non-regular files
+    are pruned at every depth BEFORE anything is copied, and the copy itself never follows
+    a link (`shutil.copyfile(..., follow_symlinks=False)`, the equivalent of
+    `cp --no-dereference` / `copytree(symlinks=True)`), so a submitted
+    `estimator.py -> /tests/hidden_data/final/panel.npz` cannot materialise verifier bytes
+    inside the sandbox the run reads from.
+    """
+    src, dst = Path(src), Path(dst)
+    pruned: list[str] = []
+    dst.mkdir(parents=True, exist_ok=True)
+    for root, dirs, files in os.walk(src, followlinks=False):
+        rel = Path(root).relative_to(src)
+        kept_dirs = []
+        for d in sorted(dirs):
+            p = Path(root) / d
+            if p.is_symlink():
+                pruned.append(str(p))
+                continue
+            kept_dirs.append(d)
+            (dst / rel / d).mkdir(parents=True, exist_ok=True)
+        dirs[:] = kept_dirs
+        for f in sorted(files):
+            p = Path(root) / f
+            st = p.lstat()
+            if stat.S_ISLNK(st.st_mode) or not stat.S_ISREG(st.st_mode) or st.st_nlink > 1:
+                pruned.append(str(p))
+                continue
+            shutil.copyfile(p, dst / rel / f, follow_symlinks=False)
+    for p in [dst, *sorted(dst.rglob("*"))]:
+        os.chown(p, 0, 0)
+        os.chmod(p, 0o755 if p.is_dir() else 0o644)
+    return {"pruned": pruned}
+
+
+def write_runner_inputs(inputs: list[dict], path: str | os.PathLike) -> None:
+    """Serialize the grade-time inputs for the candidate process. Carries NO targets."""
+    arrays: dict[str, np.ndarray] = {"n_realizations": np.array(len(inputs))}
+    for k, d in enumerate(inputs):
+        arrays[f"X_train_{k:03d}"] = d["X_train"]
+        arrays[f"t_train_{k:03d}"] = d["t_train"]
+        arrays[f"y_train_{k:03d}"] = d["y_train"]
+        arrays[f"X_test_{k:03d}"] = d["X_test"]
+    np.savez(path, **arrays)
+    os.chmod(path, 0o644)

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/intermediate.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+set -u
+
+mkdir -p /logs/verifier
+chown root:root /logs/verifier 2>/dev/null || true
+chmod 755 /logs/verifier 2>/dev/null || true
+rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json
+
+trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT
+
+chmod -R go-rwx /tests
+
+export GRADER_SPLIT=intermediate
+
+cd /tests
+/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA
+/usr/local/bin/python3 /tests/compute_reward.py
+
+exit 0

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements-verifier.txt

@@ -0,0 +1,5 @@
+iniconfig==2.3.0
+packaging==26.3
+pluggy==1.6.0
+pytest==8.3.5
+pytest-json-ctrf==0.3.5

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/requirements.txt

@@ -0,0 +1,17 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+filelock==3.32.3
+fsspec==2026.7.0
+Jinja2==3.1.6
+joblib==1.5.3
+MarkupSafe==3.0.3
+mpmath==1.3.0
+narwhals==2.24.0
+networkx==3.6.1
+numpy==2.5.2
+scikit-learn==1.9.0
+scipy==1.18.0
+setuptools==84.0.0
+sympy==1.14.0
+threadpoolctl==3.6.0
+torch==2.13.0+cpu
+typing_extensions==4.16.0

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/runner.py

@@ -0,0 +1,91 @@
+from __future__ import annotations
+
+import importlib.util
+import json
+import os
+import random
+import sys
+import time
+
+import numpy as np
+
+N_TEST = 75
+STATUS_OK, STATUS_RAISED, STATUS_BAD_SHAPE, STATUS_NON_FINITE = 0, 1, 2, 3
+
+
+def _pin_determinism() -> None:
+    """Mirror the OMP/MKL/OPENBLAS pins the launcher's env dict already sets: torch has its
+    own thread pool that a thread-count env var alone does not reliably bind, and any candidate
+    that draws from the global RNGs without its own seed needs a fixed starting state."""
+    random.seed(0)
+    np.random.seed(0)
+    try:
+        import torch
+        torch.manual_seed(0)
+        torch.set_num_threads(1)
+    except ImportError:
+        pass
+
+
+def main() -> int:
+    _pin_determinism()
+    inputs_path, solution_dir, out_path, meta_path = sys.argv[1:5]
+    meta = {"import_ok": False, "error": None, "n_calls": 0, "elapsed_sec": 0.0}
+
+    def dump_meta():
+        with open(meta_path, "w") as fh:
+            json.dump(meta, fh)
+
+    data = np.load(inputs_path, allow_pickle=False)
+    n = int(data["n_realizations"])
+    preds = np.zeros((n, N_TEST), dtype=np.float64)
+    status = np.full(n, STATUS_RAISED, dtype=np.int64)
+
+    entry_point = os.path.join(solution_dir, "estimator.py")
+    sys.path.insert(0, solution_dir)
+    try:
+        spec = importlib.util.spec_from_file_location("candidate_estimator", entry_point)
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        fn = getattr(module, "estimate_cate")
+        if not callable(fn):
+            raise TypeError("estimate_cate is not callable")
+        meta["import_ok"] = True
+    except BaseException as exc:
+        meta["error"] = f"{type(exc).__name__}: {exc}"[:500]
+        dump_meta()
+        np.savez(out_path, preds=preds, status=status)
+        return 0
+
+    t0 = time.monotonic()
+    for k in range(n):
+        X_train = np.array(data[f"X_train_{k:03d}"], dtype=np.float64)
+        t_train = np.array(data[f"t_train_{k:03d}"], dtype=np.float64)
+        y_train = np.array(data[f"y_train_{k:03d}"], dtype=np.float64)
+        X_test = np.array(data[f"X_test_{k:03d}"], dtype=np.float64)
+        try:
+            out = fn(X_train, t_train, y_train, X_test)
+            arr = np.asarray(out, dtype=np.float64)
+            if arr.shape != (X_test.shape[0],):
+                status[k] = STATUS_BAD_SHAPE
+                continue
+            if not np.all(np.isfinite(arr)):
+                status[k] = STATUS_NON_FINITE
+                continue
+            preds[k, : arr.shape[0]] = arr
+            status[k] = STATUS_OK
+        except BaseException as exc:
+            status[k] = STATUS_RAISED
+            if meta["error"] is None:
+                meta["error"] = f"call {k}: {type(exc).__name__}: {exc}"[:500]
+        finally:
+            meta["n_calls"] = k + 1
+
+    meta["elapsed_sec"] = time.monotonic() - t0
+    np.savez(out_path, preds=preds, status=status)
+    dump_meta()
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+set -u
+
+mkdir -p /logs/verifier
+chown root:root /logs/verifier 2>/dev/null || true
+chmod 755 /logs/verifier 2>/dev/null || true
+rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json
+
+trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT
+
+chmod -R go-rwx /tests
+
+export GRADER_SPLIT=final
+
+cd /tests
+/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA
+/usr/local/bin/python3 /tests/compute_reward.py
+
+exit 0

tasks/causalpfn-cate-pehe-ihdp-surfaceb/tests/test_state.py

@@ -0,0 +1,208 @@
+from __future__ import annotations
+
+import json
+import os
+import signal
+import stat
+import subprocess
+import sys
+import tempfile
+import time
+from pathlib import Path
+
+import numpy as np
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+import grader_core as gc
+
+TESTS_DIR = Path(__file__).resolve().parent
+SPLIT = os.environ.get("GRADER_SPLIT", "final")
+PANEL_PATH = TESTS_DIR / "hidden_data" / SPLIT / "panel.npz"
+ARTIFACT_DIR = Path("/app/output")
+LOG_DIR = Path("/logs/verifier")
+
+PYTHON = "/usr/local/bin/python3"
+RUNUSER = "/usr/sbin/runuser"
+AGENT_USER = "agent"
+AGENT_UID = 1001
+AGENT_GID = 1001
+
+
+def _write_metric(payload: dict) -> None:
+    LOG_DIR.mkdir(parents=True, exist_ok=True)
+    (LOG_DIR / "metric.json").write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n")
+
+
+def _reap_agent_processes() -> None:
+    """Kill anything still running as the candidate uid, without shelling out."""
+    for entry in Path("/proc").iterdir():
+        if not entry.name.isdigit():
+            continue
+        try:
+            if entry.stat().st_uid != AGENT_UID:
+                continue
+            os.kill(int(entry.name), signal.SIGKILL)
+        except (OSError, ValueError):
+            continue
+
+
+def _run_candidate(solution_dir: Path, inputs_path: Path, work_dir: Path) -> dict:
+    """Launch runner.py as uid 1001 and time it from this process, around the subprocess
+    boundary. The candidate never reports its own duration and never runs in this process."""
+    harness_dir = work_dir.parent / "harness"
+    harness_dir.mkdir(parents=True, exist_ok=True)
+    runner = harness_dir / "runner.py"
+    runner.write_bytes((TESTS_DIR / "runner.py").read_bytes())
+    os.chmod(harness_dir, 0o755)
+    os.chmod(runner, 0o644)
+
+    preds_path = work_dir / "predictions.npz"
+    meta_path = work_dir / "meta.json"
+    env_pairs = [
+        "PATH=/usr/local/bin:/usr/bin:/bin",
+        f"HOME={work_dir}",
+        f"TMPDIR={work_dir}",
+        "PYTHONSAFEPATH=1",
+        "PYTHONDONTWRITEBYTECODE=1",
+        "PYTHONHASHSEED=0",
+        "OMP_NUM_THREADS=1",
+        "MKL_NUM_THREADS=1",
+        "OPENBLAS_NUM_THREADS=1",
+        "NUMEXPR_NUM_THREADS=1",
+    ]
+    cmd = [RUNUSER, "-u", AGENT_USER, "--", "/usr/bin/env", "-i", *env_pairs,
+           PYTHON, str(runner), str(inputs_path), str(solution_dir),
+           str(preds_path), str(meta_path)]
+
+    proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
+                            cwd=str(work_dir), text=True, start_new_session=True)
+    started = time.monotonic()
+    timed_out = False
+    try:
+        stdout, stderr = proc.communicate(timeout=gc.GRADED_RUN_BUDGET_SEC)
+    except subprocess.TimeoutExpired:
+        timed_out = True
+        try:
+            os.killpg(os.getpgid(proc.pid), signal.SIGKILL)
+        except OSError:
+            proc.kill()
+        try:
+            stdout, stderr = proc.communicate(timeout=120)
+        except subprocess.TimeoutExpired:
+            stdout, stderr = "", ""
+    elapsed = time.monotonic() - started
+    _reap_agent_processes()
+
+    return {
+        "timed_out": timed_out,
+        "elapsed_sec": elapsed,
+        "returncode": proc.returncode,
+        "stderr_tail": (stderr or "")[-800:],
+        "preds_path": preds_path,
+        "meta_path": meta_path,
+    }
+
+
+def grade() -> dict:
+    payload = {
+        "split": SPLIT,
+        "metric_name": "mean_pehe",
+        "metric": None,
+        "reward": 0.0,
+        "valid": False,
+        "reason": "",
+        "n_realizations": 0,
+        "n_ok": 0,
+        "n_fallback": 0,
+        "elapsed_sec": 0.0,
+        "pruned_paths": [],
+    }
+    if not PANEL_PATH.is_file():
+        payload["reason"] = f"sealed panel missing at {PANEL_PATH}"
+        return payload
+    if not ARTIFACT_DIR.is_dir():
+        payload["reason"] = "no deliverable directory at /app/output"
+        return payload
+
+    inputs, taus = gc.load_sealed_panel(PANEL_PATH)
+    payload["n_realizations"] = len(inputs)
+
+    sandbox = Path(tempfile.mkdtemp(prefix="grade_", dir="/tmp"))
+    os.chmod(sandbox, 0o755)
+    solution_dir = sandbox / "solution"
+    work_dir = sandbox / "work"
+    work_dir.mkdir(parents=True, exist_ok=True)
+    os.chown(work_dir, AGENT_UID, AGENT_GID)
+    os.chmod(work_dir, 0o755)
+
+    copy_report = gc.copy_solution_tree(ARTIFACT_DIR, solution_dir)
+    payload["pruned_paths"] = copy_report["pruned"][:20]
+
+    inputs_path = sandbox / "inputs.npz"
+    gc.write_runner_inputs(inputs, inputs_path)
+
+    run = _run_candidate(solution_dir, inputs_path, work_dir)
+    payload["elapsed_sec"] = round(run["elapsed_sec"], 3)
+
+    if run["timed_out"]:
+        payload["reason"] = (f"graded run exceeded the {gc.GRADED_RUN_BUDGET_SEC:.0f} s "
+                             "wall-clock budget and was killed")
+        return payload
+
+    meta = {}
+    try:
+        meta = json.loads(Path(run["meta_path"]).read_text())
+    except Exception as exc:
+        payload["reason"] = f"candidate wrote no usable run metadata: {exc}"
+    if not meta.get("import_ok", False):
+        payload["reason"] = payload["reason"] or (
+            f"entry point could not be imported/called: {meta.get('error')}")
+        return payload
+
+    preds_path = Path(run["preds_path"])
+    try:
+        st = preds_path.lstat()
+        if not stat.S_ISREG(st.st_mode) or st.st_size > 200_000_000:
+            payload["reason"] = "predictions file is not a plain, plausibly-sized array file"
+            return payload
+    except OSError as exc:
+        payload["reason"] = f"candidate wrote no predictions: {exc}"
+        return payload
+
+    try:
+        with np.load(preds_path, allow_pickle=False) as f:
+            preds = np.asarray(f["preds"], dtype=np.float64)
+            status = np.asarray(f["status"], dtype=np.int64)
+    except Exception as exc:
+        payload["reason"] = f"predictions unreadable: {exc}"
+        return payload
+
+    if preds.shape != (len(inputs), gc.N_TEST) or status.shape != (len(inputs),):
+        payload["reason"] = "prediction array has the wrong shape"
+        return payload
+
+    scored = gc.score_panel(inputs, taus, preds, status)
+    if scored["n_ok"] == 0:
+        payload["n_ok"] = 0
+        payload["n_fallback"] = scored["n_fallback"]
+        payload["reason"] = f"every call failed: {meta.get('error')}"
+        return payload
+    payload.update(scored)
+
+    payload["valid"] = True
+    payload["reward"] = gc.graded_reward(scored["metric"], True)
+    payload["reason"] = "ok"
+    return payload
+
+
+def test_candidate_estimator_scores_the_sealed_panel():
+    """Execute /app/output/estimator.py on the sealed panel for this split and record the
+    mean PEHE and the mapped reward. Written on every path, including failure."""
+    payload = grade()
+    _write_metric(payload)
+    metric = "n/a" if payload["metric"] is None else f"{payload['metric']:.4f}"
+    print(f"split={payload['split']} realizations={payload['n_realizations']} "
+          f"mean_PEHE={metric} calls_ok={payload['n_ok']} "
+          f"calls_scored_as_constant={payload['n_fallback']} "
+          f"run_seconds={payload['elapsed_sec']:.1f} status={payload['reason']}")
+    assert payload["valid"], payload["reason"]

tasks/cpu-llm-decode-throughput/environment/Dockerfile

@@ -0,0 +1,47 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+RUN set -eu; \
+    if [ ! -s /etc/ssl/certs/ca-certificates.crt ]; then \
+      apt-get update && apt-get install -y --no-install-recommends ca-certificates=20230311+deb12u1; \
+    fi; \
+    rm -f /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian/20260801T000000Z bookworm main' >> /tmp/hz-prereq.list; \
+    echo 'deb [signed-by=/usr/share/keyrings/debian-archive-keyring.gpg] https://snapshot.debian.org/archive/debian-security/20260801T000000Z bookworm-security main' >> /tmp/hz-prereq.list; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false update; \
+    apt-get -o Dir::Etc::sourcelist=/tmp/hz-prereq.list -o Dir::Etc::sourceparts=/dev/null -o Acquire::Check-Valid-Until=false install -y --no-install-recommends tmux=3.3a-3 asciinema=2.2.0-1 git=1:2.39.5-0+deb12u3; \
+    rm -f /tmp/hz-prereq.list; rm -rf /var/lib/apt/lists/*
+
+
+ENTRYPOINT []
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=8 \
+    MKL_NUM_THREADS=8 \
+    OPENBLAS_NUM_THREADS=8
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir -r /tmp/requirements.txt \
+    && rm /tmp/requirements.txt
+
+RUN /usr/local/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='openai-community/gpt2', revision='607a30d783dfa663caf39e06633721c8d4cfcd7e', allow_patterns=['model.safetensors', 'config.json'], local_dir='/opt/model')" \
+    && rm -rf /opt/model/.cache \
+    && find /opt/model -type d -exec chmod 755 {} + \
+    && find /opt/model -type f -exec chmod 644 {} +
+
+WORKDIR /app
+COPY ./app /app
+
+RUN mkdir -p /app/output \
+    && chown -R agent:agent /app \
+    && find /app -exec touch -d @1700000000 {} +
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1
+
+RUN git config --system user.email "agent@localhost" \
+    && git config --system user.name "autoresearch agent"
+USER agent

tasks/cpu-llm-decode-throughput/environment/app/data/dev_workload_0.json

@@ -0,0 +1 @@
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tasks/cpu-llm-decode-throughput/environment/app/data/dev_workload_1.json

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tasks/cpu-llm-decode-throughput/environment/app/data/dev_workload_2.json

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tasks/cpu-llm-decode-throughput/environment/app/data/dev_workload_3.json

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tasks/cpu-llm-decode-throughput/environment/app/data/warmup_workload.json

@@ -0,0 +1 @@
+[{"id": 0, "prompt_token_ids": [290, 4987, 326, 262, 2551, 284, 787, 683, 609, 38453, 373, 3148, 764, 27812, 3000, 7311, 635, 16476, 319, 428, 2478, 837, 4585, 911, 1134, 46893, 705, 82, 22106, 355, 366, 281, 7485, 4293, 366, 530, 286, 262, 11330, 286, 262, 10389, 764, 554, 257, 8840, 2423, 286, 4471, 17501, 837, 287, 543, 262, 4365, 284, 19818, 37527, 468, 4054, 290, 262, 1866, 286, 911, 1134, 46893, 705, 82, 1074, 547, 19475, 6686, 837, 28730, 42086, 262, 366, 1049, 2589, 366, 287, 262, 4471, 287, 543, 911, 1134, 46893, 2540, 284, 3960, 706, 4673, 465, 2460, 481, 8551, 837, 290, 6875, 326, 339, 481, 307, 257, 1365, 3554, 329, 262, 11060, 286, 465, 2460, 764, 49755, 11058, 705, 82, 10799, 3998, 13943, 465, 366, 15163, 7654, 366, 837, 4585, 683, 366, 530, 286, 262, 749, 13899, 3435, 287, 262, 2168, 366, 290, 366, 530, 286, 262, 1178, 4988, 10457, 8486, 287, 427, 13090, 38572, 11984, 366, 764, 22254, 49902, 260], "max_new_tokens": 4}, {"id": 1, "prompt_token_ids": [1871, 883, 1944, 287, 262, 41756, 547, 8187, 276, 3701, 25656, 544, 290, 2520, 5510, 40020, 8213, 837, 355, 880, 355, 25656, 666, 6093, 3701, 4809, 5795, 290, 4664, 3790, 422, 584, 2422, 4991, 764, 4176, 290, 1811, 1230, 14138, 635, 9141, 837, 1390, 350, 509, 5719, 4587, 347, 2645, 837, 262, 763, 2488, 12, 31, 4139, 286, 8708, 10665, 764, 220, 198, 11325, 1757, 390, 18133, 2921, 257, 39362, 36706, 644, 339, 3417, 355, 257, 366, 21815, 3101, 9550, 366, 422, 11292, 764, 366, 15658, 508, 6622, 13626, 262, 16247, 286, 1692, 1204, 460, 307], "max_new_tokens": 4}, {"id": 2, "prompt_token_ids": [2756, 2620, 422, 2813, 284, 2211, 764, 220, 198, 796, 796, 3948, 590, 796, 796, 220, 198, 347, 600, 15712, 318, 7997, 416, 347, 600, 15712, 17521, 5852, 357, 350, 13, 24894], "max_new_tokens": 4}, {"id": 3, "prompt_token_ids": [262, 34316, 49904, 837, 1864, 284, 543, 47880, 74, 3214, 4291, 465, 736, 422, 262, 3726, 290, 750, 407, 1210, 1141, 262, 2121, 764, 383, 44851, 5052, 4987, 351, 262, 1812, 8123, 319, 617, 286, 465, 2173, 764, 220, 198, 554, 465, 9605, 4046, 837, 262, 1812, 8123, 1965, 262, 2184, 284, 18615, 378, 347, 306, 1169, 329, 642, 812, 837, 8512, 326, 366, 772, 1751, 287, 262, 38717, 389, 3910, 326, 257, 2121, 422, 6001, 743, 1085, 284, 281, 5095, 764, 366, 220, 198, 317, 47880, 74, 1641, 8852, 531, 326, 1912, 319, 262, 4973, 49904, 837, 262, 1641, 750, 407, 1975, 326, 347, 306, 1169, 373, 9944, 4497, 764, 679, 3767, 326, 339, 561, 407, 307, 5925, 26336, 262, 9167, 2033, 837, 355, 645, 1637, 714, 6330, 262, 2994, 262, 1641, 468, 6989, 764, 679, 2252, 2087, 326, 7806, 550, 3724, 319, 465, 2988, 705, 82, 10955, 837, 290, 465, 2802, 468, 25578, 1716, 257, 43557, 5827, 5906], "max_new_tokens": 4}, {"id": 4, "prompt_token_ids": [284, 1011, 5485, 1088, 262, 1748, 764, 220, 198, 796, 796, 796, 3517, 4560, 796, 796, 796, 220, 198, 3517, 5428, 3386, 287, 2258, 2253, 547, 7525, 8165, 510, 351, 262, 30207], "max_new_tokens": 4}, {"id": 5, "prompt_token_ids": [18222, 734, 2745, 1568, 329, 262, 15247, 10749, 319, 2439, 441, 8048, 5145, 837, 810, 11732, 23955, 1839, 706, 3932, 30711, 3804, 503, 284, 11732, 23955, 705, 82, 8886, 278, 20501, 13656, 14498, 1745, 764, 220, 198, 383, 23740, 7171, 1785, 7498, 262, 717, 640, 11732, 23955, 1138], "max_new_tokens": 4}, {"id": 6, "prompt_token_ids": [19878, 15858, 1961, 366, 837, 366, 17306, 8808, 1961, 366, 837, 290, 366, 8740, 4061, 6489, 1961, 366, 287, 41429, 319, 262, 12436, 10162, 764, 19478, 14443, 736, 379, 262, 7541, 284, 2193, 326, 673, 468, 2923, 5223, 764, 220, 198, 19478, 5860, 284, 4427, 376, 1381, 757, 837, 5137, 510, 465, 2104, 720, 513, 2488, 11, 31, 12877, 10171, 319, 257, 2060, 983], "max_new_tokens": 4}, {"id": 7, "prompt_token_ids": [2057, 884, 355, 406, 4730, 64, 837, 25910, 482, 337, 1453, 837, 449, 6909, 337, 1453, 837, 21624, 315, 350, 648, 28284, 837, 257, 271, 7365, 84, 1413, 333, 837, 269, 437], "max_new_tokens": 4}]

tasks/cpu-llm-decode-throughput/environment/app/harness/bench.py

@@ -0,0 +1,81 @@
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import sys
+import tempfile
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import metric_core
+
+DATA_DIR = "/app/data"
+REFERENCE = "/app/reference/reference_engine.py"
+MODEL_DIR = "/opt/model"
+WARMUP = "/app/data/warmup_workload.json"
+
+
+def main(argv=None):
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--engine", default="/app/output/engine.py")
+    ap.add_argument("--draws", default="0", help="comma-separated dev workload indices, or 'all'")
+    ap.add_argument("--limit", type=int, default=0, help="use only the first N requests")
+    args = ap.parse_args(argv)
+
+    indices = [0, 1, 2, 3] if args.draws == "all" else [int(x) for x in args.draws.split(",")]
+    workloads = []
+    for i in indices:
+        path = os.path.join(DATA_DIR, f"dev_workload_{i}.json")
+        with open(path) as fh:
+            reqs = json.load(fh)
+        if args.limit:
+            reqs = reqs[: args.limit]
+        workloads.append((f"dev_workload_{i}", reqs))
+
+    if not os.path.isfile(args.engine):
+        print(f"no engine at {args.engine}")
+        return 1
+
+    with tempfile.TemporaryDirectory(prefix="bench-") as work_root:
+        staged = os.path.join(work_root, "candidate")
+        shutil.copytree(os.path.dirname(os.path.abspath(args.engine)), staged)
+        for root, dirs, files in os.walk(staged):
+            for name in files:
+                os.chmod(os.path.join(root, name), 0o400)
+            os.chmod(root, 0o500)
+        rounds = metric_core.run_rounds(
+            REFERENCE,
+            os.path.join(staged, os.path.basename(args.engine)),
+            MODEL_DIR,
+            WARMUP,
+            workloads,
+            work_root,
+        )
+        for root, dirs, files in os.walk(staged):
+            os.chmod(root, 0o700)
+            for name in files:
+                os.chmod(os.path.join(root, name), 0o600)
+    summary = metric_core.summarise(rounds)
+    print()
+    for r in rounds:
+        if r.get("ok"):
+            print(
+                f"  {r['workload']}: speedup {r['speedup']:.3f}x  "
+                f"(reference {r['reference_s']:.2f}s, candidate {r['candidate_s']:.2f}s, "
+                f"agreement {r['matched_positions']}/{r['total_positions']})"
+            )
+        else:
+            print(f"  {r['workload']}: FAILED - {r.get('detail')}")
+    print()
+    print(f"token agreement : {summary['agreement']:.4f} "
+          f"(must be >= {metric_core.AGREEMENT_MIN})")
+    print(f"speedup S       : {summary['speedup']:.4f}")
+    if summary["detail"]:
+        print(f"note            : {summary['detail']}")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

tasks/cpu-llm-decode-throughput/environment/app/harness/metric_core.py

@@ -0,0 +1,202 @@
+from __future__ import annotations
+
+import os
+import shutil
+import statistics
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import runner
+
+VOCAB_SIZE = 50257
+
+LOAD_BUDGET_S = 180.0
+
+RUN_BUDGET_MULTIPLIER = 2.0
+RUN_BUDGET_FLOOR_S = 60.0
+REFERENCE_RUN_BUDGET_S = 900.0
+
+AGREEMENT_MIN = 0.99
+
+
+def validate_outputs(outputs, requests):
+    """Return (ok, detail). Outputs are untrusted data, so every shape is checked."""
+    if not isinstance(outputs, list):
+        return False, "engine did not return a list"
+    if len(outputs) != len(requests):
+        return False, f"engine returned {len(outputs)} lists, expected {len(requests)}"
+    for i, (row, req) in enumerate(zip(outputs, requests)):
+        want = int(req["max_new_tokens"])
+        if not isinstance(row, list):
+            return False, f"element {i} is not a list"
+        if len(row) != want:
+            return False, f"element {i} has {len(row)} token ids, expected {want}"
+        for tid in row:
+            if not isinstance(tid, int) or isinstance(tid, bool):
+                return False, f"element {i} contains a non-integer token id"
+            if not 0 <= tid < VOCAB_SIZE:
+                return False, f"element {i} contains out-of-range token id {tid}"
+    return True, ""
+
+
+def count_agreement(outputs, reference):
+    """Position-wise agreement between two valid output sets."""
+    matched = total = 0
+    for row, ref in zip(outputs, reference):
+        total += len(ref)
+        matched += sum(1 for a, b in zip(row, ref) if a == b)
+    return matched, total
+
+
+def tokens_per_second(requests, elapsed_s):
+    return sum(int(r["max_new_tokens"]) for r in requests) / elapsed_s
+
+
+def _fresh_dir(path, owner=None):
+    shutil.rmtree(path, ignore_errors=True)
+    os.makedirs(path, mode=0o700, exist_ok=True)
+    if owner is not None:
+        os.chown(path, owner[0], owner[1])
+    return path
+
+
+def run_rounds(
+    reference_engine_path,
+    candidate_engine_path,
+    model_dir,
+    warmup_path,
+    workloads,
+    work_root,
+    cmd_prefix=(),
+    runner_path=None,
+    run_dir_owner=None,
+    reap_uid=None,
+    log=print,
+):
+    """Interleave reference and candidate over one distinct workload per round.
+
+    Each round is a fresh process pair on a workload the previous round never saw, so
+    nothing carried over from an earlier round can stand in for the work. The reference
+    run's directory is destroyed before the candidate is launched, so the candidate
+    cannot read the answer off the run that just produced it.
+
+    Returns a list of per-round dicts; a round with ``ok`` false stops the comparison.
+    """
+    rounds = []
+    for index, (name, requests) in enumerate(workloads):
+        ref_dir = _fresh_dir(os.path.join(work_root, f"round{index}_reference"), run_dir_owner)
+        cand_dir = _fresh_dir(os.path.join(work_root, f"round{index}_candidate"), run_dir_owner)
+
+        ref = runner.run_timed(
+            reference_engine_path,
+            model_dir,
+            warmup_path,
+            requests,
+            ref_dir,
+            LOAD_BUDGET_S,
+            REFERENCE_RUN_BUDGET_S,
+            cmd_prefix=cmd_prefix,
+            runner_path=runner_path,
+            reap_uid=reap_uid,
+        )
+        if not ref.ok:
+            return rounds + [
+                {"round": index, "workload": name, "ok": False,
+                 "detail": f"reference run failed: {ref.detail}"}
+            ]
+        ref_ok, ref_detail = validate_outputs(ref.outputs, requests)
+        if not ref_ok:
+            return rounds + [
+                {"round": index, "workload": name, "ok": False,
+                 "detail": f"reference run invalid: {ref_detail}"}
+            ]
+        shutil.rmtree(ref_dir, ignore_errors=True)
+
+        budget = max(RUN_BUDGET_FLOOR_S, RUN_BUDGET_MULTIPLIER * ref.elapsed_s)
+        cand = runner.run_timed(
+            candidate_engine_path,
+            model_dir,
+            warmup_path,
+            requests,
+            cand_dir,
+            LOAD_BUDGET_S,
+            budget,
+            cmd_prefix=cmd_prefix,
+            runner_path=runner_path,
+            reap_uid=reap_uid,
+        )
+        record = {
+            "round": index,
+            "workload": name,
+            "new_tokens": sum(int(r["max_new_tokens"]) for r in requests),
+            "reference_s": round(ref.elapsed_s, 4),
+            "reference_tok_s": round(tokens_per_second(requests, ref.elapsed_s), 3),
+            "budget_s": round(budget, 2),
+        }
+        if not cand.ok:
+            record.update({"ok": False, "detail": cand.detail})
+            rounds.append(record)
+            log(f"round {index} ({name}): candidate failed - {cand.detail}")
+            return rounds
+
+        ok, detail = validate_outputs(cand.outputs, requests)
+        if not ok:
+            record.update({"ok": False, "detail": detail})
+            rounds.append(record)
+            log(f"round {index} ({name}): candidate invalid - {detail}")
+            return rounds
+
+        matched, total = count_agreement(cand.outputs, ref.outputs)
+        record.update(
+            {
+                "ok": True,
+                "detail": "",
+                "candidate_s": round(cand.elapsed_s, 4),
+                "candidate_tok_s": round(tokens_per_second(requests, cand.elapsed_s), 3),
+                "speedup": round(ref.elapsed_s / cand.elapsed_s, 4),
+                "matched_positions": matched,
+                "total_positions": total,
+            }
+        )
+        rounds.append(record)
+        log(
+            f"round {index} ({name}): reference {record['reference_tok_s']} tok/s, "
+            f"candidate {record['candidate_tok_s']} tok/s, "
+            f"speedup {record['speedup']}x, agreement {matched}/{total}"
+        )
+        shutil.rmtree(cand_dir, ignore_errors=True)
+    return rounds
+
+
+def summarise(rounds):
+    """Fold per-round records into the graded speedup S and the agreement rate."""
+    if not rounds or not all(r.get("ok") for r in rounds):
+        failed = next((r for r in rounds if not r.get("ok")), None)
+        return {
+            "speedup": 0.0,
+            "agreement": 0.0,
+            "valid": False,
+            "detail": (failed or {}).get("detail", "no rounds were run"),
+            "rounds": rounds,
+        }
+    matched = sum(r["matched_positions"] for r in rounds)
+    total = sum(r["total_positions"] for r in rounds)
+    agreement = matched / total if total else 0.0
+    speedups = [r["speedup"] for r in rounds]
+    if agreement < AGREEMENT_MIN:
+        return {
+            "speedup": 0.0,
+            "agreement": agreement,
+            "valid": True,
+            "detail": f"token agreement {agreement:.4f} is below {AGREEMENT_MIN}",
+            "rounds": rounds,
+        }
+    return {
+        "speedup": statistics.median(speedups),
+        "agreement": agreement,
+        "valid": True,
+        "detail": "",
+        "per_round_speedup": speedups,
+        "rounds": rounds,
+    }

tasks/cpu-llm-decode-throughput/environment/app/harness/runner.py

@@ -0,0 +1,216 @@
+from __future__ import annotations
+
+import json
+import os
+import signal
+import subprocess
+import sys
+import threading
+import time
+
+READY_POLL_S = 0.002
+
+
+
+
+def _child(engine_path, model_dir, warmup_path, results_path, ready_path):
+    import importlib.util
+
+    os.environ["HOME"] = os.getcwd()
+
+    import torch
+
+    torch.set_num_threads(len(os.sched_getaffinity(0)))
+    torch.manual_seed(0)
+
+    sys.path.insert(0, os.path.dirname(os.path.abspath(engine_path)))
+    spec = importlib.util.spec_from_file_location("graded_engine", engine_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+
+    engine = module.Engine(model_dir)
+    with open(warmup_path) as fh:
+        engine.generate(json.load(fh))
+
+    with open(ready_path, "w") as fh:
+        fh.write("ready")
+        fh.flush()
+        os.fsync(fh.fileno())
+
+    payload = sys.stdin.buffer.read()
+    requests = json.loads(payload.decode("utf-8"))
+
+    result = engine.generate(requests)
+
+    with open(results_path, "w") as fh:
+        json.dump(result, fh)
+        fh.flush()
+        os.fsync(fh.fileno())
+    sys.stderr.flush()
+    os._exit(0)
+
+
+
+def _kill_uid(uid):
+    """SIGKILL every process still running as `uid`.
+
+    A descendant that calls setsid() leaves the process group and survives killpg; this
+    is the backstop, so nothing a finished run left behind can consume cores during the
+    next one.
+    """
+    for entry in os.listdir("/proc"):
+        if not entry.isdigit():
+            continue
+        try:
+            if os.stat(os.path.join("/proc", entry)).st_uid != uid:
+                continue
+            os.kill(int(entry), signal.SIGKILL)
+        except (OSError, ProcessLookupError):
+            continue
+
+
+
+
+class RunOutcome:
+    """What one timed run produced. ``ok`` false means the run scores nothing."""
+
+    def __init__(self, ok, elapsed_s, outputs, detail):
+        self.ok = ok
+        self.elapsed_s = elapsed_s
+        self.outputs = outputs
+        self.detail = detail
+
+    def __repr__(self):
+        return f"RunOutcome(ok={self.ok}, elapsed_s={self.elapsed_s}, detail={self.detail!r})"
+
+
+def run_timed(
+    engine_path,
+    model_dir,
+    warmup_path,
+    workload,
+    run_dir,
+    load_budget_s,
+    run_budget_s,
+    cmd_prefix=(),
+    env_extra=None,
+    runner_path=None,
+    reap_uid=None,
+):
+    """Run one engine over one workload in a fresh process and time only generate().
+
+    ``cmd_prefix`` lets a privileged caller wrap the launch (e.g. drop to another uid),
+    and ``runner_path`` lets it point the child at a copy of this file that the reduced
+    uid can actually read. Returns a :class:`RunOutcome`; every failure returns ok=False.
+    """
+    results_path = os.path.join(run_dir, "results.json")
+    ready_path = os.path.join(run_dir, "ready.marker")
+    log_path = os.path.join(run_dir, "engine.log")
+    for stale in (results_path, ready_path):
+        if os.path.exists(stale):
+            os.unlink(stale)
+
+    env = {
+        "PATH": "/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/bin:/sbin",
+        "HOME": run_dir,
+        "TMPDIR": run_dir,
+        "PYTHONHASHSEED": "0",
+        "PYTHONDONTWRITEBYTECODE": "1",
+        "PYTHONNOUSERSITE": "1",
+        "PYTHONSAFEPATH": "1",
+        "OMP_NUM_THREADS": str(len(os.sched_getaffinity(0))),
+        "MKL_NUM_THREADS": str(len(os.sched_getaffinity(0))),
+        "OPENBLAS_NUM_THREADS": str(len(os.sched_getaffinity(0))),
+        "HF_HUB_OFFLINE": "1",
+        "TRANSFORMERS_OFFLINE": "1",
+    }
+    env.update(env_extra or {})
+
+    argv = list(cmd_prefix) + [
+        sys.executable,
+        "-s",
+        "-P",
+        runner_path or os.path.abspath(__file__),
+        "--child",
+        engine_path,
+        model_dir,
+        warmup_path,
+        results_path,
+        ready_path,
+    ]
+    payload = json.dumps(workload).encode("utf-8")
+
+    log = open(log_path, "wb")
+    proc = subprocess.Popen(
+        argv,
+        stdin=subprocess.PIPE,
+        stdout=log,
+        stderr=log,
+        cwd=run_dir,
+        env=env,
+        start_new_session=True,
+    )
+
+    pgid = proc.pid
+
+    def _reap():
+        try:
+            os.killpg(pgid, signal.SIGKILL)
+        except (ProcessLookupError, PermissionError):
+            pass
+        try:
+            proc.wait(timeout=30)
+        except subprocess.TimeoutExpired:
+            pass
+        if reap_uid is not None:
+            _kill_uid(reap_uid)
+        log.close()
+
+    deadline = time.monotonic() + load_budget_s
+    while not os.path.exists(ready_path):
+        if proc.poll() is not None:
+            _reap()
+            return RunOutcome(False, None, None, "engine exited before it was ready")
+        if time.monotonic() > deadline:
+            _reap()
+            return RunOutcome(False, None, None, "engine exceeded the load budget")
+        time.sleep(READY_POLL_S)
+
+    def _feed():
+        try:
+            proc.stdin.write(payload)
+            proc.stdin.close()
+        except (BrokenPipeError, OSError, ValueError):
+            pass
+
+    started = time.perf_counter()
+    threading.Thread(target=_feed, daemon=True).start()
+    try:
+        returncode = proc.wait(timeout=run_budget_s)
+    except subprocess.TimeoutExpired:
+        _reap()
+        return RunOutcome(False, None, None, "engine exceeded the wall-clock budget")
+    elapsed = time.perf_counter() - started
+
+    try:
+        with open(results_path) as fh:
+            outputs = json.load(fh)
+    except (OSError, ValueError) as exc:
+        outputs = None
+        detail = f"unreadable results: {exc}"
+    else:
+        detail = ""
+    _reap()
+
+    if returncode != 0:
+        return RunOutcome(False, elapsed, None, f"engine exited with code {returncode}")
+    if outputs is None:
+        return RunOutcome(False, elapsed, None, detail)
+    return RunOutcome(True, elapsed, outputs, "")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) == 7 and sys.argv[1] == "--child":
+        _child(*sys.argv[2:])
+    else:
+        raise SystemExit("runner.py is launched by the harness, not directly")

tasks/cpu-llm-decode-throughput/environment/app/reference/reference_engine.py

@@ -0,0 +1,139 @@
+from __future__ import annotations
+
+import json
+import math
+import os
+
+import torch
+from safetensors.torch import load_file
+
+_SQRT_2_OVER_PI = math.sqrt(2.0 / math.pi)
+
+_BLOCK_KEYS = (
+    "ln_1.weight",
+    "ln_1.bias",
+    "attn.c_attn.weight",
+    "attn.c_attn.bias",
+    "attn.c_proj.weight",
+    "attn.c_proj.bias",
+    "ln_2.weight",
+    "ln_2.bias",
+    "mlp.c_fc.weight",
+    "mlp.c_fc.bias",
+    "mlp.c_proj.weight",
+    "mlp.c_proj.bias",
+)
+
+
+def gelu_new(x: torch.Tensor) -> torch.Tensor:
+    """The tanh-approximated GELU the checkpoint was trained with."""
+    return 0.5 * x * (1.0 + torch.tanh(_SQRT_2_OVER_PI * (x + 0.044715 * x * x * x)))
+
+
+class Engine:
+    """Serves generation requests one at a time."""
+
+    def __init__(self, model_dir: str) -> None:
+        with open(os.path.join(model_dir, "config.json")) as fh:
+            cfg = json.load(fh)
+        self.n_layer = int(cfg["n_layer"])
+        self.n_head = int(cfg["n_head"])
+        self.n_embd = int(cfg["n_embd"])
+        self.n_positions = int(cfg["n_positions"])
+        self.eps = float(cfg["layer_norm_epsilon"])
+        self.head_dim = self.n_embd // self.n_head
+        self.scale = 1.0 / math.sqrt(self.head_dim)
+
+        sd = load_file(os.path.join(model_dir, "model.safetensors"))
+        self.wte = sd["wte.weight"].float().contiguous()
+        self.wpe = sd["wpe.weight"].float().contiguous()
+        self.ln_f_w = sd["ln_f.weight"].float().contiguous()
+        self.ln_f_b = sd["ln_f.bias"].float().contiguous()
+        self.blocks = [
+            {k: sd[f"h.{i}.{k}"].float().contiguous() for k in _BLOCK_KEYS}
+            for i in range(self.n_layer)
+        ]
+
+
+    def _forward(self, ids: torch.Tensor, past: list, start_pos: int) -> torch.Tensor:
+        """One forward pass over `ids` ([1, T]); returns logits for the last position."""
+        bsz, seq = ids.shape
+        pos = torch.arange(start_pos, start_pos + seq, dtype=torch.long)
+        h = self.wte[ids] + self.wpe[pos]
+
+        past_len = 0 if past[0] is None else past[0][0].shape[2]
+        total = past_len + seq
+        mask = None
+        if seq > 1:
+            allowed = torch.arange(total)[None, :] <= torch.arange(
+                past_len, past_len + seq
+            )[:, None]
+            mask = torch.zeros(seq, total)
+            mask.masked_fill_(~allowed, float("-inf"))
+
+        for i, b in enumerate(self.blocks):
+            x = torch.nn.functional.layer_norm(
+                h, (self.n_embd,), b["ln_1.weight"], b["ln_1.bias"], self.eps
+            )
+            qkv = torch.addmm(
+                b["attn.c_attn.bias"],
+                x.view(bsz * seq, self.n_embd),
+                b["attn.c_attn.weight"],
+            ).view(bsz, seq, 3 * self.n_embd)
+            q, k, v = qkv.split(self.n_embd, dim=2)
+            q = q.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2)
+            k = k.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2)
+            v = v.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2)
+            if past[i] is not None:
+                k = torch.cat((past[i][0], k), dim=2)
+                v = torch.cat((past[i][1], v), dim=2)
+            past[i] = (k, v)
+
+            att = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+            if mask is not None:
+                att = att + mask
+            att = torch.softmax(att, dim=-1)
+            y = torch.matmul(att, v)
+            y = y.transpose(1, 2).reshape(bsz * seq, self.n_embd)
+            y = torch.addmm(b["attn.c_proj.bias"], y, b["attn.c_proj.weight"])
+            h = h + y.view(bsz, seq, self.n_embd)
+
+            x = torch.nn.functional.layer_norm(
+                h, (self.n_embd,), b["ln_2.weight"], b["ln_2.bias"], self.eps
+            )
+            x = torch.addmm(
+                b["mlp.c_fc.bias"], x.view(bsz * seq, self.n_embd), b["mlp.c_fc.weight"]
+            )
+            x = gelu_new(x)
+            x = torch.addmm(b["mlp.c_proj.bias"], x, b["mlp.c_proj.weight"])
+            h = h + x.view(bsz, seq, self.n_embd)
+
+        h = torch.nn.functional.layer_norm(
+            h[:, -1, :], (self.n_embd,), self.ln_f_w, self.ln_f_b, self.eps
+        )
+        return torch.matmul(h, self.wte.t())
+
+
+    @torch.inference_mode()
+    def generate(self, requests: list) -> list:
+        """Serve every request; returns one list of new token ids per request, in order."""
+        out = []
+        for req in requests:
+            prompt = list(req["prompt_token_ids"])
+            n_new = int(req["max_new_tokens"])
+            past = [None] * self.n_layer
+            ids = torch.tensor([prompt], dtype=torch.long)
+            logits = self._forward(ids, past, 0)
+            produced = []
+            pos = len(prompt)
+            for _ in range(n_new):
+                nxt = int(torch.argmax(logits[0]).item())
+                produced.append(nxt)
+                if len(produced) == n_new:
+                    break
+                logits = self._forward(
+                    torch.tensor([[nxt]], dtype=torch.long), past, pos
+                )
+                pos += 1
+            out.append(produced)
+        return out

tasks/cpu-llm-decode-throughput/environment/requirements.txt

@@ -0,0 +1,6 @@
+--index-url https://download.pytorch.org/whl/cpu
+--extra-index-url https://pypi.org/simple
+torch==2.13.0+cpu
+numpy==2.5.2
+safetensors==0.8.0
+huggingface_hub==1.28.0

tasks/cpu-llm-decode-throughput/hints/hint.md

@@ -0,0 +1 @@
+Serve the whole workload in one batched decode loop over a shared KV cache, grouping prefills by prompt length and swapping finished rows to the end so live rows stay contiguous. Construction and warm-up are untimed, so pre-allocate and pre-touch the KV pool there; then chunk the vocabulary projection with a running max and pre-pack the decode-only weights. Do not try low precision: the token-agreement gate is checked position by position and one divergent argmax corrupts the rest of that request.

tasks/cpu-llm-decode-throughput/tests/Dockerfile

@@ -0,0 +1,38 @@
+FROM --platform=linux/amd64 public.ecr.aws/docker/library/python:3.13-slim-bookworm@sha256:01f42367a0a94ad4bc17111776fd66e3500c1d87c15bbd6055b7371d39c124fb
+
+ENTRYPOINT []
+ENV PYTHONPATH=
+ENV PYTHONSAFEPATH=1
+ENV DEBIAN_FRONTEND=noninteractive \
+    PYTHONHASHSEED=0 \
+    PIP_NO_CACHE_DIR=1 \
+    PIP_DISABLE_PIP_VERSION_CHECK=1 \
+    OMP_NUM_THREADS=8 \
+    MKL_NUM_THREADS=8 \
+    OPENBLAS_NUM_THREADS=8
+
+RUN useradd --create-home --uid 1001 agent
+
+COPY ./requirements.txt /tmp/requirements.txt
+COPY ./requirements-verifier.txt /tmp/requirements-verifier.txt
+RUN /usr/local/bin/python3 -m pip install --no-cache-dir \
+        -r /tmp/requirements.txt -r /tmp/requirements-verifier.txt \
+    && rm /tmp/requirements.txt /tmp/requirements-verifier.txt
+
+RUN /usr/local/bin/python3 -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='openai-community/gpt2', revision='607a30d783dfa663caf39e06633721c8d4cfcd7e', allow_patterns=['model.safetensors', 'config.json'], local_dir='/opt/model')" \
+    && rm -rf /opt/model/.cache \
+    && find /opt/model -type d -exec chmod 755 {} + \
+    && find /opt/model -type f -exec chmod 644 {} +
+
+COPY . /tests
+
+RUN chown -R root:root /tests \
+    && chmod 700 /tests \
+    && chmod 700 /tests/hidden_data /tests/hidden_data/intermediate /tests/hidden_data/final \
+    && chmod +x /tests/test.sh /tests/intermediate.sh \
+    && find /tests -exec touch -d @1700000000 {} +
+
+WORKDIR /tests
+
+ENV HF_HUB_OFFLINE=1
+ENV TRANSFORMERS_OFFLINE=1

tasks/cpu-llm-decode-throughput/tests/compute_reward.py

@@ -0,0 +1,18 @@
+import json
+import math
+from pathlib import Path
+
+REWARD_PATH = Path("/logs/verifier/reward.txt")
+
+try:
+    REWARD_PATH.parent.mkdir(parents=True, exist_ok=True)
+    value = float(json.loads(Path("/logs/verifier/metric.json").read_text())["reward"])
+    if not math.isfinite(value):
+        raise ValueError("non-finite reward")
+    reward = f"{min(1.0, max(0.0, value)):.12g}"
+    REWARD_PATH.write_text(reward + "\n")
+    print(f"reward={reward}")
+except Exception as exc:
+    REWARD_PATH.parent.mkdir(parents=True, exist_ok=True)
+    REWARD_PATH.write_text("0\n")
+    print(f"reward=0 (fallback: {exc})")

tasks/cpu-llm-decode-throughput/tests/grader_core.py

@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+M0 = 1.0
+BASELINE_SCORE = 18.0
+
+
+def graded_reward(speedup, valid):
+    """Map the measured speedup to a reward in [0, 1]; 0 for anything not valid."""
+    if not valid:
+        return 0.0
+    x_ref = BASELINE_SCORE - M0
+    assert x_ref > 0, "malformed task: the baseline score does not beat the trivial baseline"
+    u = max(0.0, float(speedup) - M0) / x_ref
+    return u / (1.0 + u)

tasks/cpu-llm-decode-throughput/tests/hidden_data/final/workload_0.json

@@ -0,0 +1 @@
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tasks/cpu-llm-decode-throughput/tests/hidden_data/final/workload_1.json

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tasks/cpu-llm-decode-throughput/tests/hidden_data/final/workload_2.json

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tasks/cpu-llm-decode-throughput/tests/hidden_data/intermediate/workload_0.json

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tasks/cpu-llm-decode-throughput/tests/hidden_data/intermediate/workload_1.json

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tasks/cpu-llm-decode-throughput/tests/hidden_data/intermediate/workload_2.json

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"max_new_tokens": 64}, {"id": 115, "prompt_token_ids": [290, 6825, 1811, 1830, 764, 679, 11589, 4504, 287, 3095, 2488, 12, 31, 2693, 878, 852, 39632, 757, 351, 24824, 36744, 2428, 1566, 2739, 3267, 764, 554, 6119, 678, 837, 5108, 25494, 4966, 329, 9773, 5695, 290, 257, 10242, 572, 286, 838, 10732, 837, 5742, 262, 47534, 320, 418, 7433, 262, 11843, 14536, 287, 257, 4153, 784, 1511, 6611, 448, 764, 7945, 1719, 465, 1622, 9785, 21879, 6021, 416, 6821, 837, 5108, 25494, 5201, 3717, 351, 44084, 15795, 5695, 290, 1936, 17692, 319, 8275, 6370, 837, 355, 880, 355, 3598, 2041, 2488, 12, 31, 3466, 18034, 764, 679, 2497, 257, 5566, 4833, 2597, 355, 257, 9733, 837, 16508, 1160, 8318, 329, 691, 19755, 5695, 764, 220, 198, 796, 796, 796, 796, 3050, 1622, 796, 796, 796, 796, 220, 198, 5108, 25494, 6150, 287, 257, 5021, 2488, 12, 31, 4007, 2597, 287, 3050, 290, 373, 21487, 517, 6777, 287, 262, 6427, 983, 3688, 284, 262, 2180, 1622, 764, 679, 925, 257], "max_new_tokens": 8}, {"id": 116, "prompt_token_ids": [257, 4894, 2957, 416, 262, 12671, 3738, 21718, 705, 609, 333, 10757, 3981, 1437, 357, 838, 2488, 13, 31, 3439, 4201, 1267, 290, 2869, 705, 82, 11013, 18228, 28835, 993, 3301, 357, 838, 2488, 13, 31, 5014, 4201, 1267, 764, 3226, 262, 4019, 10856, 508, 14888, 287, 262, 2995, 837, 262, 609, 18425, 7500, 3849, 10307, 4317, 400, 764, 679, 750, 407, 5963, 284], "max_new_tokens": 40}, {"id": 117, "prompt_token_ids": [852, 262, 530, 508, 2921, 606, 262, 8390, 4116, 764, 775, 23542, 318, 1234, 287, 262, 7356, 2685, 837, 475, 339, 4952, 44498, 326, 339, 892, 484, 7977, 683, 706, 339, 373, 44634, 287, 262, 3606, 259, 1435, 1398, 764, 1375, 44846, 1811, 286, 262, 4393, 286, 262, 3710, 32373, 837, 508, 466, 1283, 8768, 287, 19786, 775, 23542, 764, 775, 23542, 3011, 12274, 837, 475, 14926, 30375, 683, 326, 257, 3710, 4522, 13570, 4572, 373, 1043, 287, 262, 17355, 1306, 284, 775, 23542, 564, 247, 264, 764, 44498, 290, 17161, 2740, 284, 257, 12370, 8705, 6240, 837, 508, 1139, 326, 340, 561, 307, 5340, 329, 775, 23542, 284, 307, 262, 26581, 764, 350, 528, 27671, 14926, 284, 1656, 319, 465, 905, 287, 281, 3071, 2041, 878, 14926, 30375, 44498, 326, 775, 23542, 564, 247, 264, 34290, 547, 1043, 477, 625, 262, 3645, 764, 220, 198, 14926, 290, 569, 49708, 4384, 319, 350, 528, 564, 247, 264, 5243, 905, 837], "max_new_tokens": 4}, {"id": 118, "prompt_token_ids": [837, 290, 366, 11526, 2094, 705, 83, 43771, 366, 764, 383, 4471, 3033, 262, 2457, 5585], "max_new_tokens": 40}, {"id": 119, "prompt_token_ids": [16640, 7781, 1542, 2173, 287, 262, 1592, 764, 17389, 1839, 511, 1306, 983, 618, 484, 9772, 9279, 764, 1550, 2805, 2608, 837, 2813, 837, 262, 34969, 4405, 4744, 8915, 784, 9166, 287, 17820, 284, 5160, 736, 2488, 12, 31, 284, 2488, 12, 31, 736, 13229, 284, 262, 8125, 6675, 764, 1550, 3035, 362, 837, 17389, 4405, 5891, 40933, 1074, 26706, 52, 4317, 784, 8190, 284, 787, 340, 284, 257, 1218, 12785, 2260, 12184, 983, 764, 1114, 262, 1218, 12785, 614, 837, 262, 34969, 3214, 287, 262, 2260, 12184, 983, 837, 428, 640, 284, 14493, 764, 220], "max_new_tokens": 40}, {"id": 120, "prompt_token_ids": [366, 764, 30588, 306, 1160, 2706, 837, 5989, 720, 4317, 1510, 837, 550, 511, 3186, 8096], "max_new_tokens": 24}, {"id": 121, "prompt_token_ids": [286, 43184, 45733, 531, 262, 5062, 318, 366, 881, 517, 7270, 351, 262, 2613, 324, 563, 290, 26071, 5238, 621, 663, 18476, 837, 366, 10820, 319, 262, 2610, 366, 311, 6548, 6894, 366, 339, 531, 366, 340, 5238, 588, 644, 38398, 1891, 714, 307, 611, 19800, 30088, 11893, 714, 3551, 257, 922, 32859, 366, 764, 554, 465, 2423, 837, 19800, 347, 789, 15494, 7911, 1867, 357, 1683, 1267, 1737, 355, 257, 366, 845, 7758, 29512, 290, 9857, 5062, 366, 12316, 326, 366, 340, 468, 257, 1310, 1223, 329, 2506, 764, 366, 15298, 837, 27616, 25305, 68], "max_new_tokens": 24}, {"id": 122, "prompt_token_ids": [2681, 1474, 39282, 837, 262, 6388, 6364, 32413, 981, 3867, 7421, 904, 764, 509, 4951, 64, 4166, 281, 4151, 290, 2627, 257, 37428, 2049, 878, 8871, 6026, 26361, 319, 3267, 3261, 764, 383, 6388, 24135, 625, 1956, 837, 475, 302, 2488, 12, 31, 32413, 625, 262, 2520, 2807, 6896, 837, 8978, 9103, 13520, 286, 6640, 10571, 1220, 289, 357, 4101, 16462, 1267, 319, 3389, 362, 572, 262, 26015, 7051, 286, 2807, 764, 38468, 509, 4951, 64, 29759, 290, 12956, 38206, 3403, 837, 7186, 287, 2068, 34992, 764, 2750, 3389, 513, 837, 340, 550, 24135, 284, 19690, 6388, 3722, 837, 290, 373, 645, 2392, 852, 7728, 319, 416, 262, 1306, 1110, 764, 554, 7840, 6026, 26361, 837, 509, 4951, 64, 9694, 3933, 2488, 11, 31, 12877, 7777, 837, 1390, 513, 2488, 11, 31, 12877, 326, 547, 6572, 837, 290, 4073, 1440, 30091, 764, 3334, 13520, 290, 25807, 1364, 350, 27019, 1510, 357, 19599, 837, 720, 718, 2488, 13, 31, 604, 1510], "max_new_tokens": 24}, {"id": 123, "prompt_token_ids": [837, 2592, 1863, 262, 4865, 351, 2688, 5045, 273, 764, 49631, 4028, 5091, 287, 1778, 1872, 837, 337, 4265, 705, 391, 290, 379, 22225, 397, 292, 282, 6081, 287, 3267, 7358, 764], "max_new_tokens": 8}, {"id": 124, "prompt_token_ids": [287, 2258, 2253, 319, 3426, 678, 837, 10249, 290, 287, 2869, 319, 3426, 1160, 837, 10249, 764, 2264, 441, 28512, 373, 1568, 2716, 355, 636, 286, 257, 18537, 1444, 383, 8519, 12251, 329, 18993, 287, 8235, 7848, 11312, 286, 37697, 764, 383, 983, 373, 635, 49702, 284, 262, 29490, 23135, 290, 2716, 11541, 287, 2869, 7848, 11312, 286, 37697, 757, 355, 636, 286, 262, 29490, 21449, 2168, 287, 7795, 837, 9080, 29490, 21449, 1058, 314, 5896, 27232, 21839, 764, 220, 198, 796, 796, 797, 4516, 796, 796, 220, 198, 2264, 441, 28512, 2722, 257, 4632, 3967, 2882, 422, 9188, 2402, 2650, 764, 3776, 27520, 654, 837, 281, 13262, 1352, 329, 2008, 983, 8088, 837, 8686, 262, 983, 257, 4776, 286, 8541, 4064, 1912, 319, 362, 8088, 764, 13421, 4624, 262, 983, 379, 1303, 767, 287, 511, 366, 5849, 13421, 9974, 5776, 286, 1439, 3862, 366, 1351, 764, 13421, 17760, 7093, 15342, 262, 983, 705, 82, 9382, 837, 475, 12318, 262], "max_new_tokens": 16}, {"id": 125, "prompt_token_ids": [4960, 2488, 12, 31, 12030, 21865, 837, 2807, 837, 290, 788, 7298, 23075, 71, 4696, 837], "max_new_tokens": 16}, {"id": 126, "prompt_token_ids": [9978, 925, 510, 286, 838, 2488, 11, 31, 12877, 1450, 764, 797, 8103, 262, 7404, 837], "max_new_tokens": 64}, {"id": 127, "prompt_token_ids": [550, 5201, 572, 262, 938, 286, 262, 10940, 287, 1105, 3720, 475, 550, 757, 4054, 284, 1011, 2869, 287, 1105, 6659, 764, 1320, 614, 837, 262, 42188, 23129, 1908, 1194, 4365, 284, 46856, 837, 11334, 17547, 1865, 757, 764, 383, 5481, 76, 2771, 5822, 373, 284, 3758, 465, 3478, 4664, 14138, 11791, 416, 530, 7319, 30891, 3790, 284, 262, 23129, 705, 82, 2184, 764, 357, 2080, 609, 13299, 837, 262, 23129, 20056, 262, 5822, 286, 609, 13299, 2241, 284, 11618, 764, 1267, 220, 198, 1629, 46856, 837, 13596, 776, 4449, 499, 378, 8958, 515, 351, 465], "max_new_tokens": 64}]

tasks/cpu-llm-decode-throughput/tests/intermediate.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+set -u
+
+mkdir -p /app/output /logs/verifier /grade
+chown root:root /logs/verifier 2>/dev/null || true
+chmod 755 /logs/verifier 2>/dev/null || true
+rm -f /logs/verifier/reward.txt /logs/verifier/ctrf.json /logs/verifier/metric.json
+
+trap 'if [ ! -s /logs/verifier/reward.txt ]; then echo 0 > /logs/verifier/reward.txt; fi' EXIT
+
+chmod -R go-rwx /tests
+
+export GRADE_SPLIT=intermediate
+
+cd /tests
+/usr/local/bin/python3 -m pytest --ctrf /logs/verifier/ctrf.json /tests/test_state.py -rA
+/usr/local/bin/python3 /tests/compute_reward.py > /dev/null
+
+exit 0

tasks/cpu-llm-decode-throughput/tests/metric_core.py

@@ -0,0 +1,202 @@
+from __future__ import annotations
+
+import os
+import shutil
+import statistics
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+import runner
+
+VOCAB_SIZE = 50257
+
+LOAD_BUDGET_S = 180.0
+
+RUN_BUDGET_MULTIPLIER = 2.0
+RUN_BUDGET_FLOOR_S = 60.0
+REFERENCE_RUN_BUDGET_S = 900.0
+
+AGREEMENT_MIN = 0.99
+
+
+def validate_outputs(outputs, requests):
+    """Return (ok, detail). Outputs are untrusted data, so every shape is checked."""
+    if not isinstance(outputs, list):
+        return False, "engine did not return a list"
+    if len(outputs) != len(requests):
+        return False, f"engine returned {len(outputs)} lists, expected {len(requests)}"
+    for i, (row, req) in enumerate(zip(outputs, requests)):
+        want = int(req["max_new_tokens"])
+        if not isinstance(row, list):
+            return False, f"element {i} is not a list"
+        if len(row) != want:
+            return False, f"element {i} has {len(row)} token ids, expected {want}"
+        for tid in row:
+            if not isinstance(tid, int) or isinstance(tid, bool):
+                return False, f"element {i} contains a non-integer token id"
+            if not 0 <= tid < VOCAB_SIZE:
+                return False, f"element {i} contains out-of-range token id {tid}"
+    return True, ""
+
+
+def count_agreement(outputs, reference):
+    """Position-wise agreement between two valid output sets."""
+    matched = total = 0
+    for row, ref in zip(outputs, reference):
+        total += len(ref)
+        matched += sum(1 for a, b in zip(row, ref) if a == b)
+    return matched, total
+
+
+def tokens_per_second(requests, elapsed_s):
+    return sum(int(r["max_new_tokens"]) for r in requests) / elapsed_s
+
+
+def _fresh_dir(path, owner=None):
+    shutil.rmtree(path, ignore_errors=True)
+    os.makedirs(path, mode=0o700, exist_ok=True)
+    if owner is not None:
+        os.chown(path, owner[0], owner[1])
+    return path
+
+
+def run_rounds(
+    reference_engine_path,
+    candidate_engine_path,
+    model_dir,
+    warmup_path,
+    workloads,
+    work_root,
+    cmd_prefix=(),
+    runner_path=None,
+    run_dir_owner=None,
+    reap_uid=None,
+    log=print,
+):
+    """Interleave reference and candidate over one distinct workload per round.
+
+    Each round is a fresh process pair on a workload the previous round never saw, so
+    nothing carried over from an earlier round can stand in for the work. The reference
+    run's directory is destroyed before the candidate is launched, so the candidate
+    cannot read the answer off the run that just produced it.
+
+    Returns a list of per-round dicts; a round with ``ok`` false stops the comparison.
+    """
+    rounds = []
+    for index, (name, requests) in enumerate(workloads):
+        ref_dir = _fresh_dir(os.path.join(work_root, f"round{index}_reference"), run_dir_owner)
+        cand_dir = _fresh_dir(os.path.join(work_root, f"round{index}_candidate"), run_dir_owner)
+
+        ref = runner.run_timed(
+            reference_engine_path,
+            model_dir,
+            warmup_path,
+            requests,
+            ref_dir,
+            LOAD_BUDGET_S,
+            REFERENCE_RUN_BUDGET_S,
+            cmd_prefix=cmd_prefix,
+            runner_path=runner_path,
+            reap_uid=reap_uid,
+        )
+        if not ref.ok:
+            return rounds + [
+                {"round": index, "workload": name, "ok": False,
+                 "detail": f"reference run failed: {ref.detail}"}
+            ]
+        ref_ok, ref_detail = validate_outputs(ref.outputs, requests)
+        if not ref_ok:
+            return rounds + [
+                {"round": index, "workload": name, "ok": False,
+                 "detail": f"reference run invalid: {ref_detail}"}
+            ]
+        shutil.rmtree(ref_dir, ignore_errors=True)
+
+        budget = max(RUN_BUDGET_FLOOR_S, RUN_BUDGET_MULTIPLIER * ref.elapsed_s)
+        cand = runner.run_timed(
+            candidate_engine_path,
+            model_dir,
+            warmup_path,
+            requests,
+            cand_dir,
+            LOAD_BUDGET_S,
+            budget,
+            cmd_prefix=cmd_prefix,
+            runner_path=runner_path,
+            reap_uid=reap_uid,
+        )
+        record = {
+            "round": index,
+            "workload": name,
+            "new_tokens": sum(int(r["max_new_tokens"]) for r in requests),
+            "reference_s": round(ref.elapsed_s, 4),
+            "reference_tok_s": round(tokens_per_second(requests, ref.elapsed_s), 3),
+            "budget_s": round(budget, 2),
+        }
+        if not cand.ok:
+            record.update({"ok": False, "detail": cand.detail})
+            rounds.append(record)
+            log(f"round {index} ({name}): candidate failed - {cand.detail}")
+            return rounds
+
+        ok, detail = validate_outputs(cand.outputs, requests)
+        if not ok:
+            record.update({"ok": False, "detail": detail})
+            rounds.append(record)
+            log(f"round {index} ({name}): candidate invalid - {detail}")
+            return rounds
+
+        matched, total = count_agreement(cand.outputs, ref.outputs)
+        record.update(
+            {
+                "ok": True,
+                "detail": "",
+                "candidate_s": round(cand.elapsed_s, 4),
+                "candidate_tok_s": round(tokens_per_second(requests, cand.elapsed_s), 3),
+                "speedup": round(ref.elapsed_s / cand.elapsed_s, 4),
+                "matched_positions": matched,
+                "total_positions": total,
+            }
+        )
+        rounds.append(record)
+        log(
+            f"round {index} ({name}): reference {record['reference_tok_s']} tok/s, "
+            f"candidate {record['candidate_tok_s']} tok/s, "
+            f"speedup {record['speedup']}x, agreement {matched}/{total}"
+        )
+        shutil.rmtree(cand_dir, ignore_errors=True)
+    return rounds
+
+
+def summarise(rounds):
+    """Fold per-round records into the graded speedup S and the agreement rate."""
+    if not rounds or not all(r.get("ok") for r in rounds):
+        failed = next((r for r in rounds if not r.get("ok")), None)
+        return {
+            "speedup": 0.0,
+            "agreement": 0.0,
+            "valid": False,
+            "detail": (failed or {}).get("detail", "no rounds were run"),
+            "rounds": rounds,
+        }
+    matched = sum(r["matched_positions"] for r in rounds)
+    total = sum(r["total_positions"] for r in rounds)
+    agreement = matched / total if total else 0.0
+    speedups = [r["speedup"] for r in rounds]
+    if agreement < AGREEMENT_MIN:
+        return {
+            "speedup": 0.0,
+            "agreement": agreement,
+            "valid": True,
+            "detail": f"token agreement {agreement:.4f} is below {AGREEMENT_MIN}",
+            "rounds": rounds,
+        }
+    return {
+        "speedup": statistics.median(speedups),
+        "agreement": agreement,
+        "valid": True,
+        "detail": "",
+        "per_round_speedup": speedups,
+        "rounds": rounds,
+    }

tasks/cpu-llm-decode-throughput/tests/reference_engine.py

@@ -0,0 +1,139 @@
+from __future__ import annotations
+
+import json
+import math
+import os
+
+import torch
+from safetensors.torch import load_file
+
+_SQRT_2_OVER_PI = math.sqrt(2.0 / math.pi)
+
+_BLOCK_KEYS = (
+    "ln_1.weight",
+    "ln_1.bias",
+    "attn.c_attn.weight",
+    "attn.c_attn.bias",
+    "attn.c_proj.weight",
+    "attn.c_proj.bias",
+    "ln_2.weight",
+    "ln_2.bias",
+    "mlp.c_fc.weight",
+    "mlp.c_fc.bias",
+    "mlp.c_proj.weight",
+    "mlp.c_proj.bias",
+)
+
+
+def gelu_new(x: torch.Tensor) -> torch.Tensor:
+    """The tanh-approximated GELU the checkpoint was trained with."""
+    return 0.5 * x * (1.0 + torch.tanh(_SQRT_2_OVER_PI * (x + 0.044715 * x * x * x)))
+
+
+class Engine:
+    """Serves generation requests one at a time."""
+
+    def __init__(self, model_dir: str) -> None:
+        with open(os.path.join(model_dir, "config.json")) as fh:
+            cfg = json.load(fh)
+        self.n_layer = int(cfg["n_layer"])
+        self.n_head = int(cfg["n_head"])
+        self.n_embd = int(cfg["n_embd"])
+        self.n_positions = int(cfg["n_positions"])
+        self.eps = float(cfg["layer_norm_epsilon"])
+        self.head_dim = self.n_embd // self.n_head
+        self.scale = 1.0 / math.sqrt(self.head_dim)
+
+        sd = load_file(os.path.join(model_dir, "model.safetensors"))
+        self.wte = sd["wte.weight"].float().contiguous()
+        self.wpe = sd["wpe.weight"].float().contiguous()
+        self.ln_f_w = sd["ln_f.weight"].float().contiguous()
+        self.ln_f_b = sd["ln_f.bias"].float().contiguous()
+        self.blocks = [
+            {k: sd[f"h.{i}.{k}"].float().contiguous() for k in _BLOCK_KEYS}
+            for i in range(self.n_layer)
+        ]
+
+
+    def _forward(self, ids: torch.Tensor, past: list, start_pos: int) -> torch.Tensor:
+        """One forward pass over `ids` ([1, T]); returns logits for the last position."""
+        bsz, seq = ids.shape
+        pos = torch.arange(start_pos, start_pos + seq, dtype=torch.long)
+        h = self.wte[ids] + self.wpe[pos]
+
+        past_len = 0 if past[0] is None else past[0][0].shape[2]
+        total = past_len + seq
+        mask = None
+        if seq > 1:
+            allowed = torch.arange(total)[None, :] <= torch.arange(
+                past_len, past_len + seq
+            )[:, None]
+            mask = torch.zeros(seq, total)
+            mask.masked_fill_(~allowed, float("-inf"))
+
+        for i, b in enumerate(self.blocks):
+            x = torch.nn.functional.layer_norm(
+                h, (self.n_embd,), b["ln_1.weight"], b["ln_1.bias"], self.eps
+            )
+            qkv = torch.addmm(
+                b["attn.c_attn.bias"],
+                x.view(bsz * seq, self.n_embd),
+                b["attn.c_attn.weight"],
+            ).view(bsz, seq, 3 * self.n_embd)
+            q, k, v = qkv.split(self.n_embd, dim=2)
+            q = q.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2)
+            k = k.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2)
+            v = v.view(bsz, seq, self.n_head, self.head_dim).transpose(1, 2)
+            if past[i] is not None:
+                k = torch.cat((past[i][0], k), dim=2)
+                v = torch.cat((past[i][1], v), dim=2)
+            past[i] = (k, v)
+
+            att = torch.matmul(q, k.transpose(-2, -1)) * self.scale
+            if mask is not None:
+                att = att + mask
+            att = torch.softmax(att, dim=-1)
+            y = torch.matmul(att, v)
+            y = y.transpose(1, 2).reshape(bsz * seq, self.n_embd)
+            y = torch.addmm(b["attn.c_proj.bias"], y, b["attn.c_proj.weight"])
+            h = h + y.view(bsz, seq, self.n_embd)
+
+            x = torch.nn.functional.layer_norm(
+                h, (self.n_embd,), b["ln_2.weight"], b["ln_2.bias"], self.eps
+            )
+            x = torch.addmm(
+                b["mlp.c_fc.bias"], x.view(bsz * seq, self.n_embd), b["mlp.c_fc.weight"]
+            )
+            x = gelu_new(x)
+            x = torch.addmm(b["mlp.c_proj.bias"], x, b["mlp.c_proj.weight"])
+            h = h + x.view(bsz, seq, self.n_embd)
+
+        h = torch.nn.functional.layer_norm(
+            h[:, -1, :], (self.n_embd,), self.ln_f_w, self.ln_f_b, self.eps
+        )
+        return torch.matmul(h, self.wte.t())
+
+
+    @torch.inference_mode()
+    def generate(self, requests: list) -> list:
+        """Serve every request; returns one list of new token ids per request, in order."""
+        out = []
+        for req in requests:
+            prompt = list(req["prompt_token_ids"])
+            n_new = int(req["max_new_tokens"])
+            past = [None] * self.n_layer
+            ids = torch.tensor([prompt], dtype=torch.long)
+            logits = self._forward(ids, past, 0)
+            produced = []
+            pos = len(prompt)
+            for _ in range(n_new):
+                nxt = int(torch.argmax(logits[0]).item())
+                produced.append(nxt)
+                if len(produced) == n_new:
+                    break
+                logits = self._forward(
+                    torch.tensor([[nxt]], dtype=torch.long), past, pos
+                )
+                pos += 1
+            out.append(produced)
+        return out

tasks/cpu-llm-decode-throughput/tests/requirements-verifier.txt

@@ -0,0 +1,2 @@
+pytest==8.3.5
+pytest-json-ctrf==0.3.5