轨迹详情 — gpt-5.6-sol · model-extraction-relu-logits · t2

{"type": "text", "text": "Inspected `/app/forward.py`. Findings:\n\n- `/app/forward.py` is the only file in `/app`.\n- Dependency: NumPy.\n- Module import deterministically initializes parameters with `np.random.seed(0)`:\n  - `A1`: `(20, 10)`, values from `0.1 * np.random.randn`\n  - `b1`: `(20,)`, values from `0.1 * np.random.randn`\n  - `A2`: `(1, 20)`, values from `0.1 * np.random.randn`\n  - `b2`: `(1,)`, value from `0.1 * np.random.randn`\n- `relu(x)` is `np.maximum(0, x)`.\n- `forward(x)`:\n  1. Converts input using `np.array(x).reshape(-1)`.\n  2. Requires exactly 10 elements via an `assert`.\n  3. Computes `hidden = relu(A1 @ x + b1)`.\n  4. Computes scalar output `A2 @ hidden + b2`.\n  5. Returns `float(output[0])`.\n- It is a single-input callable; there is no batch handling or external query protocol.\n- Inputs may be nested array-like values if they flatten to 10 elements. Incorrect lengths raise `AssertionError`; incompatible values raise NumPy type errors. NaN inputs produce NaN outputs.\n- Calls do not mutate model state. The parameter arrays are exposed as module globals if direct local access is permitted.\n\nObserved outputs:\n\n- `forward(np.zeros(10))` → `-0.09423482539555281`\n- `forward(np.ones(10))` → `-0.2715513215667844`\n\nExtraction-relevant structure:\n\n\\[\nf(x)=b_2+\\sum_{i=1}^{20} A2_i \\max(0,A1_i\\cdot x+b1_i)\n\\]\n\nThus the function is continuous and piecewise affine over 10-dimensional input space. Each hidden unit has a boundary hyperplane:\n\n\\[\nA1_i\\cdot x+b1_i=0\n\\]\n\nSuggested probing strategy:\n\n1. Use generic one-dimensional scans `x=t v`, with random or carefully chosen 10-dimensional directions `v`.\n2. Sample outputs on an adaptive grid over `t`; estimate first differences/slopes.\n3. Detect slope changes, which indicate crossings of hidden-unit hyperplanes.\n4. Along a line, unit `i` crosses at:\n\n   \\[\n   t_i=-b1_i/(A1_i\\cdot v)\n   \\]\n\n   and causes a slope jump:\n\n   \\[\n   \\Delta s_i=A2_i(A1_i\\cdot v)\n   \\]\n\n5. Repeat with many independent directions. Shared breakpoint geometry across scans can be used to recover the hidden hyperplanes, while slope jumps provide information about output weights and directional projections.\n6. Probe away from exact boundaries because ReLU kinks and floating-point cancellation can make boundary estimates unstable.\n7. Use multiple step sizes and repeated evaluations to distinguish numerical noise from genuine slope changes.\n8. Validate any recovered network on held-out 10-element vectors, including points from multiple activation regions.\n\nImportant identifiability constraints:\n\n- Hidden-unit order is unobservable; parameters are recoverable only up to permutation.\n- ReLU units have a positive scaling symmetry: scaling a unit’s incoming affine form by a positive constant and inversely scaling its outgoing coefficient leaves the function unchanged.\n- A single line can miss units whose hyperplanes are parallel to the scan direction or whose crossing lies outside the sampled interval, so multidirectional and multiscale probing is necessary."}

[metadata] {"status": "completed", "prompt": "Inspect /app files, especially forward.py, to determine callable behavior, dependencies, and any constraints relevant to writing a query-based ReLU network extraction script. Do not modify files. Report findings and suggested probing strategy.", "agentId": "a8ba6e4e17219ddf7", "agentType": "Explore", "content": [{"type": "text", "text": "Inspected `/app/forward.py`. Findings:\n\n- `/app/forward.py` is the only file in `/app`.\n- Dependency: NumPy.\n- Module import deterministically initializes parameters with `np.random.seed(0)`:\n  - `A1`: `(20, 10)`, values from `0.1 * np.random.randn`\n  - `b1`: `(20,)`, values from `0.1 * np.random.randn`\n  - `A2`: `(1, 20)`, values from `0.1 * np.random.randn`\n  - `b2`: `(1,)`, value from `0.1 * np.random.randn`\n- `relu(x)` is `np.maximum(0, x)`.\n- `forward(x)`:\n  1. Converts input using `np.array(x).reshape(-1)`.\n  2. Requires exactly 10 elements via an `assert`.\n  3. Computes `hidden = relu(A1 @ x + b1)`.\n  4. Computes scalar output `A2 @ hidden + b2`.\n  5. Returns `float(output[0])`.\n- It is a single-input callable; there is no batch handling or external query protocol.\n- Inputs may be nested array-like values if they flatten to 10 elements. Incorrect lengths raise `AssertionError`; incompatible values raise NumPy type errors. NaN inputs produce NaN outputs.\n- Calls do not mutate model state. The parameter arrays are exposed as module globals if direct local access is permitted.\n\nObserved outputs:\n\n- `forward(np.zeros(10))` → `-0.09423482539555281`\n- `forward(np.ones(10))` → `-0.2715513215667844`\n\nExtraction-relevant structure:\n\n\\[\nf(x)=b_2+\\sum_{i=1}^{20} A2_i \\max(0,A1_i\\cdot x+b1_i)\n\\]\n\nThus the function is continuous and piecewise affine over 10-dimensional input space. Each hidden unit has a boundary hyperplane:\n\n\\[\nA1_i\\cdot x+b1_i=0\n\\]\n\nSuggested probing strategy:\n\n1. Use generic one-dimensional scans `x=t v`, with random or carefully chosen 10-dimensional directions `v`.\n2. Sample outputs on an adaptive grid over `t`; estimate first differences/slopes.\n3. Detect slope changes, which indicate crossings of hidden-unit hyperplanes.\n4. Along a line, unit `i` crosses at:\n\n   \\[\n   t_i=-b1_i/(A1_i\\cdot v)\n   \\]\n\n   and causes a slope jump:\n\n   \\[\n   \\Delta s_i=A2_i(A1_i\\cdot v)\n   \\]\n\n5. Repeat with many independent directions. Shared breakpoint geometry across scans can be used to recover the hidden hyperplanes, while slope jumps provide information about output weights and directional projections.\n6. Probe away from exact boundaries because ReLU kinks and floating-point cancellation can make boundary estimates unstable.\n7. Use multiple step sizes and repeated evaluations to distinguish numerical noise from genuine slope changes.\n8. Validate any recovered network on held-out 10-elemen

File created successfully at: /app/steal.py (file state is current in your context — no need to Read it back)

[metadata] {"type": "create", "filePath": "/app/steal.py", "content": "import numpy as np\nimport forward\n\n\n# This uses only black-box evaluations of forward.forward.  A kink of a\n# piecewise-affine ReLU function identifies one hidden hyperplane.  The jump\n# in the input gradient at that kink gives its normal direction.\ndef value(x):\n    return float(forward.forward(np.asarray(x, dtype=float)))\n\n\ndef gradient(x, step=2e-5):\n    x = np.asarray(x, dtype=float)\n    out = np.empty(10, dtype=float)\n    for j in range(10):\n        d = np.zeros(10)\n        d[j] = step\n        out[j] = (value(x + d) - value(x - d)) / (2.0 * step)\n    return out\n\n\ndef find_events(rng, directions=32, radius=7.0, samples=7001):\n    \"\"\"Return (boundary point, gradient jump) pairs from random line scans.\"\"\"\n    events = []\n    grid = np.linspace(-radius, radius, samples)\n    dt = grid[1] - grid[0]\n    for _ in range(directions):\n        v = rng.normal(size=10)\n        v /= np.linalg.norm(v)\n        # A random translate prevents a boundary from repeatedly landing on a\n        # grid point, and also makes near-coincident crossings less likely.\n        offset = rng.uniform(-0.35, 0.35)\n        ts = grid + offset * dt\n        ys = np.array([value(t * v) for t in ts])\n        curvature = np.abs(ys[2:] - 2.0 * ys[1:-1] + ys[:-2])\n\n        # With this scan spacing genuine kinks are typically 1e-5--1e-3,\n        # whereas floating point noise is below 1e-12.  Suppress broad/duplicate\n        # detections by retaining local maxima and using a small exclusion zone.\n        threshold = max(2e-8, 8.0 * np.median(curvature))\n        candidates = np.flatnonzero(\n            (curvature > threshold)\n            & (curvature >= np.r_[curvature[0], curvature[:-1]])\n            & (curvature >= np.r_[curvature[1:], curvature[-1]])\n        ) + 1\n        kept = []\n        for i in candidates:\n            if not kept or i - kept[-1] > 4:\n                kept.append(int(i))\n            elif curvature[i - 1] > curvature[kept[-1] - 1]:\n                kept[-1] = int(i)\n\n        for i in kept:\n            # Interpolate the kink location using the two adjacent secant\n            # slopes.  The result is accurate enough for fitting a hyperplane.\n            left_slope = (ys[i] - ys[i - 1]) / dt\n            right_slope = (ys[i + 1] - ys[i]) / dt\n            denom = right_slope - left_slope\n            if abs(denom) < 1e-10:\n                continue\n            frac = np.clip(-left_slope / denom, 0.05, 0.95)\n            t = ts[i - 1] + frac * dt\n            p = t * v\n\n            # Evaluate gradients on both sides, sufficiently close to the\n            # boundary to avoid crossing another hyperplane.\n            eps = min(2e-4, 0.15 * dt)\n            gm = gradient(p - eps * v)\n            gp = gradient(p + eps * v)\n            jump = gp - gm\n            if np.linalg.norm(jump) > 2e-5:\n                events.append((p, jump))\n    return events\n\n\ndef cluster_events(events):\n    # Events from the same hidden unit have parallel gradient jumps.  The sign\n    # can reverse when a line crosses that unit from its active to inactive side,\n    # so clustering is done in projective (absolute-cosine) space.\n    clusters = []\n    for point, jump in events:\n        norm = np.linalg.norm(jump)\n        if norm == 0.0:\n            continue\n        direction = jump / norm\n        best = None\n        best_score = -1.0\n        for k, c in enumerate(clusters):\n            score = abs(float(np.dot(direction, c[\"direction\"])))\n            if score > best_score:\n                best_score, best = score, k\n        if best_score > 0.985:\n            c = clusters[best]\n            c[\"points\"].append(point)\n            c[\"directions\"].append(direction)\n            # Keep a stable running representative, allowing either sign.\n            s = np.sum(c[\"directions\"], axis=0)\n            c[\"direction\"] = s / np.linalg.norm(s)\n        else:\n            clusters.append({\"direction\": direction,\n                             \"directions\": [direction],\n                             \"points\": [point]})\n    return [c for c in clusters if len(c[\"points\"]) >= 8]\n\n\ndef fit_hyperplane(points):\n    # For [x, 1] [normal, bias] = 0, the null vector is the last right-singular\n    # vector.  Normalize the normal rows so the saved matrix is well scaled.\n    z = np.c_[np.asarray(points), np.ones(len(points))]\n    _, _, vh = np.linalg.svd(z, full_matrices=False)\n    coeff = vh[-1]\n    n = coeff[:10]\n    scale = np.linalg.norm(n)\n    if scale == 0.0:\n        raise RuntimeError(\"degenerate recovered hyperplane\")\n    return n / scale\n\n\ndef main():\n    rng = np.random.default_rng(1234567)\n    events = find_events(rng)\n    clusters = cluster_events(events)\n\n    # The scan should find all 20 units.  If a rare numerical coincidence\n    # leaves a cluster underpopulated, more lines are cheap and improve fit.\n    if len(clusters) < 20:\n        events += find_events(rng, directions=48, radius=10.0, samples=9001)\n        clusters = cluster_events(events)\n\n    # Select the 20 best-supported distinct hyperplanes (the supplied model has\n    # a scalar output and a finite hidden layer; unsupported tiny clusters are\n    # numerical artifacts rather than neurons).\n    clusters.sort(key=lambda c: len(c[\"points\"]), reverse=True)\n    clusters = clusters[:20]\n    if len(clusters) != 20:\n        raise RuntimeError(\"could not resolve the hidden-layer hyperplanes\")\n\n    rows = np.array([fit_hyperplane(c[\"points\"]) for c in clusters])\n    np.save(\"/app/stolen_A1.npy\", rows)\n    print(rows)\n\n\nif __name__ == \"__main__\":\n    main()\n", "structuredPatch": [], "originalFile": null, "userModified": false}

Exit code 1
Traceback (most recent call last):
  File "/app/steal.py", line 144, in <module>
    main()
    ~~~~^^
  File "/app/steal.py", line 136, in main
    raise RuntimeError("could not resolve the hidden-layer hyperplanes")
RuntimeError: could not resolve the hidden-layer hyperplanes

[error] tool reported failure

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0 []

[stdout]
12
0 []
[metadata] {"noOutputExpected": false}

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40 [6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]

[stdout]
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40 [6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
[metadata] {"noOutputExpected": false}

The file /app/steal.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/steal.py", "oldString": "def gradient(x, step=2e-5):", "newString": "def gradient(x, step=2e-6):", "originalFile": "import numpy as np\nimport forward\n\n\n# This uses only black-box evaluations of forward.forward.  A kink of a\n# piecewise-affine ReLU function identifies one hidden hyperplane.  The jump\n# in the input gradient at that kink gives its normal direction.\ndef value(x):\n    return float(forward.forward(np.asarray(x, dtype=float)))\n\n\ndef gradient(x, step=2e-5):\n    x = np.asarray(x, dtype=float)\n    out = np.empty(10, dtype=float)\n    for j in range(10):\n        d = np.zeros(10)\n        d[j] = step\n        out[j] = (value(x + d) - value(x - d)) / (2.0 * step)\n    return out\n\n\ndef find_events(rng, directions=32, radius=7.0, samples=7001):\n    \"\"\"Return (boundary point, gradient jump) pairs from random line scans.\"\"\"\n    events = []\n    grid = np.linspace(-radius, radius, samples)\n    dt = grid[1] - grid[0]\n    for _ in range(directions):\n        v = rng.normal(size=10)\n        v /= np.linalg.norm(v)\n        # A random translate prevents a boundary from repeatedly landing on a\n        # grid point, and also makes near-coincident crossings less likely.\n        offset = rng.uniform(-0.35, 0.35)\n        ts = grid + offset * dt\n        ys = np.array([value(t * v) for t in ts])\n        curvature = np.abs(ys[2:] - 2.0 * ys[1:-1] + ys[:-2])\n\n        # With this scan spacing genuine kinks are typically 1e-5--1e-3,\n        # whereas floating point noise is below 1e-12.  Suppress broad/duplicate\n        # detections by retaining local maxima and using a small exclusion zone.\n        threshold = max(2e-8, 8.0 * np.median(curvature))\n        candidates = np.flatnonzero(\n            (curvature > threshold)\n            & (curvature >= np.r_[curvature[0], curvature[:-1]])\n            & (curvature >= np.r_[curvature[1:], curvature[-1]])\n        ) + 1\n        kept = []\n        for i in candidates:\n            if not kept or i - kept[-1] > 4:\n                kept.append(int(i))\n            elif curvature[i - 1] > curvature[kept[-1] - 1]:\n                kept[-1] = int(i)\n\n        for i in kept:\n            # Interpolate the kink location using the two adjacent secant\n            # slopes.  The result is accurate enough for fitting a hyperplane.\n            left_slope = (ys[i] - ys[i - 1]) / dt\n            right_slope = (ys[i + 1] - ys[i]) / dt\n            denom = right_slope - left_slope\n            if abs(denom) < 1e-10:\n                continue\n            frac = np.clip(-left_slope / denom, 0.05, 0.95)\n            t = ts[i - 1] + frac * dt\n            p = t * v\n\n            # Evaluate gradients on both sides, sufficiently close to the\n            # boundary to avoid crossing another hyperplane.\n            eps = min(2e-4, 0.15 * dt)\n            gm = gradient(p - eps * v)\n            gp = gradient(p + eps * v)\n            jump = gp - gm\n            if np.linalg.norm(jump) > 2e-5:\n                events.append((p, jump))\n    return events\n\n\ndef cluster_events(events):\n    # Events from the same hidden unit have parallel gradient jumps.  The sign\n    # can reverse when a line crosses that unit from its active to inactive side,\n    # so clustering is done in projective (absolute-cosine) space.\n    clusters = []\n    for point, jump in events:\n        norm = np.linalg.norm(jump)\n        if norm == 0.0:\n            continue\n        direction = jump / norm\n        best = None\n        best_score = -1.0\n        for k, c in enumerate(clusters):\n            score = abs(float(np.dot(direction, c[\"direction\"])))\n            if score > best_score:\n                best_score, best = score, k\n        if best_score > 0.985:\n            c = clusters[best]\n            c[\"points\"].append(point)\n            c[\"directions\"].append(direction)\n            # Keep a stable running representative, allowing either sign.\n            s = np.sum(c[\"directions\"], axis=0)\n            c[\"direction\"] = s / np.linalg.norm(s)\n        else:\n            clusters.append({\"direction\": direction,\n                             \"directions\": [direction],\n                             \"points\": [point]})\n    return [c for c in clusters if len(c[\"points\"]) >= 8]\n\n\ndef fit_hyperplane(points):\n    # For [x, 1] [normal, bias] = 0, the null vector is the last right-singular\n    # vector.  Normalize the normal rows so the saved matrix is well scaled.\n    z = np.c_[np.asarray(points), np.ones(len(points))]\n    _, _, vh = np.linalg.svd(z, full_matrices=False)\n    coeff = vh[-1]\n    n = coeff[:10]\n    scale = np.linalg.norm(n)\n    if scale == 0.0:\n        raise RuntimeError(\"degenerate recovered hyperplane\")\n    return n / scale\n\n\ndef main():\n    rng = np.random.default_rng(1234567)\n    events = find_events(rng)\n    clusters = cluster_events(events)\n\n    # The scan should find all 20 units.  If a rare numerical coincidence\n    # leaves a cluster underpopulated, more lines are cheap and improve fit.\n    if len(clusters) < 20:\n        events += find_events(rng, directions=48, radius=10.0, samples=9001)\n        clusters = cluster_events(events)\n\n    # Select the 20 best-supported distinct hyperplanes (the supplied model has\n    # a scalar output and a finite hidden layer; unsupported tiny clusters are\n    # numerical artifacts rather than neurons).\n    clusters.sort(key=lambda c: len(c[\"points\"]), reverse=True)\n    clusters = clusters[:20]\n    if len(clusters) != 20:\n        raise RuntimeError(\"could not resolve the hidden-layer hyperplanes\")\n\n    rows = np.array([fit_hyperplane(c[\"points\"]) for c in clusters])\n    np.save(\"/app/stolen_A1.npy\", rows)\n    print(rows)\n\n\nif __name__ == \"__main__\":\n    main()\n", "structuredPatch": [{"ol

The file /app/steal.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/steal.py", "oldString": "            eps = min(2e-4, 0.15 * dt)", "newString": "            eps = min(2e-5, 0.08 * dt)", "originalFile": "import numpy as np\nimport forward\n\n\n# This uses only black-box evaluations of forward.forward.  A kink of a\n# piecewise-affine ReLU function identifies one hidden hyperplane.  The jump\n# in the input gradient at that kink gives its normal direction.\ndef value(x):\n    return float(forward.forward(np.asarray(x, dtype=float)))\n\n\ndef gradient(x, step=2e-6):\n    x = np.asarray(x, dtype=float)\n    out = np.empty(10, dtype=float)\n    for j in range(10):\n        d = np.zeros(10)\n        d[j] = step\n        out[j] = (value(x + d) - value(x - d)) / (2.0 * step)\n    return out\n\n\ndef find_events(rng, directions=32, radius=7.0, samples=7001):\n    \"\"\"Return (boundary point, gradient jump) pairs from random line scans.\"\"\"\n    events = []\n    grid = np.linspace(-radius, radius, samples)\n    dt = grid[1] - grid[0]\n    for _ in range(directions):\n        v = rng.normal(size=10)\n        v /= np.linalg.norm(v)\n        # A random translate prevents a boundary from repeatedly landing on a\n        # grid point, and also makes near-coincident crossings less likely.\n        offset = rng.uniform(-0.35, 0.35)\n        ts = grid + offset * dt\n        ys = np.array([value(t * v) for t in ts])\n        curvature = np.abs(ys[2:] - 2.0 * ys[1:-1] + ys[:-2])\n\n        # With this scan spacing genuine kinks are typically 1e-5--1e-3,\n        # whereas floating point noise is below 1e-12.  Suppress broad/duplicate\n        # detections by retaining local maxima and using a small exclusion zone.\n        threshold = max(2e-8, 8.0 * np.median(curvature))\n        candidates = np.flatnonzero(\n            (curvature > threshold)\n            & (curvature >= np.r_[curvature[0], curvature[:-1]])\n            & (curvature >= np.r_[curvature[1:], curvature[-1]])\n        ) + 1\n        kept = []\n        for i in candidates:\n            if not kept or i - kept[-1] > 4:\n                kept.append(int(i))\n            elif curvature[i - 1] > curvature[kept[-1] - 1]:\n                kept[-1] = int(i)\n\n        for i in kept:\n            # Interpolate the kink location using the two adjacent secant\n            # slopes.  The result is accurate enough for fitting a hyperplane.\n            left_slope = (ys[i] - ys[i - 1]) / dt\n            right_slope = (ys[i + 1] - ys[i]) / dt\n            denom = right_slope - left_slope\n            if abs(denom) < 1e-10:\n                continue\n            frac = np.clip(-left_slope / denom, 0.05, 0.95)\n            t = ts[i - 1] + frac * dt\n            p = t * v\n\n            # Evaluate gradients on both sides, sufficiently close to the\n            # boundary to avoid crossing another hyperplane.\n            eps = min(2e-4, 0.15 * dt)\n            gm = gradient(p - eps * v)\n            gp = gradient(p + eps * v)\n            jump = gp - gm\n            if np.linalg.norm(jump) > 2e-5:\n                events.append((p, jump))\n    return events\n\n\ndef cluster_events(events):\n    # Events from the same hidden unit have parallel gradient jumps.  The sign\n    # can reverse when a line crosses that unit from its active to inactive side,\n    # so clustering is done in projective (absolute-cosine) space.\n    clusters = []\n    for point, jump in events:\n        norm = np.linalg.norm(jump)\n        if norm == 0.0:\n            continue\n        direction = jump / norm\n        best = None\n        best_score = -1.0\n        for k, c in enumerate(clusters):\n            score = abs(float(np.dot(direction, c[\"direction\"])))\n            if score > best_score:\n                best_score, best = score, k\n        if best_score > 0.985:\n            c = clusters[best]\n            c[\"points\"].append(point)\n            c[\"directions\"].append(direction)\n            # Keep a stable running representative, allowing either sign.\n            s = np.sum(c[\"directions\"], axis=0)\n            c[\"direction\"] = s / np.linalg.norm(s)\n        else:\n            clusters.append({\"direction\": direction,\n                             \"directions\": [direction],\n                             \"points\": [point]})\n    return [c for c in clusters if len(c[\"points\"]) >= 8]\n\n\ndef fit_hyperplane(points):\n    # For [x, 1] [normal, bias] = 0, the null vector is the last right-singular\n    # vector.  Normalize the normal rows so the saved matrix is well scaled.\n    z = np.c_[np.asarray(points), np.ones(len(points))]\n    _, _, vh = np.linalg.svd(z, full_matrices=False)\n    coeff = vh[-1]\n    n = coeff[:10]\n    scale = np.linalg.norm(n)\n    if scale == 0.0:\n        raise RuntimeError(\"degenerate recovered hyperplane\")\n    return n / scale\n\n\ndef main():\n    rng = np.random.default_rng(1234567)\n    events = find_events(rng)\n    clusters = cluster_events(events)\n\n    # The scan should find all 20 units.  If a rare numerical coincidence\n    # leaves a cluster underpopulated, more lines are cheap and improve fit.\n    if len(clusters) < 20:\n        events += find_events(rng, directions=48, radius=10.0, samples=9001)\n        clusters = cluster_events(events)\n\n    # Select the 20 best-supported distinct hyperplanes (the supplied model has\n    # a scalar output and a finite hidden layer; unsupported tiny clusters are\n    # numerical artifacts rather than neurons).\n    clusters.sort(key=lambda c: len(c[\"points\"]), reverse=True)\n    clusters = clusters[:20]\n    if len(clusters) != 20:\n        raise RuntimeError(\"could not resolve the hidden-layer hyperplanes\")\n\n    rows = np.array([fit_hyperplane(c[\"points\"]) for c in clusters])\n    np.save(\"/app/stolen_A1.npy\", rows)\n    print(rows)\n\n\nif __name__ == \"__main__\":\n    main()\n", "s

The file /app/steal.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/steal.py", "oldString": "    return [c for c in clusters if len(c[\"points\"]) >= 8]", "newString": "    return [c for c in clusters if len(c[\"points\"]) >= 3]", "originalFile": "import numpy as np\nimport forward\n\n\n# This uses only black-box evaluations of forward.forward.  A kink of a\n# piecewise-affine ReLU function identifies one hidden hyperplane.  The jump\n# in the input gradient at that kink gives its normal direction.\ndef value(x):\n    return float(forward.forward(np.asarray(x, dtype=float)))\n\n\ndef gradient(x, step=2e-6):\n    x = np.asarray(x, dtype=float)\n    out = np.empty(10, dtype=float)\n    for j in range(10):\n        d = np.zeros(10)\n        d[j] = step\n        out[j] = (value(x + d) - value(x - d)) / (2.0 * step)\n    return out\n\n\ndef find_events(rng, directions=32, radius=7.0, samples=7001):\n    \"\"\"Return (boundary point, gradient jump) pairs from random line scans.\"\"\"\n    events = []\n    grid = np.linspace(-radius, radius, samples)\n    dt = grid[1] - grid[0]\n    for _ in range(directions):\n        v = rng.normal(size=10)\n        v /= np.linalg.norm(v)\n        # A random translate prevents a boundary from repeatedly landing on a\n        # grid point, and also makes near-coincident crossings less likely.\n        offset = rng.uniform(-0.35, 0.35)\n        ts = grid + offset * dt\n        ys = np.array([value(t * v) for t in ts])\n        curvature = np.abs(ys[2:] - 2.0 * ys[1:-1] + ys[:-2])\n\n        # With this scan spacing genuine kinks are typically 1e-5--1e-3,\n        # whereas floating point noise is below 1e-12.  Suppress broad/duplicate\n        # detections by retaining local maxima and using a small exclusion zone.\n        threshold = max(2e-8, 8.0 * np.median(curvature))\n        candidates = np.flatnonzero(\n            (curvature > threshold)\n            & (curvature >= np.r_[curvature[0], curvature[:-1]])\n            & (curvature >= np.r_[curvature[1:], curvature[-1]])\n        ) + 1\n        kept = []\n        for i in candidates:\n            if not kept or i - kept[-1] > 4:\n                kept.append(int(i))\n            elif curvature[i - 1] > curvature[kept[-1] - 1]:\n                kept[-1] = int(i)\n\n        for i in kept:\n            # Interpolate the kink location using the two adjacent secant\n            # slopes.  The result is accurate enough for fitting a hyperplane.\n            left_slope = (ys[i] - ys[i - 1]) / dt\n            right_slope = (ys[i + 1] - ys[i]) / dt\n            denom = right_slope - left_slope\n            if abs(denom) < 1e-10:\n                continue\n            frac = np.clip(-left_slope / denom, 0.05, 0.95)\n            t = ts[i - 1] + frac * dt\n            p = t * v\n\n            # Evaluate gradients on both sides, sufficiently close to the\n            # boundary to avoid crossing another hyperplane.\n            eps = min(2e-5, 0.08 * dt)\n            gm = gradient(p - eps * v)\n            gp = gradient(p + eps * v)\n            jump = gp - gm\n            if np.linalg.norm(jump) > 2e-5:\n                events.append((p, jump))\n    return events\n\n\ndef cluster_events(events):\n    # Events from the same hidden unit have parallel gradient jumps.  The sign\n    # can reverse when a line crosses that unit from its active to inactive side,\n    # so clustering is done in projective (absolute-cosine) space.\n    clusters = []\n    for point, jump in events:\n        norm = np.linalg.norm(jump)\n        if norm == 0.0:\n            continue\n        direction = jump / norm\n        best = None\n        best_score = -1.0\n        for k, c in enumerate(clusters):\n            score = abs(float(np.dot(direction, c[\"direction\"])))\n            if score > best_score:\n                best_score, best = score, k\n        if best_score > 0.985:\n            c = clusters[best]\n            c[\"points\"].append(point)\n            c[\"directions\"].append(direction)\n            # Keep a stable running representative, allowing either sign.\n            s = np.sum(c[\"directions\"], axis=0)\n            c[\"direction\"] = s / np.linalg.norm(s)\n        else:\n            clusters.append({\"direction\": direction,\n                             \"directions\": [direction],\n                             \"points\": [point]})\n    return [c for c in clusters if len(c[\"points\"]) >= 8]\n\n\ndef fit_hyperplane(points):\n    # For [x, 1] [normal, bias] = 0, the null vector is the last right-singular\n    # vector.  Normalize the normal rows so the saved matrix is well scaled.\n    z = np.c_[np.asarray(points), np.ones(len(points))]\n    _, _, vh = np.linalg.svd(z, full_matrices=False)\n    coeff = vh[-1]\n    n = coeff[:10]\n    scale = np.linalg.norm(n)\n    if scale == 0.0:\n        raise RuntimeError(\"degenerate recovered hyperplane\")\n    return n / scale\n\n\ndef main():\n    rng = np.random.default_rng(1234567)\n    events = find_events(rng)\n    clusters = cluster_events(events)\n\n    # The scan should find all 20 units.  If a rare numerical coincidence\n    # leaves a cluster underpopulated, more lines are cheap and improve fit.\n    if len(clusters) < 20:\n        events += find_events(rng, directions=48, radius=10.0, samples=9001)\n        clusters = cluster_events(events)\n\n    # Select the 20 best-supported distinct hyperplanes (the supplied model has\n    # a scalar output and a finite hidden layer; unsupported tiny clusters are\n    # numerical artifacts rather than neurons).\n    clusters.sort(key=lambda c: len(c[\"points\"]), reverse=True)\n    clusters = clusters[:20]\n    if len(clusters) != 20:\n        raise RuntimeError(\"could not resolve the hidden-layer hyperplanes\")\n\n    rows = np.array([fit_hyperplane(c[\"points\"]) for c in clusters])\n    np.save(\"/app/stolen_A1.npy\", rows)\n    print(rows)\n\n\nif __

Exit code 1
Traceback (most recent call last):
  File "/app/steal.py", line 144, in <module>
    main()
    ~~~~^^
  File "/app/steal.py", line 136, in main
    raise RuntimeError("could not resolve the hidden-layer hyperplanes")
RuntimeError: could not resolve the hidden-layer hyperplanes

[error] tool reported failure

8 0 []

[stdout]
8 0 []
[metadata] {"noOutputExpected": false}

4.363818058222835e-05 [1.11022302e-16 3.33066907e-16 1.19285377e-05] 2.7755575615628914e-17

[stdout]
4.363818058222835e-05 [1.11022302e-16 3.33066907e-16 1.19285377e-05] 2.7755575615628914e-17
[metadata] {"noOutputExpected": false}

The file /app/steal.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/steal.py", "oldString": "def gradient(x, step=2e-6):", "newString": "def gradient(x, step=1e-4):", "originalFile": "import numpy as np\nimport forward\n\n\n# This uses only black-box evaluations of forward.forward.  A kink of a\n# piecewise-affine ReLU function identifies one hidden hyperplane.  The jump\n# in the input gradient at that kink gives its normal direction.\ndef value(x):\n    return float(forward.forward(np.asarray(x, dtype=float)))\n\n\ndef gradient(x, step=2e-6):\n    x = np.asarray(x, dtype=float)\n    out = np.empty(10, dtype=float)\n    for j in range(10):\n        d = np.zeros(10)\n        d[j] = step\n        out[j] = (value(x + d) - value(x - d)) / (2.0 * step)\n    return out\n\n\ndef find_events(rng, directions=32, radius=7.0, samples=7001):\n    \"\"\"Return (boundary point, gradient jump) pairs from random line scans.\"\"\"\n    events = []\n    grid = np.linspace(-radius, radius, samples)\n    dt = grid[1] - grid[0]\n    for _ in range(directions):\n        v = rng.normal(size=10)\n        v /= np.linalg.norm(v)\n        # A random translate prevents a boundary from repeatedly landing on a\n        # grid point, and also makes near-coincident crossings less likely.\n        offset = rng.uniform(-0.35, 0.35)\n        ts = grid + offset * dt\n        ys = np.array([value(t * v) for t in ts])\n        curvature = np.abs(ys[2:] - 2.0 * ys[1:-1] + ys[:-2])\n\n        # With this scan spacing genuine kinks are typically 1e-5--1e-3,\n        # whereas floating point noise is below 1e-12.  Suppress broad/duplicate\n        # detections by retaining local maxima and using a small exclusion zone.\n        threshold = max(2e-8, 8.0 * np.median(curvature))\n        candidates = np.flatnonzero(\n            (curvature > threshold)\n            & (curvature >= np.r_[curvature[0], curvature[:-1]])\n            & (curvature >= np.r_[curvature[1:], curvature[-1]])\n        ) + 1\n        kept = []\n        for i in candidates:\n            if not kept or i - kept[-1] > 4:\n                kept.append(int(i))\n            elif curvature[i - 1] > curvature[kept[-1] - 1]:\n                kept[-1] = int(i)\n\n        for i in kept:\n            # Interpolate the kink location using the two adjacent secant\n            # slopes.  The result is accurate enough for fitting a hyperplane.\n            left_slope = (ys[i] - ys[i - 1]) / dt\n            right_slope = (ys[i + 1] - ys[i]) / dt\n            denom = right_slope - left_slope\n            if abs(denom) < 1e-10:\n                continue\n            frac = np.clip(-left_slope / denom, 0.05, 0.95)\n            t = ts[i - 1] + frac * dt\n            p = t * v\n\n            # Evaluate gradients on both sides, sufficiently close to the\n            # boundary to avoid crossing another hyperplane.\n            eps = min(2e-5, 0.08 * dt)\n            gm = gradient(p - eps * v)\n            gp = gradient(p + eps * v)\n            jump = gp - gm\n            if np.linalg.norm(jump) > 2e-5:\n                events.append((p, jump))\n    return events\n\n\ndef cluster_events(events):\n    # Events from the same hidden unit have parallel gradient jumps.  The sign\n    # can reverse when a line crosses that unit from its active to inactive side,\n    # so clustering is done in projective (absolute-cosine) space.\n    clusters = []\n    for point, jump in events:\n        norm = np.linalg.norm(jump)\n        if norm == 0.0:\n            continue\n        direction = jump / norm\n        best = None\n        best_score = -1.0\n        for k, c in enumerate(clusters):\n            score = abs(float(np.dot(direction, c[\"direction\"])))\n            if score > best_score:\n                best_score, best = score, k\n        if best_score > 0.985:\n            c = clusters[best]\n            c[\"points\"].append(point)\n            c[\"directions\"].append(direction)\n            # Keep a stable running representative, allowing either sign.\n            s = np.sum(c[\"directions\"], axis=0)\n            c[\"direction\"] = s / np.linalg.norm(s)\n        else:\n            clusters.append({\"direction\": direction,\n                             \"directions\": [direction],\n                             \"points\": [point]})\n    return [c for c in clusters if len(c[\"points\"]) >= 3]\n\n\ndef fit_hyperplane(points):\n    # For [x, 1] [normal, bias] = 0, the null vector is the last right-singular\n    # vector.  Normalize the normal rows so the saved matrix is well scaled.\n    z = np.c_[np.asarray(points), np.ones(len(points))]\n    _, _, vh = np.linalg.svd(z, full_matrices=False)\n    coeff = vh[-1]\n    n = coeff[:10]\n    scale = np.linalg.norm(n)\n    if scale == 0.0:\n        raise RuntimeError(\"degenerate recovered hyperplane\")\n    return n / scale\n\n\ndef main():\n    rng = np.random.default_rng(1234567)\n    events = find_events(rng)\n    clusters = cluster_events(events)\n\n    # The scan should find all 20 units.  If a rare numerical coincidence\n    # leaves a cluster underpopulated, more lines are cheap and improve fit.\n    if len(clusters) < 20:\n        events += find_events(rng, directions=48, radius=10.0, samples=9001)\n        clusters = cluster_events(events)\n\n    # Select the 20 best-supported distinct hyperplanes (the supplied model has\n    # a scalar output and a finite hidden layer; unsupported tiny clusters are\n    # numerical artifacts rather than neurons).\n    clusters.sort(key=lambda c: len(c[\"points\"]), reverse=True)\n    clusters = clusters[:20]\n    if len(clusters) != 20:\n        raise RuntimeError(\"could not resolve the hidden-layer hyperplanes\")\n\n    rows = np.array([fit_hyperplane(c[\"points\"]) for c in clusters])\n    np.save(\"/app/stolen_A1.npy\", rows)\n    print(rows)\n\n\nif __name__ == \"__main__\":\n    main()\n", "structuredPatch": [{"ol

The file /app/steal.py has been updated successfully. (file state is current in your context — no need to Read it back)

[metadata] {"filePath": "/app/steal.py", "oldString": "            eps = min(2e-5, 0.08 * dt)", "newString": "            eps = min(2e-4, 0.15 * dt)", "originalFile": "import numpy as np\nimport forward\n\n\n# This uses only black-box evaluations of forward.forward.  A kink of a\n# piecewise-affine ReLU function identifies one hidden hyperplane.  The jump\n# in the input gradient at that kink gives its normal direction.\ndef value(x):\n    return float(forward.forward(np.asarray(x, dtype=float)))\n\n\ndef gradient(x, step=1e-4):\n    x = np.asarray(x, dtype=float)\n    out = np.empty(10, dtype=float)\n    for j in range(10):\n        d = np.zeros(10)\n        d[j] = step\n        out[j] = (value(x + d) - value(x - d)) / (2.0 * step)\n    return out\n\n\ndef find_events(rng, directions=32, radius=7.0, samples=7001):\n    \"\"\"Return (boundary point, gradient jump) pairs from random line scans.\"\"\"\n    events = []\n    grid = np.linspace(-radius, radius, samples)\n    dt = grid[1] - grid[0]\n    for _ in range(directions):\n        v = rng.normal(size=10)\n        v /= np.linalg.norm(v)\n        # A random translate prevents a boundary from repeatedly landing on a\n        # grid point, and also makes near-coincident crossings less likely.\n        offset = rng.uniform(-0.35, 0.35)\n        ts = grid + offset * dt\n        ys = np.array([value(t * v) for t in ts])\n        curvature = np.abs(ys[2:] - 2.0 * ys[1:-1] + ys[:-2])\n\n        # With this scan spacing genuine kinks are typically 1e-5--1e-3,\n        # whereas floating point noise is below 1e-12.  Suppress broad/duplicate\n        # detections by retaining local maxima and using a small exclusion zone.\n        threshold = max(2e-8, 8.0 * np.median(curvature))\n        candidates = np.flatnonzero(\n            (curvature > threshold)\n            & (curvature >= np.r_[curvature[0], curvature[:-1]])\n            & (curvature >= np.r_[curvature[1:], curvature[-1]])\n        ) + 1\n        kept = []\n        for i in candidates:\n            if not kept or i - kept[-1] > 4:\n                kept.append(int(i))\n            elif curvature[i - 1] > curvature[kept[-1] - 1]:\n                kept[-1] = int(i)\n\n        for i in kept:\n            # Interpolate the kink location using the two adjacent secant\n            # slopes.  The result is accurate enough for fitting a hyperplane.\n            left_slope = (ys[i] - ys[i - 1]) / dt\n            right_slope = (ys[i + 1] - ys[i]) / dt\n            denom = right_slope - left_slope\n            if abs(denom) < 1e-10:\n                continue\n            frac = np.clip(-left_slope / denom, 0.05, 0.95)\n            t = ts[i - 1] + frac * dt\n            p = t * v\n\n            # Evaluate gradients on both sides, sufficiently close to the\n            # boundary to avoid crossing another hyperplane.\n            eps = min(2e-5, 0.08 * dt)\n            gm = gradient(p - eps * v)\n            gp = gradient(p + eps * v)\n            jump = gp - gm\n            if np.linalg.norm(jump) > 2e-5:\n                events.append((p, jump))\n    return events\n\n\ndef cluster_events(events):\n    # Events from the same hidden unit have parallel gradient jumps.  The sign\n    # can reverse when a line crosses that unit from its active to inactive side,\n    # so clustering is done in projective (absolute-cosine) space.\n    clusters = []\n    for point, jump in events:\n        norm = np.linalg.norm(jump)\n        if norm == 0.0:\n            continue\n        direction = jump / norm\n        best = None\n        best_score = -1.0\n        for k, c in enumerate(clusters):\n            score = abs(float(np.dot(direction, c[\"direction\"])))\n            if score > best_score:\n                best_score, best = score, k\n        if best_score > 0.985:\n            c = clusters[best]\n            c[\"points\"].append(point)\n            c[\"directions\"].append(direction)\n            # Keep a stable running representative, allowing either sign.\n            s = np.sum(c[\"directions\"], axis=0)\n            c[\"direction\"] = s / np.linalg.norm(s)\n        else:\n            clusters.append({\"direction\": direction,\n                             \"directions\": [direction],\n                             \"points\": [point]})\n    return [c for c in clusters if len(c[\"points\"]) >= 3]\n\n\ndef fit_hyperplane(points):\n    # For [x, 1] [normal, bias] = 0, the null vector is the last right-singular\n    # vector.  Normalize the normal rows so the saved matrix is well scaled.\n    z = np.c_[np.asarray(points), np.ones(len(points))]\n    _, _, vh = np.linalg.svd(z, full_matrices=False)\n    coeff = vh[-1]\n    n = coeff[:10]\n    scale = np.linalg.norm(n)\n    if scale == 0.0:\n        raise RuntimeError(\"degenerate recovered hyperplane\")\n    return n / scale\n\n\ndef main():\n    rng = np.random.default_rng(1234567)\n    events = find_events(rng)\n    clusters = cluster_events(events)\n\n    # The scan should find all 20 units.  If a rare numerical coincidence\n    # leaves a cluster underpopulated, more lines are cheap and improve fit.\n    if len(clusters) < 20:\n        events += find_events(rng, directions=48, radius=10.0, samples=9001)\n        clusters = cluster_events(events)\n\n    # Select the 20 best-supported distinct hyperplanes (the supplied model has\n    # a scalar output and a finite hidden layer; unsupported tiny clusters are\n    # numerical artifacts rather than neurons).\n    clusters.sort(key=lambda c: len(c[\"points\"]), reverse=True)\n    clusters = clusters[:20]\n    if len(clusters) != 20:\n        raise RuntimeError(\"could not resolve the hidden-layer hyperplanes\")\n\n    rows = np.array([fit_hyperplane(c[\"points\"]) for c in clusters])\n    np.save(\"/app/stolen_A1.npy\", rows)\n    print(rows)\n\n\nif __name__ == \"__main__\":\n    main()\n", "s

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 [-0.13109661  0.57411098  0.25123667  0.04848109 -0.3933342   0.22613851
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 [-0.38943636  0.08894215  0.35724836  0.18730938 -0.07723161 -0.3929772
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 [ 0.29973763  0.23316217  0.30379488  0.3743827  -0.11402622 -0.29591967
   0.27029962  0.3420171  -0.19638406  0.54236916]
 [ 0.17126832 -0.07869486  0.28972353  0.55949233 -0.39270119 -0.21298331
  -0.2396498   0.2435569  -0.43967675  0.24053925]
 [ 0.392721    0.08040975  0.49300325  0.32942562  0.06803724  0.30969338
   0.28820215 -0.08984453 -0.09387378 -0.53595446]
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   0.37338787 -0.212957    0.07536799 -0.32512133]
 [ 0.2370696   0.04869628  0.30214983  0.01364395  0.24334409  0.39547478
   0.35536294 -0.5117395   0.49459096  0.03935722]
 [-0.48725927  0.07548986 -0.06174901  0.49068316 -0.42959658  0.01228474
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 [-0.12672138  0.34295874  0.46039653 -0.50238542  0.4566878   0.17988804
   0.26468625 -0.13610622 -0.0608473   0.26221244]
 [-0.1048612   0.47713716  0.16860388  0.25767268 -0.31239909  0.18529664
   0.09459153 -0.19195446  0.44535737  0.53894014]
 [ 0.50434459  0.05283593  0.38466367 -0.14202101  0.13455227  0.01156992
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 [ 0.31167364  0.07557151  0.15788586 -0.8142237   0.32250298 -0.12193473
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[stdout]
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  -0.22550548 -0.10938911 -0.14909607 -0.07068417]
 [-0.13109661  0.57411098  0.25123667  0.04848109 -0.3933342   0.22613851
  -0.26568622 -0.42772365  0.34369419  0.10112953]
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   0.02026334  0.11686596  0.19034204  0.02019664]
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   0.25752694 -0.53632591 -0.01337636 -0.55173257]
 [ 0.1818863   0.28807422 -0.13004724  0.20590916  0.21697132 -0.20362147
  -0.10534894  0.49554823  0.544342    0.42790985]
 [ 0.35300381 -0.25403244 -0.40789379 -0.18915452 -0.33296797  0.0070619
  -0.07226915 -0.52158765 -0.14024776  0.44814682]
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   0.30720562 -0.38777899  0.31188128  0.41869302]
 [ 0.29973763  0.23316217  0.30379488  0.3743827  -0.11402622 -0.29591967
   0.27029962  0.3420171  -0.19638406  0.54236916]
 [ 0.17126832 -0.07869486  0.28972353  0.55949233 -0.39270119 -0.21298331
  -0.2396498   0.2435569  -0.43967675  0.24053925]
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   0.28820215 -0.08984453 -0.09387378 -0.53595446]
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  -0.0789286   0.0501663  -0.29533031  0.10735024]
 [ 0.10853399 -0.45557391  0.01812938  0.31591535  0.56237474  0.26100355
   0.37338787 -0.212957    0.07536799 -0.32512133]
 [ 0.2370696   0.04869628  0.30214983  0.01364395  0.24334409  0.39547478
   0.35536294 -0.5117395   0.49459096  0.03935722]
 [-0.48725927  0.07548986 -0.06174901  0.49068316 -0.42959658  0.01228474
   0.47146118  0.26821105 -0.18199921 -0.01600403]
 [ 0.44114253  0.10263605 -0.11849425 -0.58990794 -0.27517458  0.31743992
  -0.34093418  0.33992176  0.10067628  0.12007937]
 [-0.12672138  0.34295874  0.46039653 -0.50238542  0.4566878   0.17988804
   0.26468625 -0.13610622 -0.0608473   0.26221244]
 [-0.1048612   0.47713716  0.16860388  0.25767268 -0.31239909  0.18529664
   0.09459153 -0.19195446  0.44535737  0.53894014]
 [ 0.50434459  0.05283593  0.38466367 -0.14202101  0.13455227  0.01156992
  -0.55310495  0.43924931  0.09207558  0.22164761]
 [ 0.31167364  0.07557151  0.15788586 -0.8142237   0.32250298 -0.12193473
   0.20875237 -0.13579222  0.03266083  0.16522791]
 [-0.3018112   0.01365955  0.17244759  0.05312145 -0.08868284  0.22017017
   0.24288209 -0.51165626  0.61859663  0.34114246]]
[metadata] {"noOutputExpected": false}

(20, 10) True

[stdout]
(20, 10) True
[metadata] {"noOutputExpected": false}