Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4901
color   = "mediumpurple"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D', 'E']
heights = [-1.89, -1.52, -2.96, -2.85, -1.38]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (5 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4902
color   = "coral"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D', 'E']
heights = [2.71, -2.88, 3.19, 2.36, 2.85]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (5 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4903
color   = "coral"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D']
heights = [-2.93, 3.58, 4.65, 0.56]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (4 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4904
color   = "mediumseagreen"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D', 'E']
heights = [1.31, 3.65, 0.96, -1.43, 0.04]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (5 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4905
color   = "steelblue"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D']
heights = [4.79, 4.99, -2.15, -2.90]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (4 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4906
color   = "coral"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D']
heights = [2.27, 3.88, -0.89, 0.48]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (4 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4907
color   = "mediumseagreen"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D', 'E', 'F']
heights = [0.40, -0.06, 0.04, -2.27, 2.11, -2.08]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (6 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4908
color   = "coral"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D']
heights = [3.93, 3.38, -1.72, -2.13]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (4 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ─────────────────────────────────────────────────────────────
seed    = 4909
color   = "mediumpurple"

# ── Data ────────────────────────────────────────────────────────────────────
labels  = ['A', 'B', 'C', 'D']
heights = [1.29, -0.41, 4.48, 4.96]

# ── Plot ────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(7, 4))
bars = ax.bar(labels, heights, color=color, edgecolor='white', linewidth=0.5)
ax.axhline(0, color='black', linewidth=0.8)
for bar, h in zip(bars, heights):
    va = 'bottom' if h >= 0 else 'top'
    offset = 0.05 if h >= 0 else -0.05
    ax.text(bar.get_x() + bar.get_width()/2, h + offset,
            f'{h:.2f}', ha='center', va=va, fontsize=8, fontweight='bold')
ax.set_title("Bar Chart  (4 categories)")
ax.set_xlabel("Category")
ax.set_ylabel("Value")
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5900
n_points  = 59
slope     = 1.149370
intercept = 1.299066
noise_std = 0.539668

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5901
n_points  = 37
slope     = -1.855496
intercept = -1.322886
noise_std = 0.441639

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5902
n_points  = 37
slope     = -1.529994
intercept = -0.004273
noise_std = 0.730252

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5903
n_points  = 51
slope     = 1.440904
intercept = 0.738086
noise_std = 0.352827

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5904
n_points  = 59
slope     = 0.890379
intercept = 1.055611
noise_std = 0.559377

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5905
n_points  = 49
slope     = -1.929874
intercept = -0.664004
noise_std = 0.672751

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5906
n_points  = 40
slope     = 1.016841
intercept = 1.151654
noise_std = 0.678537

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5907
n_points  = 52
slope     = -0.891248
intercept = 0.612783
noise_std = 0.538393

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5908
n_points  = 53
slope     = 1.044824
intercept = 0.686445
noise_std = 0.641637

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```python
import numpy as np
import matplotlib.pyplot as plt

# ── Parameters ──────────────────────────────────────────────────────────────
seed      = 5909
n_points  = 38
slope     = -0.918191
intercept = -1.427642
noise_std = 0.533250

# ── Data ────────────────────────────────────────────────────────────────────
rng = np.random.default_rng(seed)
x   = np.sort(rng.uniform(-3.0, 3.0, n_points))
y   = slope * x + intercept + rng.normal(0.0, noise_std, n_points)

x_line = np.array([-3.0, 3.0])
y_line = slope * x_line + intercept

# ── Plot ─────────────────────────────────────────────────────────────────────
fig, ax = plt.subplots(figsize=(6, 5))
ax.scatter(x, y, alpha=0.6, s=30, color="steelblue", zorder=2)
ax.plot(x_line, y_line, color="crimson", linewidth=2, zorder=3,
        label=f"y = {slope:.3f}x + {intercept:.3f}")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.set_title(f"Linear Scatter  (n={n_points})")
ax.legend(fontsize=9)
ax.grid(True, alpha=0.3)
fig.tight_layout()
plt.show()
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 1.803\,cos(1.790x + 1.502)

x \in [0, 2\pi]
n_points = 229
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.723\,cos(1.810x + 0.773)

x \in [0, 2\pi]
n_points = 102
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.814\,cos(1.098x + 1.407)

x \in [0, 2\pi]
n_points = 213
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 1.745\,cos(2.896x + 1.774)

x \in [0, 2\pi]
n_points = 211
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.537\,sin(1.486x + 0.274) + 0.899\,cos(1.249x + 0.255)

x \in [0, 2\pi]
n_points = 219
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.933\,sin(1.359x + 3.084)

x \in [0, 2\pi]
n_points = 168
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 1.152\,cos(2.279x + 1.723) + 1.637\,sin(2.199x + 1.175)

x \in [0, 2\pi]
n_points = 189
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 1.143\,sin(1.289x + 1.056)

x \in [0, 2\pi]
n_points = 229
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 1.080\,sin(1.208x + 0.887) + 1.355\,cos(2.425x + 1.128)

x \in [0, 2\pi]
n_points = 169
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.961\,sin(2.271x + 1.308)

x \in [0, 2\pi]
n_points = 147
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.273x^3 - 0.686x^2 - 1.162x + 0.540

x \in [-3.0, 3.0]
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.348x^2 - 0.103x + 1.950

x \in [-3.0, 3.0]
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 1.483x^2 + 1.711x - 0.297

x \in [-3.0, 3.0]
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.866x^3 - 1.746x^2 - 1.098x + 0.551

x \in [-3.0, 3.0]
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```


Prompt:
You are evaluating a scientific visualization for **causal consistency**.

The following specification is the **symbolic generator** — it fully specifies
what the output plot should look like:

```latex
f(x) = 0.989x^2 - 0.364x - 1.721

x \in [-3.0, 3.0]
```

I am showing you an image that claims to be the output of this generator.

---

## Your Task

1. Read the specification carefully. Reason about what the plot should look like
   (shape, orientation, color mapping, symmetry, value signs, etc.).
2. Examine the attached image.
3. Decide whether the image is **causally consistent** with the generator.

If the image is **not** consistent, classify the inconsistency using exactly one
of these labels:

| Label | Meaning |
|---|---|
| `colormap_inversion` | The colormap used is different from what the code specifies |
| `axis_swap` | Axes or data dimensions are transposed or mirrored |
| `sign_inversion` | Values are negated — peaks and troughs (or bar directions) are swapped |
| `amplitude_scale` | The value scale is wrong — the colorbar, y-axis range, or bar value labels do not match the formula's amplitudes |
| `phase_shift` | The pattern is shifted from its correct position |
| `frequency_doubling` | The number of oscillations or cycles is wrong |
| `dc_offset` | The curves or point cloud are shifted away from their correct baseline |
| `wrong_petal_count` | The number of petals/lobes differs from what the formula produces |
| `symmetry_mismatch` | The image contains asymmetry that the code cannot produce |
| `bar_order_swap` | Two bars have had their heights swapped — a bar's value label contradicts the code's heights list |
| `coefficient_scale` | Polynomial coefficients are scaled but the formula in the spec is unchanged |
| `wrong_gravity` | Trajectory uses a different gravitational constant than the spec |
| `wrong_launch_angle` | Trajectory uses a different launch angle than the spec |
| `wrong_iteration_depth` | L-system rendered with a different iteration count than the spec |
| `wrong_angle` | L-system rendered with a different turn angle than the spec |

---

## Response Format

Respond with **only** this JSON object and nothing else:

```json
{
  "consistent": true | false,
  "bug_type": "<one label from the table above, or null if consistent>",
  "reasoning": "<1–3 sentences: what you expected vs. what you see>"
}
```