| # VeriRender — Causal Consistency Evaluation |
| **Sample:** `sample_00239` |
|
|
| > **Before sending:** attach `clean.png` from this folder as the image, |
| > then paste everything below the horizontal rule into the chat. |
|
|
| --- |
|
|
| 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() |
| ``` |
|
|
| **Domain:** Data visualization |
| **Plot family:** Scatter plot with linear regression line |
|
|
| 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>", |
| "confidence": "low | medium | high", |
| "reasoning": "<1–3 sentences: what you expected vs. what you see>" |
| } |
| ``` |
|
|
