Datasets:

VietMedTeam
/

veri-render

sample_id stringlengths 12 12	split stringclasses 1 value	domain stringclasses 4 values	family stringclasses 11 values	perturbation_type stringclasses 15 values	perturbation_description stringlengths 21 105	prompt stringlengths 2.37k 3.42k	generator_content stringlengths 51 1.1k	generator_type stringclasses 4 values	seed int64 1k 11.1k
sample_00001	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'magma' → 'seismic' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1000 size = 30 colormap = "magma" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,000
sample_00002	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'plasma' → 'RdYlGn' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1001 size = 47 colormap = "plasma" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lin...	python	1,001
sample_00003	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'magma' → 'seismic' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1002 size = 40 colormap = "magma" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,002
sample_00004	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'inferno' → 'ocean' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1003 size = 32 colormap = "inferno" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,003
sample_00005	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'magma' → 'seismic' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1004 size = 43 colormap = "magma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,004
sample_00006	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'magma' → 'seismic' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1005 size = 35 colormap = "magma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,005
sample_00007	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'plasma' → 'RdYlGn' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1006 size = 37 colormap = "plasma" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lin...	python	1,006
sample_00008	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'magma' → 'seismic' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1007 size = 26 colormap = "magma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,007
sample_00009	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'magma' → 'seismic' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1008 size = 33 colormap = "magma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,008
sample_00010	inconsistent	data_visualization	heatmap	colormap_inversion	Colormap substituted: 'inferno' → 'ocean' (cross-family swap)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1009 size = 43 colormap = "inferno" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,009
sample_00011	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1100 size = 36 colormap = "inferno" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,100
sample_00012	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1101 size = 30 colormap = "inferno" n_blobs = 3 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,101
sample_00013	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1102 size = 34 colormap = "inferno" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,102
sample_00014	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1103 size = 35 colormap = "inferno" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,103
sample_00015	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1104 size = 47 colormap = "viridis" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,104
sample_00016	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1105 size = 49 colormap = "inferno" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,105
sample_00017	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1106 size = 32 colormap = "plasma" n_blobs = 3 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lin...	python	1,106
sample_00018	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1107 size = 44 colormap = "viridis" n_blobs = 3 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,107
sample_00019	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1108 size = 46 colormap = "magma" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,108
sample_00020	inconsistent	data_visualization	heatmap	axis_swap	X/Y meshgrids swapped and Z transposed: axis tick labels and blob positions are wrong	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1109 size = 39 colormap = "inferno" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,109
sample_00021	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1200 size = 42 colormap = "inferno" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,200
sample_00022	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1201 size = 47 colormap = "inferno" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,201
sample_00023	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1202 size = 42 colormap = "plasma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lin...	python	1,202
sample_00024	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1203 size = 49 colormap = "plasma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lin...	python	1,203
sample_00025	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1204 size = 42 colormap = "magma" n_blobs = 3 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,204
sample_00026	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1205 size = 27 colormap = "viridis" n_blobs = 3 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,205
sample_00027	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1206 size = 30 colormap = "magma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,206
sample_00028	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1207 size = 40 colormap = "inferno" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.li...	python	1,207
sample_00029	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1208 size = 42 colormap = "magma" n_blobs = 2 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,208
sample_00030	inconsistent	data_visualization	heatmap	sign_inversion	Z field negated: Z → −Z (hot↔cold inversion)	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1209 size = 34 colormap = "plasma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lin...	python	1,209
sample_00031	inconsistent	data_visualization	heatmap	amplitude_scale	Z scaled by ×2.0: colorbar range doubled while pattern shape is unchanged	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1300 size = 37 colormap = "magma" n_blobs = 3 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,300
sample_00032	inconsistent	data_visualization	heatmap	amplitude_scale	Z scaled by ×2.0: colorbar range doubled while pattern shape is unchanged	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 ────────────────────────────────────────────────...	import numpy as np import matplotlib.pyplot as plt # ── Parameters ───────────────────────────────────────────────────────────── seed = 1301 size = 27 colormap = "magma" n_blobs = 1 # ── Data ──────────────────────────────────────────────────────────────────── x = np.linspace(-3.0, 3.0, size) y = np.lins...	python	1,301

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VeriRender Benchmark Dataset

Causal consistency verification samples for Vision-Language Models.

Layout

manifest.jsonl          ← canonical index (one row per sample)
benchmark.yaml          ← config used to generate this release
inconsistent/{domain}/{sample_id}/   ← corrupted evaluation samples
consistent/{domain}/{sample_id}/     ← negative controls (clean images)

Splits

Split	Description	Eval image
`inconsistent`	Symbolic spec is correct; image has a perturbation	`corrupted.png`
`consistent`	Symbolic spec matches the clean image	`clean.png`

Sample folder

Each sample contains:

spec.py / spec.tex / spec.txt — symbolic generator (unchanged for inconsistent samples)
clean.png — faithful rendering
corrupted.png — perturbed rendering (inconsistent only)
prompt.md — VLM evaluation prompt
metadata.json — full provenance

Loading

import json
from pathlib import Path

root = Path(".")
rows = [json.loads(line) for line in (root / "manifest.jsonl").open()]

Or rebuild the manifest after edits:

python scripts/build_manifest.py

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