SynthFormulaNet_cleaned

The SynthFormulaNet__x family of the ElliotVL supervised-fine-tuning pool, after VLM cleaning.

images	477,803
QA turns	856,390
answers rewritten by the cleaning pass	103,773
QA created by the cleaning pass (new_qa)	405,724 (47.4%)
shards	6

How this was cleaned

A vision-language model read each image together with its QA and judged the item. The pass is not a filter that only removes rows — it rewrites answers it finds wrong but salvageable, drops what it cannot salvage, and adds QA where the image supports more than the source provided. Each row carries the judge's own record in clean_meta, including the cleaner identity, the policy it applied, and its per-item scores for legibility, richness and coverage.

A large share of the QA here was written by the cleaning pass, not by the original dataset. Across the pool that share runs from roughly half to over 80% of a family's turns, and it is reported in the table above. Those rows have an empty org_answer because no pre-clean original exists. Treat them as model-generated supervision: they were judged against the image, but they are not human annotation, and model-written QA is where formatting defects are most likely.

The effect on the answers that were carried over is substantive rather than cosmetic. In this pool the pass corrects values, not just wording — a curve's critical point restated from 4.00 to 2.00, a computed ratio from 1 to Approximately 1.33 — and for some families it removed the content entirely, which is why those families are absent here rather than published empty.

answer vs org_answer

• answer — the cleaned answer. Train on this.
• org_answer — the pre-cleaning answer from the same (image, question) in the uncleaned pool. It is empty for QA the cleaning pass added, which have no original.

Where the two differ, the difference is the correction. Keeping both makes every edit auditable instead of asking you to take the cleaning on trust.

Schema

• image — HF Image(), renders directly in the dataset viewer
• image_sha256 — content hash of the image
• n_qa — number of turns attached to this image
• qa — list of {question, answer, org_answer}, in source order
• family / clean_meta — provenance and the judge's per-row record

A row with more than one entry in qa is a conversation over a single image, not a set of independent questions — the turns depend on each other and the image belongs to all of them. Keep them together and in order when training.

Parquet row groups are written at ~32 MB so the dataset viewer pages without stalling.

What is NOT claimed

No rejection sampling was run against this family: there is no accept/reject verdict per row, and answer is the cleaned reference rather than a model generation that a string verifier confirmed. For that, use the *_RS_think / *_rejected datasets in elliot-mllm.

Examples

10 rows taken straight from this dataset. Images are the original files as stored here - no downscaling, no recompression - and every turn is shown in full.

Example 1

#	question	answer
1	Convert formula to latex.	\bigwedge _ { i \in I } \overline { x } ^ { i } \leq \overline { x } ^ { j } \leq \overline { a } ^ { j } = a .

Example 2 — 2 turns on one image

#	question	answer
1	What is the full mathematical inequality written in the image?	g(a_1) < g(a_2)
2	Which function is being compared in the expression?	g

Example 3 — 4 turns on one image

#	question	answer
1	Convert formula to latex.	4
2	What color is the digit?	Black
3	What is the background color?	White
4	What font style is the digit?	Serif

Example 4

#	question	answer
1	Convert formula to latex.	F ( x ) = { \frac { 1 } { q + 1 } } x ^ { q + 1 }

Example 5 — 2 turns on one image

#	question	answer (cleaned)	org_answer (pre-judge)
1		v(x,t)	v ( x , t )
2	What is the color of the mathematical expression?	Black	(unchanged)

Example 6

#	question	answer
1	Describe the visual appearance of the character in the image.	It is a black, lowercase, italic letter 'd' on a white background.

Example 7

#	question	answer
1	Convert formula to latex.	v ^ { ( 2 ) } ( z ) = \begin{cases} \begin{pmatrix} 1 & - s e ^ { - 2 t f ( z ) } \delta ^ { 2 } ( z ) \ 0 & 1 \end{pmatrix} \begin{pmatrix} 1 & 0 \ s e ^ { 2 t f ( z ) } \delta ^ { - 2 } ( z ) & 1 \end{pmatrix} , \quad & z \in \Sigma _ { 1 } , \ \begin{pmatrix} 1 & 0 \ \frac { s } { 1 - s ^ { 2 } } e ^ { 2 t f ( z ) } \delta _ { - } ^ { - 2 } & 1 - s ^ { 2 } \end{pmatrix} \begin{pmatrix} 1 & - \frac { s } { 1 - s ^ { 2 } } e ^ { - 2 t f ( z ) } \delta _ { + } ^ { 2 } ( z ) \ 0 & 1 \end{pmatrix} , \quad & z \in \Sigma _ { 2 } . \end{cases}

Example 8 — 3 turns on one image

#	question	answer
1	Convert formula to latex.	\begin{array} { r l } { [ c ] \phi _ { 0 } } & = 1 } \ { \phi _ { 1 } } & = 2 \sin ( n x / 2 ) \sin ( y ) } \ { \phi _ { 2 } } & = 2 \sin ( n x / 2 ) \sin ( 2 y ) } \end{array}
2	What are the colors of the text and the background?	Black text on a white background.
3	How many equations are listed in the image?	Three.

Example 9

#	question	answer (cleaned)	org_answer (pre-judge)
1		P ^ { - 1 } & = \left ( R + \frac { 1 } { N - 1 } H A \left ( H A \right ) ^ { T } \right ) ^ { - 1 } \ & = R ^ { - 1 } \left [ I - \frac { 1 } { N - 1 } \left ( H A \right ) \left ( I + \left ( H A \right ) ^ { T } R ^ { - 1 } \frac { 1 } { N - 1 } \left ( H A \right ) \right ) ^ { - 1 } \left ( H A \right ) ^ { T } R ^ { - 1 } \right ]	P ^ { - 1 } & = \left ( R + \frac { 1 } { N - 1 } H A \left ( H A \right ) ^ { T } \right ) ^ { - 1 } \ & = R ^ { - 1 } \left [ I - \frac { 1 } { N - 1 } \left ( H A \right ) \left ( I + \left ( H A \right ) ^ { T } R ^ { - 1 } \frac { 1 } { N - 1 } \left ( H A \right ) \right ) ^ { - 1 } \left ( H A \right ) ^ { T } R ^ { - 1 } \right ] ,

Example 10 — 2 turns on one image

#	question	answer
1	Convert formula to latex.	\mu \frac { d m ^ { 2 } ( \mu ) } { d \mu } = \gamma _ { m } ( \lambda ) m ^ { 2 }
2	What are the colors of the text and the background in the image?	The text is black and the background is white.