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98e8615 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | # Math VLM-Based Evaluation (VLM-as-Judge)
Evaluates a generated mathematical solution by asking **Gemini-2.5-Pro** to compare it against the ground-truth solution. This folder provides the exact evaluation prompt; run it through any Gemini access path (Google AI Studio, Gemini API, Vertex AI, or your own client).
A single prompt (`evaluation_prompt.txt`) is used across all math subtasks (AIME, MATH-500, GSM8K, OmniMath) and for **both image and video** generations.
## Evaluation Protocol
1. Take `evaluation_prompt.txt` and substitute the `{solution_text}` placeholder with the ground-truth solution text for the problem being evaluated.
2. Send one user message to `gemini-2.5-pro` containing:
- **The filled-in prompt** (text)
- **The generated output** to evaluate — the generated solution image, or the generated video (the prompt instructs Gemini to watch the whole video / read all visible work)
The ground truth is provided as text inside the prompt; only the generated solution is attached as media.
## Evaluation Criteria
Gemini judges three binary dimensions:
| Metric | Meaning |
|--------|---------|
| `INTERMEDIATE_CORRECT` | 1 if the intermediate reasoning/steps/methodology match the ground truth (minor notation differences OK), else 0 |
| `FINAL_CORRECT` | 1 if the final answer matches the ground truth (equivalent forms like `1/2 = 0.5` count), else 0 |
| `ACTION_REFLECTION` | 1 if the solution shows self-checking / reconsidering / trying multiple approaches, else 0 |
Note `INTERMEDIATE_CORRECT` and `FINAL_CORRECT` are independent — a solution can reach the right answer through flawed steps (`FINAL_CORRECT=1`, `INTERMEDIATE_CORRECT=0`) or vice-versa.
## Response Format
The prompt instructs Gemini to reply in a fixed structured format:
```
INTERMEDIATE_CORRECT: <0 or 1>
FINAL_CORRECT: <0 or 1>
ACTION_REFLECTION: <0 or 1>
EXPLANATION: <detailed per-dimension explanation>
```
Parse the scores line-by-line. The prompt includes five few-shot examples covering correct/incorrect and reflective/non-reflective cases.
## Metrics
`FINAL_CORRECT` is the primary accuracy metric. For a set of generations, report:
```
accuracy = (# of outputs with FINAL_CORRECT = 1) / (total # of outputs)
```
`INTERMEDIATE_CORRECT` and `ACTION_REFLECTION` give process-level diagnostic signal.
## Note
This is the canonical prompt used by `math_evaluation.py` / `omni_math_evaluation.py` (handles both image and video). The per-model evaluation wrappers (`math_evaluation_gpt_image_1.5.py`, `_openai.py`, `_qwen_image.py`, etc.) use lightly-trimmed image-only variants of this same prompt with the identical three metrics.
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