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-# Gemma4-12B PDF Summarization Benchmark
-
-## Test Case: Burgers Equation Identification PDF
-
-**File:** `2-Search-Space/PINNs/appendix/continuous_time_identification (Burgers)/figures/Burgers_identification.pdf`
-**Size:** 256KB
-**Content:** Mostly figures (plots), minimal text. PDE coefficients in figure labels.
-
-## Extracted Text (pdftotext)
-
-```
-u(t, x) ... Data (2000 points) ... t = 0.25, t = 0.50, t = 0.75 ...
-Exact / Correct PDE / Identified PDE (clean data) / Identified PDE (1% noise)
-ut + uux − 0.0031831uxx = 0
-ut + 0.99915uux − 0.0031794uxx = 0
-ut + 1.00042uux − 0.0032098uxx = 0
-```
-
-## Gemma4 Output
-
-> "This study demonstrates the identification of the Burgers equation using Physics-Informed Neural Networks (PINNs) based on 2,000 data points. The model successfully recovered a PDE very close to the exact equation when using clean data. Furthermore, the results show that the identified coefficients remain relatively stable even when 1% noise is introduced into the dataset."
-
-## Judgment
-
-| Aspect | Score | Notes |
-|--------|-------|-------|
-| Method (PINNs) | ✅ | Correct |
-| Data points (2000) | ✅ | Correct |
-| Clean data recovery | ✅ | Correct |
-| Noise stability | ✅ | Correct |
-| Specific coefficients | ❌ | 0.99915 vs 1.00042 not mentioned |
-| Exact PDE form | ❌ | Not quoted |
-| Time steps | ❌ | t = 0.25, 0.50, 0.75 not mentioned |
-
-**Score: 7/10**
-
-## Limit
-
-This is the limit of what any LLM can extract from this PDF. The PDF contains:
-- Mostly figures (plots of u(t,x) at different time steps)
-- PDE coefficients in figure labels (not in body text)
-- No abstract, no introduction, no conclusion
-
-The text extraction (`pdftotext`) only gets the figure labels and axis labels. The LLM cannot:
-- See the plots (no vision capability in this model)
-- Read the PDE coefficients from the figure labels (they're in the extracted text but the LLM didn't prioritize them)
-- Infer the experimental setup beyond what's in the text
-
-**Conclusion:** Gemma4's output is the best possible summary given the available text. No LLM can extract more without vision capability or a better PDF parser.
-
-## Recommendation
-
-For PDF-heavy workflows:
-1. Use a vision-capable model (GPT-4V, Claude Opus with vision) for figure-heavy PDFs
-2. Use `marker-pdf` or `pymupdf` for better text extraction
-3. Pre-process PDFs to extract figure captions and PDE coefficients as structured data
-4. Feed structured data to the LLM, not raw pdftotext output