# Deep Paper Reading Methodology

Extracting full technical detail from arXiv papers when PDF parsing tools are unavailable.

## Extraction Fallback Chain

1. **pymupdf (fitz)** — best quality, but needs `uv pip install pymupdf`
2. **pdftotext** — `apt install poppler-utils`, then `pdftotext file.pdf -`
3. **Raw regex on PDF bytes** — `re.findall(rb'\(([^)]+)\)', data)` extracts text streams; works for metadata/abstract but garbles body
4. **HTML version** — `curl https://arxiv.org/html/{id}v{N}` — cleanest structured extraction; **preferred method**
5. **Abstract page** — `curl https://arxiv.org/abs/{id}` + regex on `<blockquote class="abstract">`

## HTML Extraction Patterns (preferred)

The arXiv HTML version (`/html/{id}v{N}`) has structured `<section>` elements with IDs:

```
S1 = Introduction
S2 = Problem definition
S3 = Key findings
S4 = Method
S5 = Experiments (S5.SS1, S5.SS2 = subsections)
S6 = Related work
S7 = Conclusion
Appendices: indexed by letter (A, B, C...) in ltx_tocentry
```

### Section extraction pattern:

```python
import re
html = re.sub(r'<(script|style)[^>]*>.*?</\1>', '', html, flags=re.DOTALL)
m = re.search(r'<section[^>]*id="S4"[^>]*>(.*?)(?=<section[^>]*id="S5"|$)', html, re.DOTALL)
text = re.sub(r'<[^>]+>', ' ', m.group(1)).strip()
text = re.sub(r'\s+', ' ', text)
```

### Table extraction:

```python
tables = re.findall(r'<table[^>]*>(.*?)</table>', html, re.DOTALL)
for t in tables:
    text = re.sub(r'<[^>]+>', ' | ', t).strip()
    text = re.sub(r'\s+', ' ', text)
```

### Appendix content (avoid TOC duplicates):

Appendix headings appear twice — once in TOC, once as actual content. Use `positions[-1]` (last occurrence) for the real content. Search by keyword rather than section ID for appendices.

### Targeted keyword search (when section IDs fail):

```python
searches = ['keyword1', 'keyword2']
for s in searches:
    positions = [m.start() for m in re.finditer(re.escape(s), html, re.IGNORECASE)]
    if positions:
        pos = positions[-1]  # last occurrence = actual content, not TOC
        chunk = html[max(0,pos-200):pos+500]
        text = re.sub(r'<[^>]+>', ' ', chunk)
```

## Structured Methodology Extraction Template

When the user asks to "learn the method" or do a deep read, extract:

1. **Architecture** — pipeline stages, model sizes, data flow
2. **Training data** — how it's constructed, sources, sizes, prompts used
3. **Negative mining** — strategy for hard negatives, filtering
4. **Loss functions** — exact objective, temperature, why this choice
5. **Training hyperparams** — LR, batch size, epochs, hardware
6. **Inference flow** — online vs offline steps, latency, throughput
7. **Key ablations** — what matters and by how much
8. **Code/models released** — check GitHub repo structure

## GitHub Repo Inspection Pattern

```bash
# Check if repo exists and get stats
curl -sL "https://api.github.com/repos/{owner}/{repo}"

# List top-level structure
curl -sL "https://api.github.com/repos/{owner}/{repo}/contents"

# Check subdirectories
for d in src scripts; do
  curl -sL "https://api.github.com/repos/{owner}/{repo}/contents/$d"
done

# Read README
curl -sL "https://raw.githubusercontent.com/{owner}/{repo}/main/README.md"
```

## Semantic Scholar for Citation Context

```bash
curl -s "https://api.semanticscholar.org/graph/v1/paper/arXiv:{id}?fields=citationCount,influentialCitationCount"
```
