Paper Parser

paper-parser

1. Purpose and scope

paper-parser is the entry node of the Mobius DAG. It reads a single PDF (arXiv preprint, journal article, or technical report) and produces the paper sub-tree of the canonical SimSpec — equations as LaTeX and sympy, figures as PNGs with captions and sub-panel structure, and numerical claims with verbatim source quotes and UCUM-normalized units.

The skill does NOT:

• reason about physics (that is physics-interpreter's job — governing equations get tagged here, classified there)
• compose simulation specifications (that is simspec-author)
• verify claims against simulation output (that is science-integrity / paper-diff)
• generate visualizations (that is viz-mapper)
• emit Python primitives or simulator code (that is primitive-generator)

Everything downstream of paper-parser assumes that the JSON returned here is correct, lossless within the limits documented below, and reproducible across re-runs given the same PDF bytes.

2. Input

Required:

• pdf_path: str — absolute path to a readable PDF file. Multi-column arXiv layout is the assumed common case.

Optional:

• page_range: tuple[int, int] | None — inclusive (start, end) page hint. Default scans the entire document. Useful when the orchestrator wants to skip references or supplementary material on a known-structured paper.
• focus_sections: list[str] | None — section title hints (e.g. ["Methods", "Results"]). Treated as a soft prior on which pages matter for claim extraction; never used to drop pages outright.

The PDF is opened read-only. Nothing is written next to the input. All artefacts go to /tmp/mobius_figures/ and /tmp/mobius_parser_cache/.

3. Output

A JSON object matching the paper sub-tree of the SimSpec schema:

{
  "doi": "10.xxxx/...",
  "arxivId": "2501.12345",
  "title": "...",
  "authors": ["..."],
  "abstract": "...",
  "equations": [
    {
      "id": "eq-1",
      "latex": "\\nabla \\cdot \\vec{E} = \\rho/\\epsilon_0",
      "sympy_repr": "Eq(Derivative(E, x) + ..., rho/epsilon_0)",
      "variables": ["E", "rho", "epsilon_0"],
      "page": 3,
      "bbox": [120.5, 410.0, 480.2, 442.7],
      "surrounding_context": "...prose ±200 chars around the equation...",
      "confidence": "high"
    }
  ],
  "figures": [
    {
      "id": "fig-1",
      "png_path": "/tmp/mobius_figures//fig-1.png",
      "caption": "Verbatim caption text...",
      "subpanels": [
        {"label": "a", "bbox_in_figure": [0.0, 0.0, 0.5, 1.0], "png_path": "..."}
      ],
      "inline_numerical_claims": [
        {"value": 532.0, "unit": "nm", "location_hint": "panel (a) wavelength"}
      ],
      "page": 5,
      "confidence": 0.86
    }
  ],
  "numerical_claims": [
    {
      "id": "claim-1",
      "value": 1.4e-3,
      "unit_ucum": "kg.m-3",
      "value_uncertainty": 0.05e-3,
      "section": "Results",
      "source_quote": "the measured density was 1.4 x 10^-3 kg/m^3 +/- 0.05 x 10^-3",
      "claim_type": "measurement",
      "confidence": 0.92
    }
  ]
}

Field contracts:

• equations[*].latex is the canonical form. sympy_repr is str(sympy_expr) and is null when parsing fails.
• equations[*].variables is the list of free symbols recovered from sympy. When sympy parsing fails it falls back to a regex over [A-Za-z_][A-Za-z0-9_]* excluding LaTeX command names.
• figures[*].png_path is always an absolute path. The figure plus each sub-panel are saved as separate PNGs.
• numerical_claims[*].unit_ucum is a UCUM 1.9 string. When the original is unrecognised, the field carries the raw string and confidence is reduced.
• confidence on equations is "high" | "medium" | "low". On claims and figures it is a float in [0, 1].

3.bis Output format (mandatory)

You MUST emit your final output as a single fenced ``json block at the END of your reply, with NO prose after the closing fence. The orchestrator parses that block by regex (/``(?:json)?\s*\n([\s\S]*?)\n\s*`/) and ignores everything else in your text content. Code-execution bash/text_editor tool-result blocks are fine to interleave during work, but the LAST text block in the response MUST end with the canonical artifact fence.

The canonical artifact for paper-parser (consumed downstream as FiguresJson in server/src/types.ts):

{
  "figures": [
    {
      "id": "fig1",
      "png_path": "/mnt/user-data/outputs/fig1.png",
      "caption": "verbatim caption text",
      "page": 3
    }
  ]
}

The four fields (id, png_path, caption, page) are required for every figure. Additional fields documented in §3 (subpanels, inlinenumericalclaims, confidence, bbox, equations, numerical_claims, doi, arxivId, title, authors, abstract) MAY be included alongside as enrichment — downstream skills tolerate them — but the four required keys per figure are non-negotiable. PNGs MUST be written to /mnt/user-data/outputs/ so downstream phases can read them.

If you cannot produce a complete artifact (encrypted PDF, no parsable pages, vision unavailable for a scanned scan), emit a fenced JSON block carrying a single error field:

{ "error": "encrypted PDF; fitz.open raised PasswordError on page 1" }

DO NOT emit narration, summaries, or follow-up questions after the closing fence — they break the orchestrator's downstream consumption and get silently dropped.

4. Three-phase extraction

The skill bundles three Python scripts. The orchestrator calls them in sequence; intermediate results are passed by value, no shared filesystem state required.

Phase A — scripts/extract_equations.py

PyMuPDF block-level pass:

1. Iterate pages in the requested range.
2. For each page, run page.get_text("dict") to obtain blocks with font metadata.
3. Hunt LaTeX delimiters ($...$, \(...\), \[...\], \begin{equation}...\end{equation}, \begin{align}...\end{align}) over reconstructed line text.
4. For each candidate, attempt sympy.parsing.latex.parse_latex(latex, backend="antlr"). On success, capture expr.free_symbols. On failure, keep the latex with confidence: "low" and continue.
5. For pages where step 3 finds nothing but font analysis hints at math (italic-heavy spans, glyphs from Cambria Math, STIX, LMRoman italic, or a span size more than 1.5x the body median), rasterize the page region and submit to Opus 4.7 vision for equation extraction.

Vision fallback prompt (also a constant in the script):

> "This is a region from a physics paper page that may contain governing equations. Identify every governing equation visible. For each, output {latex, location_description}. Skip inline variable definitions and footnote references. Output strict JSON array. No prose outside JSON."

Deduplication: equations are canonicalized through sympy when possible (sympify(expr) or srepr equality); duplicates that appear in both prose and a numbered display block are merged, keeping the higher-confidence record and the larger bbox.

Phase B — scripts/extract_figures.py

1. For each page in range, render at 200 DPI via page.get_pixmap(dpi=200).
2. Submit the rasterized page to Opus 4.7 vision with a single tightly scoped prompt that asks for figure boxes, captions verbatim, sub-panel labels, and numerical annotations. The system prompt is a module constant; see scripts/extract_figures.py.
3. Crop each figure bbox from the high-DPI rendering and save as /tmp/mobius_figures//fig-{n}.png.
4. For figures with sub-panels, crop and save each sub-panel as fig-{n}-{label}.png.
5. Caption text is stored verbatim. Inline numerical claims found in the caption are stored on the figure record AND propagated into numerical_claims with claim_type: "measurement" and a back-reference to the figure id.

Phase C — scripts/extract_claims.py

1. Concatenate body text (skipping References / Bibliography / Acknowledgments), all figure captions, and the surrounding context of every equation.
2. Chunk into ~4000-token segments with 500-token overlap.
3. For each chunk, call Opus 4.7 with a tight extraction prompt (see scripts/extract_claims.py). The prompt forbids extraction of pure formula symbols, page numbers, and years.
4. Merge chunk results, deduplicate by (value, unit_ucum, source_quote[:50]).
5. Normalize units via Pint loaded with UCUM-compatible definitions. When a unit cannot be parsed, keep the raw string and lower the confidence.

5. Vision invocation protocol

All three phases use the same model and the same beta/no-beta surface:

from anthropic import Anthropic
import base64

client = Anthropic()

response = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=4096,
    system=PHASE_SYSTEM_PROMPT,
    messages=[{
        "role": "user",
        "content": [
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": "image/png",
                    "data": base64.b64encode(png_bytes).decode("utf-8"),
                },
            },
            {"type": "text", "text": PHASE_USER_PROMPT},
        ],
    }],
)

Model identifier is locked to claude-opus-4-7. Adaptive thinking is implicit on Opus 4.7; do not pass budget_tokens (deprecated, returns 400). Temperature defaults are tuned per phase: 0 for equation OCR, 0 for figure detection, 0.2 for claim extraction.

Phase-specific user prompts are stored verbatim as module-level constants in each script:

• EQUATION_VISION_PROMPT in extract_equations.py
• FIGURE_VISION_SYSTEM and FIGURE_VISION_USER in extract_figures.py
• CLAIM_SYSTEM_PROMPT in extract_claims.py

When the orchestrator updates a prompt, it bumps the cache namespace (see section 7) so stale results never come back.

6. Failure modes

Scanned PDFs with poor OCR. PyMuPDF returns degenerate text blocks (no spans, glyph soup). Detection: a page where `len(page.get_text()) / equations.v1.json figures.v1.json claims.v1.json

Cache invalidation is manual: bump `PROMPT_VERSION` at the top of each script. A second invocation on the same PDF reads each phase's JSON in O(file-read) time. Vision cost on a re-run is exactly zero unless prompts changed.

The figure PNGs sit alongside in `/tmp/mobius_figures//` — the JSON paths point there directly, so cache-served runs return paths that are still valid on disk.

## 8. Hand-off to physics-interpreter

The output is the `paper` field of the SimSpec root. `physics-interpreter` reads it under that path:

simspec.paper.equations[].latex simspec.paper.equations[].sympyrepr simspec.paper.equations[].variables simspec.paper.figures[].pngpath simspec.paper.figures[].caption simspec.paper.numerical_claims[]

The interpreter uses `equations[*]` to classify the PDE family, `figures[*].caption` to identify boundary conditions verbally described, and `numerical_claims[*]` to seed parameter values for the simulation. Nothing in the schema is optional from the interpreter's perspective; `paper-parser` is responsible for emitting at least empty arrays when a paper has no figures or no numerical claims.

## 9. Performance expectations

Reference paper: typical arXiv physics preprint, 30 pages, 5 figures, 15 displayed equations, 50 numerical claims.

- Phase A: ~10 s (pure PyMuPDF) + 1-2 vision calls x 8 s ~ 25 s total
- Phase B: 5 figure-bearing pages x 8 s vision ~ 40 s
- Phase C: 10 chunks x 6 s claim extraction ~ 60 s

Total: under 2 minutes on a Managed Agents container. Token budget: roughly 40 k Opus tokens per paper, dominated by Phase B image input. Cache hit on a re-run: ~0.5 s end-to-end.

When a paper exceeds these by more than 3x, the orchestrator should treat it as an outlier and route to a chunked re-parse with more aggressive page filtering.

## 10. Adversarial note — multi-panel figures on dense 2-column papers

Counterexample known to break v1: figures with 3x3 sub-panel grids where the per-panel label sits in the upper-left corner in 6-pt sans-serif and the caption is split across the two columns of text below. On a single full-page rasterization at 200 DPI, the label glyphs are around 9 pixels tall — Opus 4.7 vision identifies the figure and extracts the joint caption, but assigns inconsistent panel labels (sometimes `(a)..(i)`, sometimes `(a)..(c)` repeated three times across rows). Confidence: this fails on roughly 1 in 5 dense biophysics / condensed matter papers in ad-hoc testing.

v2 strategy (already wired into Phase B as a partial fallback): crop the figure bbox at 2x zoom and re-submit with a prompt that explicitly enumerates expected panel labels. If the label set still does not stabilise, fall back to per-panel cropping driven by horizontal/vertical white-space detection in the cropped figure (`scipy.ndimage.label` on a thresholded grayscale), then re-run vision per sub-crop. This is implemented for a single zoom step in v1 — full white-space-driven cropping is a v2 deliverable. The orchestrator should treat any figure record whose sub-panel labels are `?` or whose `confidence < 0.5` as a candidate for the v2 path, log it, and continue.

This is the single largest source of low-confidence output on real arXiv corpora and is called out explicitly so downstream skills do not silently propagate ambiguous panel labels into the simulator's UI.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [leventilo](https://github.com/leventilo)
- **Source:** [leventilo/mobius](https://github.com/leventilo/mobius)
- **License:** MIT

Install and usage instructions live in the source repository linked above.