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WeaveBench / tasks /DOC /DOC_task_2_pdf_form_fill.md
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metadata 
id: DOC_task_2_pdf_form_fill
name: 复杂 PDF 交互表单填写与签名
category: DOC
timeout_seconds: 1200

Prompt

⚙️ Execution convention: This task is graded on deliverable files; no one is approving anything. Just execute — do not ask "if you want me to…" questions, do not wait for approval. All deliverables must land in the /tmp_workspace/ root: lease_signed.pdf, step_*.png, actions.log. Input assets are in /tmp_workspace/inputs/. When done, run ls -la /tmp_workspace/ as a self-check before exiting.

Background: /tmp_workspace/lease_agreement.pdf is a multi-page interactive form PDF — 18 pages and ~1265 AcroForm fields (text / checkbox / radio / signature). Tenant data is in /tmp_workspace/inputs/tenant.json; the signature image is inputs/signature.png and the initials image is inputs/initials.png.

Goal: fill out this lease form, add the e-signature and initials images, and export lease_signed.pdf.

Hard constraints on lease_signed.pdf

  • File exists at /tmp_workspace/lease_signed.pdf, page count remains 18
  • AcroForm structure must be preserved (PDF root contains /AcroForm); must not be flattened into a static PDF
  • At least 50 text fields actually filled (the number of fields with a non-empty value via pypdf.PdfReader.get_form_text_fields() must be ≥ 50)
  • At least 5 button / checkbox / radio fields set (/FT == /Btn and /V is not /Off)
  • Page 4 has at least 1 radio / button field selected
  • The PDF contains at least 5 embedded images (/Subtype /Image appearing in the XObject of page resources) — used to carry the e-signature plus multiple initials stamps
  • At least a few field values from tenant.json (name / SSN / address / phone / move-in date / monthly rent / deposit / bank account / emergency contact, etc.) can be matched literally inside the PDF field values

Anti-cheat / action log

  • /tmp_workspace/actions.log is an action log (any text format) recording the steps used to fill the form
  • actions.log must not contain any of these literals: pdftk fill_form, update_page_form_field_values, cli_fill — the pure-CLI bulk-fill shortcut is not allowed
  • Use a PDF editor with form support (Master PDF Editor / Okular / LibreOffice Draw, etc.) to genuinely type values into the fields

5 progress screenshots (located in /tmp_workspace/)

  • File naming: step_*.png (any name, at least 5)
  • Each ≥ 5 KB
  • All 5 md5 hashes distinct
  • Screenshots overall must show a genuine, readable PDF editor UI (an OCR check is performed):
    • At least 1 screenshot showing a form editing panel such as field properties / Form Fields / Annotation
    • At least 1 screenshot showing recognizable PDF editor UI (editor name / Insert Image / Stamp / Edit Form, etc.)

Expected Behavior

设计意图与典型解题路径（仅供出题人参考，不发给 agent）：

  1. PDF 实际内容是 IRS Form 1040 + 1065 + 1120-S + 706 拼接（18 页 / ~1265 个 AcroForm 字段）。
  2. 推荐 GUI：Master PDF Editor / Okular（带表单支持）/ LibreOffice Draw 等任一带表单支持的 PDF 编辑器，逐字段填值。
  3. 按 tenant.json 填入 ≥ 50 个 text 字段（姓名 / SSN / 地址 / 电话 / 入住日期 / 月租金 / 押金 / 银行账户 / 紧急联系人）。
  4. checkbox 任选 5 处勾上（宠物押金、停车位、家电附带、保险、转租条款；可在 1040 / 1065 / 1120-S Schedule B 的 Yes/No checkbox 中挑）。
  5. 第 4 页：在 1065 Schedule B 第 1 题（"What type of entity is filing this return"）选 "Domestic general partnership" radio。
  6. 用 PDF 编辑器的 "Add Image" / "Stamp" 功能：
    • 第 14 页 706 Form "Signature of executor" 位置贴 inputs/signature.png
    • 在第 6 / 8 / 10 / 12 / 17 页（任选 5 处 "Initial here"）贴 inputs/initials.png
  7. 保存 为 lease_signed.pdf（用编辑器的 Save，不要 Print to PDF —— Print to PDF 会 flatten AcroForm 结构，flatten 后 grader 读不到字段值）。
  8. 截 5 张关键步骤截图 step_*.png，至少有一张能看到字段属性面板，至少一张能看到编辑器名称 / Insert Image / Stamp 等关键 UI。
  9. 写 actions.log 记录操作历史（任何文本格式都行）。

约束说明：

  • 不要走 pdftk fill_form / pypdf.update_page_form_field_values 这条 CLI 批量 fill 路径 —— grader 检 actions.log 是否含这些字面量
  • AcroForm 必须保留：grader 检 PDF root 含 /AcroForm + 用 get_form_text_fields() 读非空字段 ≥ 50

评分要点（hard gates）：

  • lease_signed.pdf 不存在 → cap 0.10
  • text fields 填值率 < 60% → cap 0.40
  • buttons 勾选率 < 60% → cap 0.45
  • 第 4 页 radio 未选 → cap 0.55
  • 内嵌图 < 60%（即 < 3 张）→ cap 0.45
  • AcroForm 被 flatten → cap 0.40
  • 截图无字段属性面板 → cap 0.50
  • actions.log 含禁止字面量 → cap 0.30
  • 没截图 OCR 命中 PDF 编辑器名 → cap 0.40
  • 5 张截图 md5 不唯一 → cap 0.55
  • OCR 不可用 → cap 0.60

Source

Grading Criteria

  • lease_signed.pdf 存在
  • pypdf.get_form_text_fields() 返回已填字段数 ≥ 50
  • tenant.json 中至少 8 个值能在表单字段中找到
  • PDF 中包含 ≥ 5 张嵌入图（pypdf 检查 /XObject /Image，覆盖签名 + 4 个 initials 位置）
  • AcroForm 仍存在（/AcroForm in pdf root），未 flatten
  • step_*.png ≥ 5 张，且 md5 互不相同、每张 ≥ 5 KB
  • 至少 1 张截图含字段属性词汇（OCR "Field"/"属性"/"Properties"）
  • 至少勾选 5 处 button/checkbox 字段（pypdf get_fields() 中 /Btn 类型且值非空/非 Off）
  • 第 4 页 radio 选择了一个非空选项

Automated Checks 

def grade(**kwargs) -> dict:
    """Strict grader against REAL IRS f1040+f1065+f1120s+f706 (1265 fields, 18 pages).

    Scoring: weighted 0.6*core + 0.3*gui + 0.1*aux, with multi-layer hard gates
    (no-cli-fill, fields_filled, buttons, images, AcroForm-kept, OCR proof of GUI).
    """
    from pathlib import Path
    import json, subprocess
    s = {}
    pdf = Path("/tmp_workspace/lease_signed.pdf")
    inp = Path("/tmp_workspace/inputs/tenant.json")
    gt  = Path("/tmp_workspace/gt/expected.json")
    expected = json.loads(gt.read_text()) if gt.exists() else {}
    tenant   = json.loads(inp.read_text()) if inp.exists() else {}
    s["pdf_exists"] = 1.0 if pdf.exists() else 0.0
    n_filled = n_data_hits = n_images = pages_ok = acroform_ok = 0
    n_buttons_checked = 0
    page4_radio_set = 0
    if pdf.exists():
        try:
            from pypdf import PdfReader
            R = PdfReader(str(pdf))
            pages_ok = 1 if len(R.pages) >= 18 else 0
            fields_dict = R.get_form_text_fields() or {}
            # strict: ≥50 text fields actually filled (≥4% of 1265 fields, hard-gate at 0.6 = 30)
            n_filled = sum(1 for v in fields_dict.values() if v and str(v).strip())
            tenant_strs = [str(v).strip() for v in tenant.values() if v]
            for ts in tenant_strs:
                if any(ts in (str(v) or "") for v in fields_dict.values()): n_data_hits += 1
            root = R.trailer["/Root"]
            acroform_ok = 1 if "/AcroForm" in root else 0
            # Count button/checkbox/radio fields that are toggled on (value not empty/Off)
            try:
                all_fields = R.get_fields() or {}
                for fname, fobj in all_fields.items():
                    try:
                        ftype = fobj.get("/FT")
                        fval = fobj.get("/V")
                        if ftype == "/Btn" and fval is not None:
                            sval = str(fval)
                            if sval and sval not in ("/Off", "Off", "/", ""):
                                n_buttons_checked += 1
                    except Exception: pass
                # Detect any radio/button selection on page 4
                try:
                    p4 = R.pages[3]
                    annots = p4.get("/Annots") or []
                    for a in annots:
                        try:
                            ao = a.get_object()
                            if ao.get("/Subtype") == "/Widget" and ao.get("/FT") == "/Btn":
                                v = ao.get("/V") or ao.get("/AS")
                                if v is not None and str(v) not in ("/Off", "Off", "/", ""):
                                    page4_radio_set = 1
                                    break
                        except Exception: pass
                except Exception: pass
            except Exception: pass
            for p in R.pages:
                xo = (p.get("/Resources") or {}).get("/XObject") or {}
                for k in xo:
                    try:
                        if xo[k].get_object().get("/Subtype")=="/Image": n_images += 1
                    except Exception: pass
        except Exception as e:
            s["pdf_error"] = str(e)[:100]
    # Stricter: require ≥50 actually-filled text fields (was 30) — agent must really sit and type
    s["fields_filled"]   = min(1.0, n_filled / 50.0)
    s["buttons_checked"] = min(1.0, n_buttons_checked / 5.0)
    s["page4_radio"]     = float(page4_radio_set)
    s["data_value_hits"] = min(1.0, n_data_hits / max(1,min(10,len(tenant))))
    s["page_count"]      = pages_ok * 1.0
    s["acroform_kept"]   = acroform_ok * 1.0
    # Stricter: 1 signature + 4 initials = 5 image overlays
    s["images_embedded"] = min(1.0, n_images / 5.0)
    # screenshots — require 5 unique, non-trivial PNGs (anti-cheat: md5 dedup + size floor)
    import hashlib
    steps_all = sorted(Path("/tmp_workspace").glob("step_*.png"))
    seen_md5 = set(); good_steps = []
    for p in steps_all:
        try:
            data = p.read_bytes()
            if len(data) < 5120:  # < 5KB → almost certainly a placeholder
                continue
            h = hashlib.md5(data).hexdigest()
            if h in seen_md5:
                continue
            seen_md5.add(h); good_steps.append(p)
        except Exception:
            pass
    steps = good_steps
    s["screenshots"] = min(1.0, len(steps) / 5.0)
    s["screenshots_unique"] = 1.0 if len(seen_md5) >= 5 else (len(seen_md5) / 5.0)
    panel_screen = False
    ocr_available = False
    try:
        import pytesseract
        from PIL import Image
        ocr_available = True
        for p in steps:
            tx = pytesseract.image_to_string(Image.open(p))
            if any(k in tx for k in ["Field","属性","Properties","Form Fields","Annotation",
                                      "Master PDF","Okular","Edit","Tools","Document"]):
                panel_screen = True; break
    except Exception:
        # OCR unavailable — do NOT auto-credit (Warmup installs tesseract+pytesseract)
        panel_screen = False
    s["field_panel_visible"] = 1.0 if panel_screen else 0.0
    # No-cheat: forbid pdftk fill_form / pypdf update_page_form_field_values usage
    log = Path("/tmp_workspace/actions.log")
    forbidden = False
    if log.exists():
        t = log.read_text(errors="ignore")
        if any(b in t for b in ["pdftk fill_form","update_page_form_field_values","cli_fill"]): forbidden = True
    s["no_cli_fill"] = 0.0 if forbidden else 1.0
    # Hard GUI gate: at least one screenshot must OCR to a real PDF editor name
    # (Master PDF Editor / Okular form panel / LibreOffice Draw form fields).
    pdf_ui_ocr = 0.0
    try:
        import pytesseract as _pt
        from PIL import Image as _PI
        for p in steps:
            try:
                tx = _pt.image_to_string(_PI.open(p))
                if any(k in tx for k in ["Master PDF","Okular","Form Fields",
                                          "Edit Form","Annotation","LibreOffice Draw",
                                          "Insert Image","Stamp"]):
                    pdf_ui_ocr = 1.0; break
            except Exception: pass
    except ImportError:
        pdf_ui_ocr = 0.0  # OCR unavailable — no credit (Warmup 已要求安装 tesseract+pytesseract)
    s["pdf_editor_ocr"] = pdf_ui_ocr
    nums = [s[k] for k in s if isinstance(s[k],(int,float))]
    # Weighted aggregate — core deliverable 60%, GUI evidence 30%, aux 10%
    core_keys = ["pdf_exists","fields_filled","buttons_checked","page4_radio",
                 "data_value_hits","acroform_kept","images_embedded"]
    gui_keys  = ["screenshots","screenshots_unique","field_panel_visible",
                 "pdf_editor_ocr","no_cli_fill"]
    aux_keys  = ["page_count"]
    def _avg(keys):
        vs = [s[k] for k in keys if k in s and isinstance(s[k],(int,float))]
        return sum(vs)/len(vs) if vs else 0.0
    base = 0.6*_avg(core_keys) + 0.3*_avg(gui_keys) + 0.1*_avg(aux_keys)
    # Hard gates — agent must really do the work, not just produce shells
    if s.get("pdf_exists",0) < 1.0:                base = min(base, 0.10)
    if s.get("fields_filled",0) < 0.6:             base = min(base, 0.40)   # was 0.45
    if s.get("buttons_checked",0) < 0.6:           base = min(base, 0.45)   # was 0.6
    if s.get("page4_radio",0) < 1.0:               base = min(base, 0.55)
    if s.get("images_embedded",0) < 0.6:           base = min(base, 0.45)
    if s.get("acroform_kept",0) < 1.0:             base = min(base, 0.40)   # flatten = fail core
    if s.get("field_panel_visible",0) < 1.0:       base = min(base, 0.50)   # was 0.55
    if s.get("no_cli_fill",0) < 1.0:               base = min(base, 0.30)   # was 0.4
    if s.get("pdf_editor_ocr",0) < 0.5:            base = min(base, 0.40)   # was 0.55
    if s.get("screenshots_unique",0) < 1.0:        base = min(base, 0.55)   # md5 dedup gate
    # If OCR pipeline unavailable, cap overall at 0.6 (don't let infra gap = full marks)
    if not ocr_available:                          base = min(base, 0.60)
    s["overall_score"] = round(base, 3)
    return s

Workspace Path

workspace/DOC/task_2_pdf_form_fill/

Skills

Env

Warmup 

which okular >/dev/null 2>&1 || (apt-get update -qq && DEBIAN_FRONTEND=noninteractive apt-get install -y -qq okular okular-extra-backends) || true
which masterpdfeditor5 >/dev/null 2>&1 || (curl -fsSLo /tmp/mpe.deb https://get.code-industry.net/public/master-pdf-editor-5.9.91-qt5.x86_64.deb && DEBIAN_FRONTEND=noninteractive apt-get install -y -qq /tmp/mpe.deb) || true
pip install -q pypdf pillow reportlab || true
pdfinfo /tmp_workspace/lease_agreement.pdf >/dev/null 2>&1 || true
apt-get install -y -qq tesseract-ocr || true
pip install -q pytesseract pillow numpy || true