{"submission_id":"750a5531-a26b-481a-8e70-8a713386cf2b","submission_revision_id":"c5e46fea-dbc4-4aad-af2d-0cc030f6eda7","revision_version":1,"submission_status":"under_review","submitted_at":"2026-08-13T04:25:26.540523Z","submitter_name":"Jingxu Xie","submitter_email":"jingxuxie@berkeley.edu","task":{"status":"submitted","version":1,"extra_fields":{"ai_draft":{"source":"submissions_workbench","status":"ready","preview":{"domain":"Credit Union / Financial Compliance","task_goal":"You are given `Input_Materials_case_file.pdf`, a six-page synthetic consumer-onboarding case file. Create a securely redacted PDF named `case_file_redacted.pdf`.\n\nPermanently redact all personally identifiable information belonging to the two protected applicants, **Jordan Lee** and **Maya Patel**, including every occurrence of:\n\n- their names and signatures;\n- dates of birth and Social Security numbers;\n- residential addresses, personal email addresses, and personal phone numbers;\n- customer IDs, driver's-license identifiers, financial account numbers, and routing numbers; and\n- the same information when it appears in scanned images, PDF metadata, comments/annotations, or interactive form fields.\n\nUse opaque black redactions and preserve the original six-page order, layout, and readability. The redactions must be permanent: moving or deleting a black shape, extracting text, inspecting metadata or annotations, or reading form fields must not reveal the protected information.\n\nDo **not** redact non-protected business and case information. In particular, preserve `Jordan Lake Branch`, `Maya Consulting LLC`, `Elena Garcia`, `Northstar Credit Union`, case identifier `AC-2026-0417`, review and transaction dates, transaction descriptions, and transaction amounts.","short_name":"cu_pdf_pii_redaction","input_files":"- Input_Materials_case_file.pdf, a six-page synthetic consumer-onboarding case file.","output_files":"- A securely redacted PDF named case_file_redacted.pdf, with the same six pages in the same order.","operating_system":"Windows, Linux, MacOS","software_licensing":"Free/Open Source","verification_method":"1. Open the output in an independent PDF viewer and inspect all six pages. Confirm each protected value is covered and each required near-miss remains readable.\n2. Render every output page to images. OCR page 4 (the scanned identity page) and confirm that its protected name, birth date, address, license number, customer ID, and signature cannot be recovered.\n3. Extract text from all pages and search for the evaluator's forbidden-value list. There should be no matches.\n4. Inspect document metadata, the AcroForm field tree, page annotations, and embedded object content. Metadata must be sanitized, and the output must contain zero form fields, widget annotations, and comments.\n5. Confirm the output has six US Letter pages and that the required preservation anchors, transaction facts, and overall layout agree with the input and reference output.\n\n`Evaluation_Manifest.json` contains the evaluator-only forbidden and preservation anchors used for this synthetic example. Do not give that manifest or the reference output to the evaluated agent.","evaluation_criterion":"The output passes only if all of the following are true:\n\n1. **Complete PII removal:** No protected identifier remains visibly readable or recoverable from page text, raster images, metadata, annotations, form values, or other PDF objects.\n2. **Correct entity handling:** Near-miss organization and staff names remain visible; for example, the word `Jordan` in `Jordan Lake Branch` and `Maya` in `Maya Consulting LLC` must not be removed merely because they resemble protected names.\n3. **Content preservation:** Non-sensitive labels, case facts, dates, transaction descriptions, and amounts match the input/reference output, apart from harmless PDF-rendering differences.\n4. **Document integrity:** The result opens without warnings, contains exactly six US Letter pages in the original order, has no remaining interactive form fields or comment annotations, and uses opaque black redaction marks.\n\nThis should be graded by content and rendered-page comparison, not by PDF file hash, because different valid tools may serialize equivalent PDFs differently. Any recoverable protected PII is a critical failure. An overly broad redaction that removes a required near-miss or material case content also fails.","software_and_version":"Adobe Acrobat Pro DC, OCRmyPDF, Python 3.11","tools_software_details":"Software: Adobe Acrobat Pro DC, OCRmyPDF, Python 3.11\nOCR and identify PII spans across all pages (Tools: OCRmyPDF, Adobe Acrobat Pro DC)\nApply permanent redaction marks and flatten the document (Tools: Adobe Acrobat Pro DC)\nExport redaction coordinates and PII type into a structured log (Tools: Python 3.11)\nAny PDF editor that supports permanent redaction, OCR/image redaction, metadata removal, annotation removal, and form sanitization. The task is software-agnostic."},"warnings":["estimated_expert_hours \"\" does not meet the ≥5 hour requirement"],"createdAt":1786594656840,"formPrefill":{"extra_fields":{},"industry_domain":"Credit Union / Financial Compliance","task_short_name":"cu_pdf_pii_redaction","operating_system":"Windows, Linux, MacOS","task_description":"# Task Goal\n\nYou are given `Input_Materials_case_file.pdf`, a six-page synthetic consumer-onboarding case file. Create a securely redacted PDF named `case_file_redacted.pdf`.\n\nPermanently redact all personally identifiable information belonging to the two protected applicants, **Jordan Lee** and **Maya Patel**, including every occurrence of:\n\n- their names and signatures;\n- dates of birth and Social Security numbers;\n- residential addresses, personal email addresses, and personal phone numbers;\n- customer IDs, driver's-license identifiers, financial account numbers, and routing numbers; and\n- the same information when it appears in scanned images, PDF metadata, comments/annotations, or interactive form fields.\n\nUse opaque black redactions and preserve the original six-page order, layout, and readability. The redactions must be permanent: moving or deleting a black shape, extracting text, inspecting metadata or annotations, or reading form fields must not reveal the protected information.\n\nDo **not** redact non-protected business and case information. In particular, preserve `Jordan Lake Branch`, `Maya Consulting LLC`, `Elena Garcia`, `Northstar Credit Union`, case identifier `AC-2026-0417`, review and transaction dates, transaction descriptions, and transaction amounts.\n\n# Input Files\n\n- Input_Materials_case_file.pdf, a six-page synthetic consumer-onboarding case file.\n\n# Output Files\n\n- A securely redacted PDF named case_file_redacted.pdf, with the same six pages in the same order.\n\n# Tools / Software Details\n\nSoftware: Adobe Acrobat Pro DC, OCRmyPDF, Python 3.11\nOCR and identify PII spans across all pages (Tools: OCRmyPDF, Adobe Acrobat Pro DC)\nApply permanent redaction marks and flatten the document (Tools: Adobe Acrobat Pro DC)\nExport redaction coordinates and PII type into a structured log (Tools: Python 3.11)\nAny PDF editor that supports permanent redaction, OCR/image redaction, metadata removal, annotation removal, and form sanitization. The task is software-agnostic.\n\n# Evaluation Criterion\n\nThe output passes only if all of the following are true:\n\n1. **Complete PII removal:** No protected identifier remains visibly readable or recoverable from page text, raster images, metadata, annotations, form values, or other PDF objects.\n2. **Correct entity handling:** Near-miss organization and staff names remain visible; for example, the word `Jordan` in `Jordan Lake Branch` and `Maya` in `Maya Consulting LLC` must not be removed merely because they resemble protected names.\n3. **Content preservation:** Non-sensitive labels, case facts, dates, transaction descriptions, and amounts match the input/reference output, apart from harmless PDF-rendering differences.\n4. **Document integrity:** The result opens without warnings, contains exactly six US Letter pages in the original order, has no remaining interactive form fields or comment annotations, and uses opaque black redaction marks.\n\nThis should be graded by content and rendered-page comparison, not by PDF file hash, because different valid tools may serialize equivalent PDFs differently. Any recoverable protected PII is a critical failure. An overly broad redaction that removes a required near-miss or material case content also fails.","software_licensing":"Free/Open Source","verification_method":"1. Open the output in an independent PDF viewer and inspect all six pages. Confirm each protected value is covered and each required near-miss remains readable.\n2. Render every output page to images. OCR page 4 (the scanned identity page) and confirm that its protected name, birth date, address, license number, customer ID, and signature cannot be recovered.\n3. Extract text from all pages and search for the evaluator's forbidden-value list. There should be no matches.\n4. Inspect document metadata, the AcroForm field tree, page annotations, and embedded object content. Metadata must be sanitized, and the output must contain zero form fields, widget annotations, and comments.\n5. Confirm the output has six US Letter pages and that the required preservation anchors, transaction facts, and overall layout agree with the input and reference output.\n\n`Evaluation_Manifest.json` contains the evaluator-only forbidden and preservation anchors used for this synthetic example. Do not give that manifest or the reference output to the evaluated agent.","software_and_version":"Adobe Acrobat Pro DC, OCRmyPDF, Python 3.11"},"draftSession":"e6a0f8d3-78f9-4776-9dee-5144393d491f"}},"submitted_at":"2026-08-13T04:25:26.540523+00:00","industry_domain":"Credit Union / Financial Compliance","task_short_name":"cu_pdf_pii_redaction","operating_system":"Windows, Linux, MacOS","task_description":"# Task Goal\n\nYou are given `Input_Materials_case_file.pdf`, a six-page synthetic consumer-onboarding case file. Create a securely redacted PDF named `case_file_redacted.pdf`.\n\nPermanently redact all personally identifiable information belonging to the two protected applicants, **Jordan Lee** and **Maya Patel**, including every occurrence of:\n\n- their names and signatures;\n- dates of birth and Social Security numbers;\n- residential addresses, personal email addresses, and personal phone numbers;\n- customer IDs, driver's-license identifiers, financial account numbers, and routing numbers; and\n- the same information when it appears in scanned images, PDF metadata, comments/annotations, or interactive form fields.\n\nUse opaque black redactions and preserve the original six-page order, layout, and readability. The redactions must be permanent: moving or deleting a black shape, extracting text, inspecting metadata or annotations, or reading form fields must not reveal the protected information.\n\nDo **not** redact non-protected business and case information. In particular, preserve case identifier, review and transaction dates, transaction descriptions, and transaction amounts.\n\n# Input Files\n\n- Input_Materials_case_file.pdf, a six-page synthetic consumer-onboarding case file.\n\n# Output Files\n\n- A securely redacted PDF named case_file_redacted.pdf, with the same six pages in the same order.\n\n# Tools / Software Details\n\nSoftware: Adobe Acrobat Pro DC, OCRmyPDF, Python 3.11\nOCR and identify PII spans across all pages (Tools: OCRmyPDF, Adobe Acrobat Pro DC)\nApply permanent redaction marks and flatten the document (Tools: Adobe Acrobat Pro DC)\nExport redaction coordinates and PII type into a structured log (Tools: Python 3.11)\nAny PDF editor that supports permanent redaction, OCR/image redaction, metadata removal, annotation removal, and form sanitization. The task is software-agnostic.\n\n# Evaluation Criterion\n\nThe output passes only if all of the following are true:\n\n1. **Complete PII removal:** No protected identifier remains visibly readable or recoverable from page text, raster images, metadata, annotations, form values, or other PDF objects.\n2. **Correct entity handling:** Near-miss organization and staff names remain visible; for example, the word `Jordan` in `Jordan Lake Branch` and `Maya` in `Maya Consulting LLC` must not be removed merely because they resemble protected names.\n3. **Content preservation:** Non-sensitive labels, case facts, dates, transaction descriptions, and amounts match the input/reference output, apart from harmless PDF-rendering differences.\n4. **Document integrity:** The result opens without warnings, contains exactly six US Letter pages in the original order, has no remaining interactive form fields or comment annotations, and uses opaque black redaction marks.\n\nThis should be graded by content and rendered-page comparison, not by PDF file hash, because different valid tools may serialize equivalent PDFs differently. Any recoverable protected PII is a critical failure. An overly broad redaction that removes a required near-miss or material case content also fails.","software_licensing":"Free/Open Source","evaluation_criteria":null,"verification_method":"1. Open the output in an independent PDF viewer and inspect all six pages. Confirm each protected value is covered and each required near-miss remains readable.\n2. Render every output page to images. OCR page 4 (the scanned identity page) and confirm that its protected name, birth date, address, license number, customer ID, and signature cannot be recovered.\n3. Extract text from all pages and search for the evaluator's forbidden-value list. There should be no matches.\n4. Inspect document metadata, the AcroForm field tree, page annotations, and embedded object content. Metadata must be sanitized, and the output must contain zero form fields, widget annotations, and comments.\n5. Confirm the output has six US Letter pages and that the required preservation anchors, transaction facts, and overall layout agree with the input and reference output.\n\n`Evaluation_Manifest.json` contains the evaluator-only forbidden and preservation anchors used for this synthetic example. Do not give that manifest or the reference output to the evaluated agent.","software_and_version":"Adobe Acrobat Pro DC, OCRmyPDF, Python 3.11","specific_requirements":null,"expected_completion_time":null},"files":[{"file_id":"ded526cb-245d-4850-be22-2c1dfa73e35f","filename":"Input_Materials_case_file.pdf","category":"input_materials","content_type":"application/pdf","size_bytes":96555},{"file_id":"d97a74be-d4bf-4615-b4c2-5e03e332a9ac","filename":"Reference_Output_case_file_redacted.pdf","category":"reference_output","content_type":"application/pdf","size_bytes":78130},{"file_id":"c40039d2-f708-443b-b998-b3813acd89d5","filename":"Evaluation_Manifest.json","category":"reference_output","content_type":"application/json","size_bytes":1910}],"deep_review":{"overall_verdict":"Strong Accept","review_text":"**Overall Verdict:** Strong Accept — The submission is complete, securely constructed, objectively gradable, and reachable through both commercial and open-source PDF workflows.\n\n**Completeness Check:**\n- Instruction: Complete — Clearly defines protected PII, preservation exceptions, permanent-redaction requirements, output name, and structural constraints.\n- Inputs: Complete — The readable, unencrypted input contains six US Letter pages with searchable text, an image-based identity page, four form widgets, sensitive metadata, and a comment annotation.\n- Toolchain: Complete — The workflow is feasible with legally obtainable PDF and OCR tools; compatible alternatives are explicitly allowed.\n- Reference output: Complete — The six-page reference preserves layout and required content, uses opaque black redactions, modifies the scanned image pixels, sanitizes metadata, and contains no forms or annotations.\n- Rubric: Complete — The evaluator manifest provides protected values, preservation anchors, structural requirements, image OCR checks, and clear critical-failure conditions.\n\n**File and Task Reachability:**\nAttachment classification: `reachable`. Task classification: `reachable`. `Input_Materials_case_file.pdf`, `Reference_Output_case_file_redacted.pdf`, and `Evaluation_Manifest.json` were all inspected in full; their actual formats and byte counts agree with the attachment manifest. Both PDFs parse successfully and contain exactly six 612-by-792-point pages in the correct order. The JSON is valid US-ASCII JSON with a coherent evaluator-only schema covering 20 forbidden values, 11 preservation anchors, structure, and page-4 image checks. The reference contains no forbidden matches in extracted text or serialized bytes, retains every preservation anchor, has no embedded files, forms, widgets, comments, signatures, incremental revisions, or hidden prior version, and visibly redacts the extracted page-4 image at the pixel level. No attachment content was left unreviewed. Automated OCR was unavailable, so page 4 was verified through full-resolution inspection of the extracted image and rendered page rather than an OCR pass; this is a reviewer-tooling limitation, not a submission defect. The solver-visible input is correctly separated from the evaluator-only reference and manifest, and the path from input through permanent sanitization to objective evaluation is complete. License keys, accounts, tokens, and other credentials are not required from the contributor. Confidential, proprietary, or internal material may be evaluated as submitted evidence without requiring additional disclosure.\n\n**Special Screening:**\nNone\n\n**Recommended Changes:**\nThe submission is ready and requires no material changes. As optional metadata cleanup, the licensing field could distinguish commercial Adobe Acrobat from the permitted open-source alternatives.","reviewed_at":"2026-08-15T00:37:12.651900Z"},"claim":null}