{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["006:55"], "contactPoint": {"fn": "Michael Majurski", "hasEmail": "mailto:michael.majurski@nist.gov"}, "description": "An agentic AI measurement tool for deep research over local corpora of PDF and Markdown documents. Given a research question, it orchestrates a programmatic AI pipeline to exhaustively evaluate, synthesize, and verify information from your documents, producing a Markdown report with inline footnote citations -- then automatically measures the quality of every citation using LM-judge measurement probes.\n\nThe probes are a first-class feature, not an afterthought. After each report section is written, three mutually exclusive probe evaluators run automatically, scoring every citation along distinct quality dimensions: faithfulness (does the source support the claim?), completeness (is the source's full message represented without cherry-picking?), and sufficiency (does the source carry the evidentiary burden the claim requires, or does the author overreach?). Probe results are stored alongside the report as a structured audit trail, enabling quantitative measurement of AI-generated research quality.", "distribution": [{"accessURL": "https://github.com/usnistgov/agentic-research-measurement-probes", "description": "Github repository of the agentic-research-measurement-probes demo", "title": "agentic-research-measurement-probes Github Repository"}], "identifier": "ark:/88434/mds2-4158", "issued": "2026-03-27", "keyword": ["Agentic-AI Measurement", "Deep Research", "LLM"], "landingPage": "https://data.nist.gov/od/id/mds2-4158", "language": ["en"], "license": "https://www.nist.gov/open/license", "modified": "2026-03-26 00:00:00", "programCode": ["006:045"], "publisher": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "theme": ["Information Technology:Software research"], "title": "Demo Codebase for agentic-research-measurement-probes"}