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.
The 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.
About this Dataset
| Title | Demo Codebase for agentic-research-measurement-probes |
|---|---|
| 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. The 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. |
| Modified | 2026-03-26 00:00:00 |
| Publisher Name | National Institute of Standards and Technology |
| Contact | mailto:[email protected] |
| Keywords | Agentic-AI Measurement , LLM , Deep Research |
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"title": "Demo Codebase for agentic-research-measurement-probes",
"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.",
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