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Demo Codebase for agentic-research-measurement-probes

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

Updated: 2026-09-04
Metadata Last Updated: 2026-03-26 00:00:00
Date Created: N/A
Data Provided by:
Dataset Owner: N/A

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Table representation of structured data
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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    "accessLevel": "public",
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        "fn": "Michael Majurski"
    },
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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.",
    "language": [
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    "distribution": [
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            "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"
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    "modified": "2026-03-26 00:00:00",
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        "name": "National Institute of Standards and Technology"
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    "theme": [
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