Experimental
AI-Assisted Research Validation
Whether AI agents can reliably check citations, code, and sensitivity inside a reproducible workflow.
Status
- Experimental
Version
- Experimental
Date
- 2026-04
Authors
- Aryan Patel
Abstract
An experimental systems program testing whether AI agents can reliably check citation validity, code reliability, and sensitivity inside a reproducible research workflow, and where automation is safe versus where it introduces hidden risk. AI agents act as software roles under human approval, not as independent researchers.
Research question
Can an AI agent reliably test citation validity, code, and sensitivity inside a research workflow?
Methods
- Citation resolution checks
- Multi-agent error reduction
- Automated critique
- Measuring how much human review changes conclusions
Data
Internal research workflows, logs, and artifacts, preserved for analysis.
Results
Experimental. Preliminary analysis; conclusions are not finalized.
Limitations
Findings about automation safety are preliminary and depend on the specific systems studied.
Code availability
- Internal experimental system.
Data availability
- Internal.
AI disclosure
- AI agents assisted with literature retrieval, code generation, analysis support, critique, and drafting. Deterministic systems produced and verified the estimates. A human researcher approved the question, design, interpretation, and release.
Reproduction status
- Experimental system.