Research
Our research team explores how human intelligence shapes AI systems, publishing findings that push the boundaries of data quality, evaluation frameworks and expert-in-the-loop training.
Featured · RLHF
January 2026
We present a framework for integrating domain expert feedback into RLHF pipelines at scale, demonstrating that expert-curated preference data yields significantly stronger model alignment than crowd-sourced alternatives across reasoning, safety, and factuality benchmarks.
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Recent work from the Think5 research team.
This paper introduces a multi-layered quality assurance methodology for human data operations, combining automated consistency checks with expert peer review to achieve 99.7% accuracy rates across 100+ knowledge domains.
We describe the architecture of a production data pipeline that enables domain experts to create, annotate, and validate multi-modal training data for vision-language models, reducing dataset preparation time by 60%.
Through extensive experimentation, we show that red-teaming conducted by domain experts uncovers 3.2x more critical safety vulnerabilities compared to general crowd workers, with higher-severity findings across medical, legal, and financial domains.
We analyze data quality patterns from our global network of expert contributors, revealing that rigorous vetting and continuous performance tracking—not geography—are the primary predictors of output quality for AI training data.
An analysis of the cost-quality tradeoff in AI training data production, demonstrating that investing in expert-generated data yields 5-10x ROI compared to low-cost alternatives when measured by downstream model performance improvements.
Research areas
How AI can identify and assess domain experts at scale while maintaining rigorous quality standards. Our work on Aria explores conversational AI interviewing, multi-language assessment, and anti-fraud detection.
Frameworks for measuring, validating, and ensuring the quality of human-generated training data. We study expert calibration, inter-annotator agreement, and performance-driven talent optimization.
Developing rigorous benchmarks and evaluation methods for AI systems trained on human feedback. Our research covers RLHF evaluation, safety testing methodologies, and domain-specific model assessment.
Collaborate
We partner with labs and universities on data quality, evaluation and expert-in-the-loop training.