Research from Natton Labs.
Methods, evaluations, and lessons from frontier deployments. Independent, peer-reviewable, reproducible.
Calibrating reward models against drift
Methods to keep reward models accurate as the policy improves.
Inter-annotator agreement for long-form generation
An evaluation framework for multi-paragraph generations.
Counterfactual fairness probes for Indic LLMs
Slice-level probes across caste, region, dialect, gender.
Human-in-the-Loop Evaluation for Enterprise AI
A practical framework for combining expert reviewers with automated evaluation to improve enterprise AI reliability.
Optimizing AI Model Performance with High-Quality Data
Research on how curated datasets, annotation quality, and continuous validation improve large language model performance.
Benchmarking Multilingual AI Assistants
A comparative evaluation of multilingual AI assistants across accuracy, reasoning, and cultural understanding.
Reducing Hallucinations in Enterprise LLMs
Research on retrieval augmentation, grounding techniques, and evaluation strategies to minimize hallucinations in enterprise AI systems.
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