Blog
Builder Center2026We benchmark Task Arithmetic, TIES, DARE, and Model Soups for merging Qwen3.5 experts across five domains. The best expert training duration turns out to depend on the merging method, with sparsification-based methods peaking well past the validation optimum.
Builder Center2026Self-reflection lets a model critique and revise its own output before returning it. We apply it at inference time on Amazon Bedrock and measure the accuracy gain against the added cost and latency across reflection depths.
AWS Blog2026We built MACROS, a system on Amazon Bedrock with Lambda and Step Functions that reviews medical articles against guidelines and proposes revisions. The proof of concept reached 80% accuracy and over 90% recall, cutting per-guideline review from hours to minutes.
AWS Blog2025RAG systems still answer confidently when the retrieved context does not support the answer. We add a detection layer that flags those cases, comparing methods such as LLM-as-judge and semantic similarity on accuracy against cost.
Self-published2021This post adapts test-time augmentation, a computer vision staple, to tabular data. We benchmark strategies for numeric and categorical features and measure how much accuracy they actually buy at inference.
Self-published2020We forecast retail demand with gradient boosted trees in our Data Mining Cup 2020 solution. Optimizing a profit-based objective rather than plain forecast error aligns the model with the underlying inventory cost and revenue trade-offs.
Self-published2020This post introduces the fairness R package for computing and visualizing algorithmic fairness metrics. We walk through demographic parity, proportional parity, and equalized odds, and show how the three diverge on the very same classifier.