Review Table is a fascinating fan-out problem: M documents and N columns. Naively, each table can require O(MN) model calls. We wanted to fix that.
For our first foray into specialized model training, we worked with Applied Compute to build a model that beat frontier models on answer and citation quality while cutting cost per cell by 55%. Agentic retrieval also used 50% fewer input tokens at the same quality.
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