Rerank

LLM-powered multi-attribute reranking over ExoPriors entity sets. Uses pairwise comparison (not pointwise scoring) to produce calibrated rankings with uncertainty estimates.

Skill generation: 2026032401

Mental model

Traditional search returns documents ordered by a single signal (recency, BM25, embedding distance). Rerank adds a second stage: an LLM reads pairs of documents and judges which is better on each attribute you care about. A robust solver (iteratively reweighted least squares) converts those pairwise judgements into a global ranking.

Why pairwise instead of pointwise? Comparative judgement is more reliable than absolute scoring. Humans and LLMs are better at "A vs B" than "rate A on 1-10." The resulting rankings are more stable and composable.

Key properties:

• Multi-attribute: rank by clarity AND insight AND depth simultaneously, with weights.
• Memoized: canonical attributes share cached comparisons across users and queries, reducing cost on repeated candidate sets.
• Algebraically composable: comparisons are stored as log-ratios in public_binary_ratio_comparisons, composable with the full ExoPriors rating engine.
• Adaptive: the TopK algorithm focuses comparisons on items near the decision boundary, not wasting budget on obvious winners or losers.

Cost scales with comparisons x chosen_model. A typical 100-entity, 2-attribute rerank with openai/gpt-5.2-chat costs roughly $0.05-0.15.