Develop query-conditioned context pruning for SmartSearch

Develop and evaluate aggressive query-conditioned context pruning methods that retain only words relevant to the specific input question within retrieved passages in SmartSearch, and assess their impact on token reduction and answer accuracy.

Background

The paper identifies a compilation bottleneck where ranking and truncation, rather than retrieval, limit performance. It suggests context compression as a promising direction to reduce token usage without sacrificing evidence quality, noting preliminary gains from format-level changes.

However, beyond basic format-level compression, the authors highlight that more aggressive, query-conditioned pruning—selectively retaining only words pertinent to the question—has not yet been explored, leaving a concrete opportunity for future research.