Contexting as Recommendation: Evolutionary Collaborative Filtering for Context Engineering

ArXi:2605.15721v1 Announce Type: new Large Language Models (LLMs) are highly sensitive to their input contexts, motivating the development of automated context engineering. However, existing methods predominantly treat this as a global search problem, seeking a single context strategy that maximizes average performance across a dataset. This restrictive assumption overlooks the fact that different inputs often require distinct guidance, leaving substantial instance-level performance gains untapped.