Archived
2026-10-08RECAST: Learning to Compute the Right Context through Adaptive Evidence Routing
Yilun Hao, Krishna Sayana, Isabella Ye, James S Ren, Sukhdeep Sodhi, Craig Boutilier, et al.
arXiv:2610.10507v1Large language models are great at finding text, but many real tasks need more than retrieval. You may have to compare sources, filter noisy evidence, aggregate numbers, or even run a small computation before you can answer correctly. This paper tackles that gap with RECAST, a system that learns when to retrieve, when to compute, and when to combine both. Instead of using a fixed similarity search pipeline, RECAST treats evidence gathering as a sequence of decisions. A router model chooses primitive actions or asks a compiler model to turn a custom operation into executable code, then passes the built evidence to an answer model. The key idea is that evidence is not always sitting neatly in one document; sometimes it has to be constructed. That matters because it moves LLM systems closer to real analytical work, from scientific questions to messy enterprise data. On six benchmark families, RECAST substantially outperforms strong baselines and also generalizes well to held-out tasks.
Also spotted that day
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