Split the Labor: Separating Evidence Interpretation from Decision Aggregation

When LLMs are asked to synthesize conclusions from many sources, cramming everything into one prompt conflates two different jobs: understanding each piece of evidence and combining conclusions fairly. This paper identifies a subtle failure mode, "count-scale drift," where naive vote-counting shifts as more sources are added, and proposes separating interpretation from aggregation using a structured evidence format and calibrated probability pooling. Validated on a real medical/survival dataset, this approach could improve any multi-source reasoning system, from diagnostic AI panels to fraud detection to automated research synthesis tools. Authors: Zhelun Wu Paper: https://arxiv.org/abs/2608.14509v1