When combining evidence from multiple sources, separate the task of interpreting each source from aggregating those interpretations—use structured tuples and calibrated scoring rather than simple concatenation and vote counting.
This paper separates evidence interpretation from decision aggregation in multi-source reasoning systems. Instead of concatenating sources into one prompt, the authors propose a structured evidence tuple (hypothesis, reliability, rationale, provenance) and show how to properly combine interpretations using calibrated log-likelihood ratios.