Can Calibration of Positional Encodings Enhance Long Context Utilization?

Large language models suffer from positional biases like the "Lost in the Middle" (LiM) phenomenon and recency bias, which reduce the effective utilization of long contexts. In this work, we investigate the role of Positional Encodings in this context. Our empirical study confirms the persistence of these biases in modern large language models. Drawing on these findings, we introduce Caliope, a training-free framework for calibrating Positional Encodings at inference time. Our calibrators yield substantial improvements on needle-in-a-haystack and cross-chunk reasoning benchmarks, and offer a practical, lightweight method for improving long-context utilization.