A development team is adapting a powerful pre-trained language model, originally designed for a 4,096-token context window, to handle sequences up to 16,384 tokens. Their method involves directly scaling the existing positional encodings to fit the new, longer context length before a brief fine-tuning phase. After adaptation, they observe a peculiar issue: the model excels at 'needle-in-a-haystack' tasks when the key piece of information is located near the end of a long document, but its performance drops significantly when the key information is located near the beginning. Analyze the likely technical cause of this specific performance discrepancy.