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Position bias in LLM-based rerankers can lead to significant inconsistencies in preference rankings, undermining their reliability in recommendation systems.

A2TTA transforms the challenge of evolving traffic networks into a robust adaptation strategy, significantly enhancing forecasting accuracy in dynamic environments.

LLMs' ranking instability, where shuffling candidates changes recommendations, can be solved with a novel architecture that enforces permutation invariance.