Mixing Matters: Evidence Position Bias Across Sequence Mixers in Long-Context Question Answering.

arXiv:2610.31135v1 Announce Type: cross Abstract: Long-context models often use answer-bearing evidence less effectively when it appears away from a prompt boundary. We compare this position bias across Transformer, state-space, and hybrid Mamba architectures