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cs.IR2026
Tevatron Meets Megatron: Expert-Parallel LLM Reranker Training on an Academic Budget
Zhichao Xu, Xueguang Ma, Shengyao Zhuang +5
Modern reranking recipes---billion-scale cross-encoders, mixture-of-experts (MoE) backbones, and distillation against strong teachers---have outpaced the training infrastructure av…
cs.IR2026
Improving Long-Context Retrieval with Multi-Prefix Embedding
Zhenglin Yu, Xueguang Ma, Shengyao Zhuang +4
Long-context retrieval exposes a tension: single-vector embeddings lose fine-grained detail, while token-level multi-vector methods incur prohibitive storage. We propose Multi-Pref…
cs.IR2026
LACONIC: Dense-Level Effectiveness for Scalable Sparse Retrieval via a Two-Phase Training Curriculum
Zhichao Xu, Shengyao Zhuang, Crystina Zhang +5
While dense retrieval models have been the standard for state-of-the-art information retrieval, their deployment is often constrained by high memory requirements and reliance on GP…