MoL: Adaptive Mixture-of-Length Reasoning for Efficient Question Answering with Context
Guocong Li · Jinjian Zhang · Ping Wang · Dongnan Liu · Tian Liang · Qiuyi Qi · Hao Huang · Siyan Guo · Mutian Bao · Wei Zhou · Linjian Mo · Hongxia Xu · JIAN Wu
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Guocong Li · Jinjian Zhang · Ping Wang · Dongnan Liu · Tian Liang · Qiuyi Qi · Hao Huang · Siyan Guo · Mutian Bao · Wei Zhou · Linjian Mo · Hongxia Xu · JIAN Wu
Liqi He · Zuchao Li · Hao Huang · Ping Wang
Early Long-context Document Visual Question Answering (DocVQA) methods struggle with preserving visual semantics or handling finite context windows. Conversely, recent RAG-based approaches suffer from “semantic gaps” and “structural disconnections” due to passive retrieval mechanisms that ignore...
Haoqi Yang · Luohe Shi · Qiwei Li · Zuchao Li · Ping Wang · Hao Huang · Hai Zhao
In fine-grained sparse Mixture-of-Experts (MoE) models, a large pool of specialized experts replaces a small homogeneous set, shifting performance and throughput to be governed by inference-time expert activation. Yet most existing optimization recipes implicitly assume a fixed activation budget...