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Hao Huang

ACL 2026×2ICLR 2026推理效率与系统可解释性多模态检索与知识
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0 一作 · 3 篇 · ACL 2026 / ICLR 2026

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2026 顶会论文

3
作者位次 #3/4ACL 2026Poster· Natural Language Processing

TRACE : Traversal Retrieval-Augmented Chain of Evidence for Document Understanding

Liqi He · Zuchao Li · Hao Huang · Ping Wang

摘要

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...

检索与知识推理可解释性OpenReview 项目页
作者位次 #6/7ACL 2026Findings· Findings of ACL

Faster M o E LLM Inference for Extremely Large Models

Haoqi Yang · Luohe Shi · Qiwei Li · Zuchao Li · Ping Wang · Hao Huang · Hai Zhao

摘要

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...

效率与系统可解释性评测与基准OpenReview 项目页

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