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Qiwei Li

ACL 2026×3推理评测与基准可解释性多模态强化学习与后训练
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0 一作 · 3 篇 · ACL 2026

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2026 顶会论文

3
作者位次 #4/8ACL 2026Poster· Natural Language Processing

B o Y a E val: Evaluating Multimodal Large Language Models on Understanding A ncient C hinese Musical Scores

Jiajia Li · Weizhi Xue · Yao Yao · Qiwei Li · Chenchong · Zuchao Li · Ping Wang · Hai Zhao

摘要

Multimodal Large Language Models (MLLMs) excel in general tasks but struggle with specialized, structured cultural symbols. We introduce BoYaEval, the first comprehensive benchmark dedicated to deciphering diverse Ancient Chinese musical notations, including five types of ancient Chinese music no...

推理多模态评测与基准OpenReview 项目页
作者位次 #2/6ACL 2026Findings· Findings of ACL

G o T - R 1: Internalizing Graph-of-Thought via Structural Reinforcement for High-Density Reasoning

Zuchao Li · Qiwei Li · Yao Yao · Hai Zhao · Lefei Zhang · Bo Du

摘要

Chain-of-Thought (CoT) reasoning, while effective, suffers from an inherent mechanism flaw: linearity induces overthinking. Constrained by sequential generation, models often produce redundant narration and circular self-corrections to maintain logical context. We propose GoT-R1, a framework that...

推理强化学习与后训练可解释性OpenReview 项目页
作者位次 #3/7ACL 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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