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Jiangshan Guan

ACL 2026×2KDD 2026多模态评测与基准对齐与偏好推理可解释性
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0 一作 · 3 篇 · ACL 2026 / KDD 2026

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2026 顶会论文

3
作者位次 #3/5ACL 2026Findings· Findings of ACL

From Coarse to Fine: A Multi-Granularity Multimodal Framework for Teacher Sentiment Analysis

Zhiyi Duan · Xiangren Wang · Jiangshan Guan · Bing Jia · Qianli Xing

摘要

Teacher sentiment analysis is pivotal for understanding instructional dynamics, yet it remains challenging because classroom expressions are professionally regulated performances rather than spontaneous outbursts. However, existing approaches typically treat sentiment as a static, monolithic labe...

对齐与偏好多模态OpenReview 项目页
作者位次 #3/5ACL 2026Findings· Findings of ACL

B loom E val: A Bloom’s Cognitive Taxonomy-Based Benchmark for Evaluating LRM s via Cognitive Hierarchy Trace

Zhiyi Duan · Lei Gao · Jiangshan Guan · Qi Wang · Rui Liu

摘要

Current benchmarks for Large Reasoning Models (LRMs) primarily rely on answer correctness, failing to assess the structural coherence and cognitive soundness of the reasoning process itself. To address this gap, we introduce Cognitive Hierarchy Trace (CHT), a novel evaluation framework grounded i...

推理评测与基准可解释性OpenReview 项目页
作者位次 #2/3KDD 2026Oral· Knowledge Discovery & Data Mining

Edu-Eval: A Large-Scale Multimodal Benchmark for MLLMs in Authentic Educational Scenarios

Zhiyi Duan · Jiangshan Guan · Qianli Xing

多模态评测与基准检索与知识OpenReview

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