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2026 顶会论文

作者位次 #4/5ICML 2026Poster· Applications / Neuroscience, Cognitive Science

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

Wei Xiong · Jiangtong Li · Jie Li · Kun Zhu · Changjun Jiang

评测与基准OpenReview

作者位次 #3/4ACL 2026Findings· Findings of ACL

Can LLM s Really Judge? A Progressive Argumentation-Mining Framework for Distinguishing Understanding from Aggregation

Fuyu Wang · Jiangtong Li · Kun Zhu · Changjun Jiang

摘要

Current evaluations of large language models (LLMs) mainly rely on dataset-based generation accuracy. However, generative correctness does not guarantee the discriminative capability required to verify solutions, frequently masking an inability to distinguish valid reasoning from plausible errors...

智能体与工具评测与基准推理OpenReview 项目页

作者位次 #3/3KDD 2026Oral· Knowledge Discovery & Data Mining

Combating Web-Based E-Commerce Fraud Syndicates: Fairness-Aware Hypergraph Contrastive Fraud Detection with Multi-Dimensional Reinforcement Rewards

Nana Zhang · Xunxin Liu · Kun Zhu

强化学习与后训练检索与知识社会与人本OpenReview

作者位次 #2/7AAAI 2026Poster· Artificial Intelligence

Targeting Borderline Fraudsters: Multi-View Hypergraph Fraud Detection with LLM-Guided Contrastive Learning

Rui Ou · Kun Zhu · Nana Zhang · Jiangtong Li · Chaochao Chen · Yuhua Xu · Changjun Jiang

OpenReview

Kun Zhu

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2026 顶会论文

EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models

Can LLM s Really Judge? A Progressive Argumentation-Mining Framework for Distinguishing Understanding from Aggregation

Combating Web-Based E-Commerce Fraud Syndicates: Fairness-Aware Hypergraph Contrastive Fraud Detection with Multi-Dimensional Reinforcement Rewards

Targeting Borderline Fraudsters: Multi-View Hypergraph Fraud Detection with LLM-Guided Contrastive Learning

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