Papers by Weidong Zhou
GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs (2026.acl-long)
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Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You, Wangbo Zhao
| Challenge: | Existing models for general intelligence fail to model how mental states interact and crystallize into group-level outcomes. |
| Approach: | They propose a multimodal benchmark for group-level Theory of Mind (ToM) to probe nonlinear collective behavior. |
| Outcome: | The proposed model performs significantly below human levels, exposing blind spots in modeling social structures and nonlinear collective behavior. |
SafeAgent: Safeguarding LLM Agents via an Automated Risk Simulator (2026.acl-long)
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Xueyang Zhou, Weidong Wang, Lin Lu, Jiawen Shi, Guiyao Tie, Xu Yongtian, Lixing Chen, Pan Zhou, Neil Zhenqiang Gong, Lichao Sun
| Challenge: | SafeAgent improves agent safety through fully automated synthetic data generation. |
| Approach: | They propose a framework that improves agent safety through fully automated synthetic data generation. |
| Outcome: | The proposed framework outperforms closed-source models on two safety benchmarks and one real-world task. |
KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension (2026.findings-acl)
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| Challenge: | Existing evaluation metrics suggest that Multimodal large language models have acquired fine-grained visual grounding capabilities. |
| Approach: | They propose a benchmark to assess Referring Expression Comprehension (REC) that uses intra-image visual cues to localize target objects and a controllable evaluation mechanism to test sensitivity to fine-grained factual changes. |
| Outcome: | The proposed benchmarks show that multimodal large language models have a high level of performance on the RefCOCO family of benchmarks. |
Entity-Aware Abstractive Multi-Document Summarization (2021.findings-acl)
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| Challenge: | Existing models for multidocument summarization do not focus on explicitly modeling the underlying semantic information across documents. |
| Approach: | They propose an entityaware model for abstractive multi-document summarization that augments the classical Transformer-based encoder-decoder framework with a heterogeneous graph consisting of text units and entities as nodes. |
| Outcome: | The proposed model can deal with saliency and redundancy issues explicitly and can be used with pre-trained language models, arriving at improved performance. |
LearnerCoMPASS: Intelligent Tutoring System with Dynamic Cognitive Diagnosis and Multi-Model Path Planning (2026.acl-long)
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| Challenge: | Existing adaptive learning systems struggle to achieve deep personalization, dynamic adaptability, and content trustworthiness. |
| Approach: | They propose a framework that integrates large language models into adaptive learning systems . they propose 'cognitive multi-model planning adapted system' to enable deep personalization . |
| Outcome: | The proposed framework outperforms state-of-the-art learning paths and improves trustworthiness. |
TiKMiX: Efficient Semi-Dynamic Data Mixture via Data Influence for LLM Pre-training (2026.acl-long)
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Yifan Wang, null Binbinliu, Fengze Liu, Yuanfan Guo, Jiyao Deng, Xuecheng Wu, Weidong Zhou, Xiaohuan Zhou, Taifeng Wang
| Challenge: | Static data mixing strategies in large language models are often suboptimal as they fail to adapt to the model’s evolving learning states. |
| Approach: | They propose a semi-dynamic data mixing framework that uses a key observation of influence ranking invariance to reduce computational overhead by 80% . |
| Outcome: | The proposed method reduces computational overhead by 80% and achieves an average performance gain of 2% across nine downstream benchmarks, effectively mitigating data under-digestion. |
Exploring the Choice Behavior of Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly being adopted across various domains where they help to make choices. |
| Approach: | They construct a virtual QA platform that includes three different experimental conditions, with four models from GPT and Llama series participating in repeated experiments. |
| Outcome: | The proposed model includes three experimental conditions and four models from GPT and Llama series. |