Papers by Wenhui Zhang
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
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| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
LLM-as-Scheduler: Agentic Workflow Dynamic Scheduling (2026.acl-long)
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| Challenge: | Experiments show that LAS cuts token usage by 43% and reduces end-to-end latency by more than 36%, while causing at most a 1.4 percentage-point drop in accuracy compared with a strong fixed workflow. |
| Approach: | They propose a system that dynamically chooses the right workflow for each query. |
| Outcome: | Experiments show that LAS cuts token usage by 43% and reduces end-to-end latency by more than 36% while causing at most a 1.4 percentage-point drop in accuracy compared with a strong fixed workflow. |
Mis-prompt: Benchmarking Large Language Models for Proactive Error Handling (2025.acl-long)
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| Challenge: | Current error-handling works are performed in a passive manner, with explicit error- handling instructions. |
| Approach: | They propose a new benchmark to analyze LLMs' performance on a mis-prompt benchmark and a dataset to promote further research. |
| Outcome: | The proposed benchmark shows that current LLMs show poor performance on proactive error handling, and that SFT improves on error handling instances. |
RedundancyLens: Revealing and Exploiting Visual Token Processing Redundancy for Efficient Decoder-Only MLLMs (2025.findings-acl)
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| Challenge: | Current decoder-only architectures achieve higher performance but lower efficiency . cross-attention-based architectures skip visual token computations . |
| Approach: | They propose a training-free framework for analyzing trained MLLMs to investigate redundancy . they propose 'probe-activated Dynamic FFN and Hollow Attention' algorithms for visual token reductions and a layer ranking algorithm for inference acceleration. |
| Outcome: | The proposed framework achieves comparable performance to or better than state-of-the-art methods while remaining compatible with them. |