Papers by Zining Zhang
PipeSpec: Breaking Stage Dependencies in Hierarchical LLM Decoding (2025.findings-acl)
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| Challenge: | Speculative decoding is limited by sequential stage dependencies that prevent full hardware utilization. |
| Approach: | They propose a framework that generalizes speculative decoding to use multiple models arranged in a hierarchical pipeline and enables asynchronous execution with lightweight coordination for prediction verification and rollback. |
| Outcome: | The proposed framework achieves 2.25 tokens/unit through pipelined parallelism with multiple models arranged in a hierarchical pipeline. |
Mitigating Sequential Dependencies: A Survey of Algorithms and Systems for Generation-Refinement Frameworks in Autoregressive Models (2025.findings-emnlp)
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| Challenge: | Sequential dependencies present a fundamental bottleneck in deploying large-scale autoregressive models . |
| Approach: | They analyze methods based on generation strategies and refinement mechanisms . they examine deployment strategies across computing environments and explore applications spanning text, images, and speech generation. |
| Outcome: | The proposed frameworks can be used to improve the quality of autoregressive models. |
XtraGPT: Context-Aware and Controllable Academic Paper Revision via Human-AI Collaboration (2026.acl-long)
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Nuo Chen, Andre Lin HuiKai, Jiaying Wu, Junyi Hou, Zining Zhang, Qian Wang, Xidong Wang, Bingsheng He
| Challenge: | Existing systems are designed for general-purpose scientific text generation and fail to support high-quality scientific writing beyond surface-level polishing. |
| Approach: | They propose a human-AI collaboration framework for academic paper revision based on criteria-guided intent alignment and context-aware modeling. |
| Outcome: | The proposed framework outperforms existing LLMs and rivals the quality of proprietary ones. |
DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation (2026.acl-long)
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| Challenge: | Speculative decoding (SD) has proven to be effective for autoregressive generation in large language models (LLMs), however its application to vision-language models (VLMs) remains relatively unexplored. |
| Approach: | They propose a Speculative Decoding framework for vision-language models that integrates a neural architecture search framework and target-aware supernet training to identify optimal interaction strategies. |
| Outcome: | DREAM-S achieves 3.85 speedup compared to baselines on well-established vision-language models. |