Papers by Yuzhang Shang
AdaTooler-V: Adaptive Tool-Use for Images and Videos (2026.findings-acl)
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Chaoyang Wang, Kaituo Feng, Dongyang Chen, Zhongyu Wang, Zhixun Li, Sicheng Gao, Meng Meng, Xu Zhou, Manyuan Zhang, Yuzhang Shang, Xiangyu Yue
| Challenge: | Existing models exhibit blind tool-use reasoning patterns, which significantly increases inference overhead and degrades model performance. |
| Approach: | They propose an MLLM that performs adaptive tool-use by determining whether a visual problem truly requires tools. |
| Outcome: | The proposed model outperforms existing methods in visual reasoning tasks. |
SilentDrift: Exploiting Action Chunking for Stealthy Backdoor Attacks on Vision-Language-Action Models (2026.findings-acl)
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| Challenge: | Existing backdoor attacks introduce kinematic discontinuities and distributional anomalies that can be flagged by standard trajectory detection. |
| Approach: | They propose a backdoor attack exploiting an intra-chunk visual open-loop vulnerability . they propose 93.2% Attack Success Rate and a poisoning rate under 2% . |
| Outcome: | The proposed attack achieves a 93.2% Attack Success Rate with a poisoning rate under 2% while maintaining a 95.3% Clean Task Success Rate. |
PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for sub 2-bit quantization introduce an extra 1-bit or more per weight. |
| Approach: | They propose a sub 2-bit post-training quantization method that enables weight quantization to 1.61-bit for the first time. |
| Outcome: | The proposed method reduces the upper bound of quantization error to 1.61-bit for the first time. |
GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuning (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) fine-tuning techniques require large Floating Point(FP) computation and are impractical for resource-constrained edge devices. |
| Approach: | They propose a framework for on-device LLM fine-tuning that eliminates the need for floating-point operations in both inference and training. |
| Outcome: | The proposed framework reduces memory and compute costs while reducing memory usage. |