Papers by Zhongbin Guo
Temporal Token Matters: Investigating and Interpreting the Consistency of Temporal Ordering in Large Language Models (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) exhibit notable deficiencies in temporal reasoning . phrasing changes can lead LLMs to produce inconsistent outputs . |
| Approach: | They investigate the mechanistic interpretability of temporal ordering within event temporal reasoning . they identify a sparse subset of attention heads that are causally responsible for reasoning outcomes . |
| Outcome: | The proposed model outperforms other models in a variety of tasks and is validated by intervention-based experiments. |
How Do LLMs and VLMs Understand Viewpoint Rotation Without Vision? An Interpretability Study (2026.acl-long)
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Zhen Yang, Ping Jian, Zhongbin Guo, Zuming Zhang, Chengzhi Li, Yonghong Deng, Xinyue Zhang, Wenpeng Lu
| Challenge: | Existing studies on spatial intelligence from the perspective of visual-spatial intelligence have not explored whether visual intelligence alone is sufficient to endow models with spatial intelligence. |
| Approach: | They propose to use a linguistic perspective to investigate spatial intelligence from a theoretical perspective. |
| Outcome: | The proposed model performs poorly on the proposed dataset while human can easily achieve 100% accuracy. |
Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions (2026.findings-acl)
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Zhongbin Guo, Zhen Yang, Yushan Li, Xinyue Zhang, Wenyu Gao, Jiacheng Wang, Chengzhi Li, Xiangrui Liu, Ping Jian
| Challenge: | Existing advances in Spatial Intelligence rely on vision-Language Models . however, a critical question remains: does spatial understanding originate from visual encoders? |
| Approach: | They propose to evaluate the SI performance of Large Language Models without pixel-level input. |
| Outcome: | The proposed benchmark challenges large language models to perform symbolic reasoning rather than visual pattern matching. |