This page is out of dated, you can find my articles on my Google Scholar profile.
Published in TMLR, 2025
Converting textual reasoning data into images to probe vision-language model reasoning capabilities
Recommended citation: Lyu, Z., Ma, X., & Chen, W. (2025). PixelWorld: Towards Perceiving Everything as Pixels. Transactions on Machine Learning Research. https://arxiv.org/abs/placeholder
Published in Technical Report, 2025
Technical report on MiniMax-M1 model with focus on software engineering capabilities and test-time compute scaling
Recommended citation: MiniMax. (2025). MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention. Technical Report. https://arxiv.org/abs/placeholder
Published in NAACL 2025 (Oral), 2025
A novel approach to tracking dynamic world states and detecting contradictions in story narratives
Recommended citation: Lyu, Z., Yang, K., Kong, L., & Klein, D. (2025). FACTTRACK: Time-Aware World State Tracking in Story Outlines. NAACL 2025. https://arxiv.org/abs/placeholder
Published in Under Review, 2025
A framework for training web agents that directly interact with browser environments for information-seeking tasks
Recommended citation: Yu, T., Zhang, Z., Lyu, Z., Gong, J., Yi, H., Wang, X., Zhou, Y., Yang, J., Nie, P., Huang, Y., & Chen, W. (2025). BrowserAgent: Building Web Agents with Human-Inspired Web Browsing Actions. Under Review. https://arxiv.org/abs/placeholder
Published in , 2023
This research introduces the first benchmark dataset, Corr2Cause, to test large language models (LLMs) pure causal inference skills.
Recommended citation: Jin Z, Liu J, Lyu Z, et al. Can Large Language Models Infer Causation from Correlation? arXiv preprint arXiv:2306.05836, 2023. https://arxiv.org/abs/2306.05836
Published in , 2023
Our paper conducts a post-hoc analysis to check whether large language models can be used to distinguish cause from effect.
Recommended citation: Jin, Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., & Schölkopf, B. (2022). Logical Fallacy Detection. https://openreview.net/forum?id=ucHh-ytUkOH
Published in , 2023
This paper is about a prompting method embedded causal direction and analyze the performance gap of LLMs
Recommended citation: Lyu, Z., Jin, Z., Mattern, J., Mihalcea, R., Sachan, M., & Schoelkopf, B. (2023). Psychologically-Inspired Causal Prompts. arXiv preprint arXiv:2305.01764. https://arxiv.org/pdf/2305.01764
Published in , 2022
This paper is about the a dataset of Logical Fallacy Detection and its baseline model
Recommended citation: Jin, Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., & Schölkopf, B. (2022). Logical Fallacy Detection. https://arxiv.org/abs/2202.13758