Published in , 2022
A dataset for logical fallacy detection (LOGIC and LOGICCLIMATE) with a structure-aware 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
Published in , 2023
Verbalizing three causal mechanisms of sentiment classification into prompts to study how causal structure affects LLM behavior
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 , 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 ICLR 2024, 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 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/2407.16347
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/2501.19339
Published in TMLR, 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. Transactions on Machine Learning Research. https://arxiv.org/abs/2502.01882
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/2506.13585
Published in TMLR 2026 · Best Paper Award @ SPOT Workshop, ICLR 2026, 2025
A unified, easy-to-extend tool-agent RL training framework based on verl, supporting agentic RL across code, search, SQL, and SWE environments.
Recommended citation: Dongfu Jiang*, Yi Lu*, Zhuofeng Li*, Zhiheng Lyu*, Ping Nie, Haozhe Wang, Alex Su, Hui Chen, Kai Zou, Chao Du, Tianyu Pang, Wenhu Chen (2025). VerlTool: Towards Holistic Agentic Reinforcement Learning with Tool Use. TMLR 2026. https://arxiv.org/abs/2509.01055
Published in ACL 2026 Findings, 2026
A contamination-resistant benchmark for agentic repository-level code understanding, plus a training recipe that lets an 8B model surpass GPT-4o.
Recommended citation: Songcheng Cai*, Zhiheng Lyu*, Yuansheng Ni, Xiangchao Chen, Baichuan Zhou, Shenzhe Zhu, Yi Lu, Haozhe Wang, Chi Ruan, Benjamin Schneider, Weixu Zhang, Xiang Li, Andy Zheng, Yuyu Zhang, Ping Nie, Wenhu Chen (2026). SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding. ACL 2026 Findings. https://arxiv.org/abs/2603.16124
Published in Under Review, 2026
An execution-grounded framework for scalable SWE task and trajectory collection: 2,308 verifiable tasks mined from 311 real repositories.
Recommended citation: Jiarong Liang*, Zhiheng Lyu*, Zijie Liu, Xiangchao Chen, Ping Nie, Kai Zou, Wenhu Chen (2026). SWE-Next: Scalable Real-World Software Engineering Tasks for Agents. Under Review. https://arxiv.org/abs/2603.20691
Published in Technical Report, 2026
Technical report on the MiniMax-M2 MoE series (229.9B total / 9.8B active), built end-to-end for agentic deployment. 69% Pass@1 on SWE-bench Verified, #2 on MultiSWE and TerminalBench.
Recommended citation: MiniMax. (2026). The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence. Technical Report. https://arxiv.org/abs/2605.26494
Published in Technical Report, 2026
An open benchmark of 260 contamination-resistant, real-world tasks spanning Code, Web, Office, and Security.
Recommended citation: Tencent WorkBuddy Bench Team, Siqi Cai, Shaopeng Chen, Xiang Fei, Yong Mao, Zihan Xu, Zhiheng Lyu, et al. (2026). Tencent WorkBuddy Bench: A Multi-Domain Coding-Agent Benchmark with Contamination-Resistant Task Construction. Technical Report. https://arxiv.org/abs/2607.20911