Shang Zhu

According to our database, Shang Zhu authored at least 19 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Automated Benchmark Auditing for AI Agents and Large Language Models.

Junlin Wang, Federico Bianchi, Shang Zhu, Fan Nie, Yongchan Kwon, Bhuwan Dhingra, James Zou

CoRR, May, 2026

DSGym: A Holistic Framework for Evaluating and Training Data Science Agents.

Fan Nie, Junlin Wang, Harper Hua, Federico Bianchi, Yongchan Kwon, Zhenting Qi, Owen Queen, Shang Zhu, James Zou

CoRR, January, 2026

2025

Intelligence per Watt: Measuring Intelligence Efficiency of Local AI.

Jon Saad-Falcon, Avanika Narayan, Hakki Orhun Akengin, J. Wes Griffin, Herumb Shandilya, Adrian Gamarra Lafuente, Medhya Goel, Rebecca Joseph, Shlok Natarajan, Etash Kumar Guha, Shang Zhu, Ben Athiwaratkun, John Hennessy, Azalia Mirhoseini, Christopher Ré

CoRR, November, 2025

ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning.

Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou, James Zou

CoRR, October, 2025

DREAMS: Density Functional Theory Based Research Engine for Agentic Materials Simulation.

Ziqi Wang, Hongshuo Huang, Hancheng Zhao, Changwen Xu, Shang Zhu, Jan Janssen, Venkatasubramanian Viswanathan

CoRR, July, 2025

Shrinking the Generation-Verification Gap with Weak Verifiers.

Jon Saad-Falcon, Estefany Kelly Buchanan, Mayee F. Chen, Tzu-Heng Huang, Brendan McLaughlin, Tanvir Bhathal, Shang Zhu, Ben Athiwaratkun, Frederic Sala, Scott W. Linderman, Azalia Mirhoseini, Christopher Ré

CoRR, June, 2025

CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning.

Changwen Xu, Shang Zhu, Venkatasubramanian Viswanathan

CoRR, June, 2025

When Does Divide and Conquer Work for Long Context LLM? A Noise Decomposition Framework.

Zhen Xu, Shang Zhu, Jue Wang, Junlin Wang, Ben Athiwaratkun, Chi Wang, James Zou, Ce Zhang

CoRR, June, 2025

Improving Model Alignment Through Collective Intelligence of Open-Source LLMS.

Junlin Wang, Roy Xie, Shang Zhu, Jue Wang, Ben Athiwaratkun, Bhuwan Dhingra, Shuaiwen Leon Song, Ce Zhang, James Zou

CoRR, May, 2025

34 Examples of LLM Applications in Materials Science and Chemistry: Towards Automation, Assistants, Agents, and Accelerated Scientific Discovery.

Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali, Mehrad Ansari, Joshua D. Bocarsly, L. Catherine Brinson, Yuan Chiang, Defne Circi, Min-Hsueh Chiu, Nathan Daelman, Matthew L. Evans, Abhijeet Sadashiv Gangan, Janine George, Hassan Harb, Ghazal Khalighinejad, Sartaaj Khan, Sascha Klawohn, Magdalena Lederbauer, Soroush Mahjoubi, Bernadette Mohr, Seyed Mohamad Moosavi, Aakash Naik, Aleyna Beste Ozhan, Dieter Plessers, Aritra Roy, Fabian Schöppach, Philippe Schwaller, Carla Terboven, Katharina Ueltzen, Yue Wu, Shang Zhu, Jan Janssen, Calvin Li, Ian T. Foster, Ben Blaiszik

CoRR, May, 2025

Think Deep, Think Fast: Investigating Efficiency of Verifier-free Inference-time-scaling Methods.

Junlin Wang, Shang Zhu, Jon Saad-Falcon, Ben Athiwaratkun, Qingyang Wu, Jue Wang, Shuaiwen Leon Song, Ce Zhang, Bhuwan Dhingra, James Zou

CoRR, April, 2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.

Yoel Zimmermann, Adib Bazgir, Alexander Al-Feghali, Mehrad Ansari, Joshua D. Bocarsly, L. Catherine Brinson, Yuan Chiang, Defne Circi, Min-Hsueh Chiu, Nathan Daelman, Matthew L. Evans, Abhijeet Sadashiv Gangan, Janine George, Hassan Harb, Ghazal Khalighinejad, Sartaaj Khan, Sascha Klawohn, Magdalena Lederbauer, Soroush Mahjoubi, Bernadette Mohr, Seyed Mohamad Moosavi, Aakash Naik, Aleyna Beste Ozhan, Dieter Plessers, Aritra Roy, Fabian Schöppach, Philippe Schwaller, Carla Terboven, Katharina Ueltzen, Yue Wu, Shang Zhu, Jan Janssen, Calvin Li, Ian T. Foster, Ben Blaiszik

Mach. Learn. Sci. Technol., 2025

Weaver: Shrinking the Generation-Verification Gap by Scaling Compute for Verification.

Jon Saad-Falcon, Estefany Kelly Buchanan, Mayee F. Chen, Tzu-Heng Huang, Brendan McLaughlin, Tanvir Bhathal, Shang Zhu, Ben Athiwaratkun, Frederic Sala, Scott W. Linderman, Azalia Mirhoseini, Christopher Ré

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Improving Model Alignment Through Collective Intelligence of Open-Source Models.

Junlin Wang, Roy Xie, Shang Zhu, Jue Wang, Ben Athiwaratkun, Bhuwan Dhingra, Shuaiwen Leon Song, Ce Zhang, James Zou

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Scaling Instruction-tuned LLMs to Million-token Contexts via Hierarchical Synthetic Data Generation.

Linda He, Jue Wang, Maurice Weber, Shang Zhu, Ben Athiwaratkun, Ce Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Open-Source Fermionic Neural Networks with Ionic Charge Initialization.

Shai Pranesh, Shang Zhu, Venkat Viswanathan, Bharath Ramsundar

CoRR, 2024

Theme Work on Teamwork: Mapping Student Teaming Experiences in Design Projects.

Sara Beckman, Krina Patel, Qiuquan Gu, Shang Zhu, Alexander Cui, Jacob Yim

Proceedings of the IEEE Frontiers in Education Conference, 2024

2023

Differentiable Chemical Physics by Geometric Deep Learning for Gradient-based Property Optimization of Mixtures.

Shang Zhu, Bharath Ramsundar, Emil Annevelink, Hongyi Lin, Adarsh Dave, Pin-Wen Guan, Kevin Gering, Venkatasubramanian Viswanathan

CoRR, 2023

Open Source Infrastructure for Differentiable Density Functional Theory.

Advika Vidhyadhiraja, Arun Pa Thiagarajan, Shang Zhu, Venkat Viswanathan, Bharath Ramsundar

CoRR, 2023