Jingyu Zhang

Accurate but Not Humble: Evaluating Epistemic Humility in LLM Agents under Knowledge Conflict

Kaiser Sun, Bernal Jimenez Gutierrez, Hongjun Liu, et al.

Minimally Invasive Steering of Language Models

Taha Entesari, Jingyu Zhang, Daniel Khashabi, et al.

Self-Compacting Language Model Agents

Tianjian Li, Jingyu Zhang, William Jurayj, et al.

Many-Tier Instruction Hierarchy in LLM Agents

Jingyu Zhang, Tianjian Li, William Jurayj, et al.

Reasoning over mathematical objects: on-policy reward modeling and test time aggregation

Pranjal Aggarwal, Marjan Ghazvininejad, Seungone Kim, et al.

This paper tackles how AI language models can better solve complex math and science problems that require deriving formally structured mathematical expressions (like equations or proofs) rather than just picking a number or multiple choice answer. The researchers created a new training dataset called Principia, developed improved training methods using AI judges that learn during training, and showed how to use extra computation at test time to aggregate multiple solution attempts for better results.