9 papers
Reason Popper-ly: Patching In-Context Reasoning with Inductive Logic Programming
Zirong Chen, Meiyi Ma
Chain-of-thought (CoT) prompting enables large language models (LLMs) to tackle multi-step reasoning tasks, yet the generated intermediate steps are not guaranteed to be logically…
PACE: A Personalized Adaptive Curriculum Engine for 9-1-1 Call-taker Training
Zirong Chen, Hongchao Zhang, Meiyi Ma
9-1-1 call-taking training requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances. A nationwide labor shortage is…
Empowering 9-1-1 Calltaking Training with Generative AI: Experiences and Lessons Learned
Zirong Chen, Meiyi Ma
Emergency call-takers form the first operational link in public safety response, handling over 240 million calls annually while facing a sustained training crisis: staffing shortag…
Step-wise Rubric Rewards for LLM Reasoning
Weichu Xie, Haozhe Zhao, Wenpu Liu +15
Reinforcement Learning with Verifiable Rewards (RLVR) is widely used to improve reasoning in large language models, but rewards only final-answer correctness with no supervision ov…
Towards Generalist Game Players: An Investigation of Foundation Models in the Game Multiverse
Kuan Zhang, Dongchen Liu, Qiyue Zhao +12
The real world unfolds along a single set of physics laws, yet human intelligence demonstrates a remarkable capacity to generalize experiences from this singular physical existence…
Learning with Preserving for Continual Multitask Learning
Hanchen David Wang, Siwoo Bae, Zirong Chen +1
Artificial intelligence systems in critical fields like autonomous driving and medical imaging analysis often continually learn new tasks using a shared stream of input data. For i…