6 papers
CARE-RFT: Confidence-Anchored Reinforcement Finetuning for Reliable Reasoning in Large Language Models
Shuozhe Li, Jincheng Cao, Bodun Hu +3
Reinforcement finetuning (RFT) has emerged as a powerful paradigm for unlocking reasoning capabilities in large language models. However, we identify a critical trade-off: while un…
Towards Video Thinking Test: A Holistic Benchmark for Advanced Video Reasoning and Understanding
Yuanhan Zhang, Yunice Chew, Yuhao Dong +3
Human intelligence requires correctness and robustness, with the former being foundational for the latter. In video understanding, correctness ensures the accurate interpretation o…
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval Augmented Generation Across Learning Style
Debdeep Sanyal, Agniva Maiti, Umakanta Maharana +4
Effective teaching requires adapting instructional strategies to accommodate the diverse cognitive and behavioral profiles of students, a persistent challenge in education and teac…
MatTools: Benchmarking Large Language Models for Materials Science Tools
Siyu Liu, Bo Hu, Beilin Ye +3
Large language models (LLMs) are increasingly applied to materials science questions, including literature comprehension, property prediction, materials discovery and alloy design.…
Extract, Match, and Score: An Evaluation Paradigm for Long Question-context-answer Triplets in Financial Analysis
Bo Hu, Han Yuan, Vlad Pandelea +3
The rapid advancement of large language models (LLMs) has sparked widespread adoption across diverse applications, making robust evaluation frameworks crucial for assessing their p…
A Multi-agent Framework for Physical Laws Discovery
Bo Hu, Siyu Liu, Beilin Ye +6
Discovering explicit physical laws has traditionally depended on human intuition and domain expertise. Recent advances in artificial intelligence, particularly large language model…