Paper-Conference

Round-trip Reinforcement Learning: Self-Consistent Training for Better Chemical LLMs

Large Language Models (LLMs) are emerging as versatile foundation models for computational chemistry, handling bidirectional tasks like …

L. Kong, X. Wang, Y. Chen, M. Zhang

Coevolutionary Continuous Discrete Diffusion: Make Your Diffusion Language Model a Latent Reasoner

C. Zhou, C. Yang, Y. Hu, C. Wang, C. Zhang, M. Zhang, L. Mackey, T. Jaakkola, S. Bates, D. Zhang

Expressive Graph Neural Networks via Equivariant Use of Noise

X. Wang, M. Zhang

JurisBench: A Deep Benchmark for Assessing Large Language Models in Professional Legal Practice

Z. Chen, G. Li, F. Ji, Y. Kang, J. Li, M. Zhang, Y. Zhang, T. Li, J. Wang, X. Guo, S.-C. Zhu, B. Ling

Knowledge is Not Enough: Injecting RL Skills for Continual Adaptation

P. Tang, Y. Wang, M. Zhang

Law in Silico: Simulating Legal Society with LLM-Based Agents

Y. Wang, Y. Chen, F. Meng, X. Chen, X. Yang, M. Zhang

LIFT: A Novel Framework for Enhancing Long-Context Understanding of LLMs via Long Input Fine-Tuning

Y. Mao, Y. Xu, J. Li, F. Meng, H. Yang, Z. Zheng, X. Wang, M. Zhang

Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Y. Wang, J. You, C. Shi, M. Zhang

SHINE: A Scalable In-Context Hypernetwork for Mapping Context to LoRA in a Single Pass

Y. Liu, X. Wang, Y. Mao, Y. Gelberg, H. Maron, M. Zhang

SubTokenTest: A Practical Benchmark for Real-World Sub-token Understanding

S. Hou, Y. Hu, M. Zhang