5 papers
EnvRL: Learn from Environment Dynamics in Agentic Reinforcement Learning
Zhitong Wang, Songze Li, Hao Peng +4
Reinforcement learning (RL) has emerged as a powerful paradigm for training Large Language Models (LLMs) as agents. However, conventional RL methods for long-horizon agentic tasks…
Crafter: A Multi-Agent Harness for Editable Scientific Figure Generation from Diverse Inputs
Haozhe Zhao, Shuzheng Si, Zhenhailong Wang +6
Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains one of the most labor-intensi…
InFi-Check: Interpretable and Fine-Grained Fact-Checking of LLMs
Yuzhuo Bai, Shuzheng Si, Kangyang Luo +5
Large language models (LLMs) often hallucinate, yet most existing fact-checking methods treat factuality evaluation as a binary classification problem, offering limited interpretab…
MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints
Kangyang Luo, Shuzheng Si, Yuzhuo Bai +8
In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, par…
ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement
Kangyang Luo, Yuzhuo Bai, Shuzheng Si +9
Coreference Resolution (CR) is a critical task in Natural Language Processing (NLP). Current research faces a key dilemma: whether to further explore the potential of supervised ne…