3 papers
cs.AI2026
Proof-of-Use: Mitigating Tool-Call Hacking in Deep Research Agents
SHengjie Ma, Chenlong Deng, Jiaxin Mao +5
While reinforcement learning (RL) enhances their ability to plan and reason across retrieval steps, we identify a critical failure mode in this setting: Tool-Call Hacking. Unlike e…
cs.AI2025
Leveraging Large Language Models for Relevance Judgments in Legal Case Retrieval
Shengjie Ma, Qi Chu, Jiaxin Mao +3
Determining which legal cases are relevant to a given query involves navigating lengthy texts and applying nuanced legal reasoning. Traditionally, this task has demanded significan…
cs.CL2025
Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
Shengjie Ma, Chengjin Xu, Xuhui Jiang +5
Retrieval-augmented generation (RAG) has improved large language models (LLMs) by using knowledge retrieval to overcome knowledge deficiencies. However, current RAG methods often f…