2 citations · 3 across the 10 of their papers we have counts for
10 papers
PARSER: Read in Parallel, Reason in Depth for Long-Context LLM Agents
Kun Li, Zexuan Qiu, Tianhua Zhang +2
Sequential memory agents process long documents by reading chunks one after another while maintaining a compact memory state, coupling document traversal to reasoning depth. This c…
TriggerBench: Investigating Prospective Memory for Large Language Models
Tianhua Zhang, Xinjiang Wang, Qianxi Zhang +6
While Large Language Models (LLMs) are increasingly deployed in long interactions, existing evaluations focus predominantly on retrospective memory (RM) via explicit queries. Prosp…
Agentic Cognitive Profiling: Realigning Automated Alzheimer's Disease Detection with Clinical Construct Validity
Jiawen Kang, Kun Li, Dongrui Han +5
Automated Alzheimer's Disease (AD) screening has predominantly followed the inductive paradigm of pattern recognition, which directly maps the input signal to the outcome label. Th…
TreePS-RAG: Tree-based Process Supervision for Reinforcement Learning in Agentic RAG
Tianhua Zhang, Kun Li, Junan Li +5
Agentic retrieval-augmented generation (RAG) formulates question answering as a multi-step interaction between reasoning and information retrieval, and has recently been advanced b…
RAG-Zeval: Towards Robust and Interpretable Evaluation on RAG Responses through End-to-End Rule-Guided Reasoning
Kun Li, Yunxiang Li, Tianhua Zhang +4
Robust evaluation is critical for deploying trustworthy retrieval-augmented generation (RAG) systems. However, current LLM-based evaluation frameworks predominantly rely on directl…
Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution
Kun Li, Tianhua Zhang, Yunxiang Li +5
Improving context faithfulness in large language models is essential for developing trustworthy retrieval augmented generation systems and mitigating hallucinations, especially in…
Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chains
Kun Li, Tianhua Zhang, Xixin Wu +3
Knowledge Graphs (KGs) can serve as reliable knowledge sources for question answering (QA) due to their structured representation of knowledge. Existing research on the utilization…
Purple-teaming LLMs with Adversarial Defender Training
Jingyan Zhou, Kun Li, Junan Li +4
Existing efforts in safeguarding LLMs are limited in actively exposing the vulnerabilities of the target LLM and readily adapting to newly emerging safety risks. To address this, w…
Adaptive Query Rewriting: Aligning Rewriters through Marginal Probability of Conversational Answers
Tianhua Zhang, Kun Li, Hongyin Luo +3
Query rewriting is a crucial technique for passage retrieval in open-domain conversational question answering (CQA). It decontexualizes conversational queries into self-contained q…
SGP-TOD: Building Task Bots Effortlessly via Schema-Guided LLM Prompting
Xiaoying Zhang, Baolin Peng, Kun Li +2
Building end-to-end task bots and maintaining their integration with new functionalities using minimal human efforts is a long-standing challenge in dialog research. Recently large…