28 citations · 71 across the 24 of their papers we have counts for
26 papers
R Flow: Recursive Self-Improvement via Recursive Skill Evolution
Mingda Zhang, Qiang Huang, Yanjin Li +4
LLM-based agents can improve themselves across tasks by reusing and revising the skills they orchestrate into executable procedures. Flow-based training fits this loop: it samples…
Beyond the Stability-Exploration Dilemma: Environmental Regularization for LLM Policy Optimization
Xianlei Zhou, Xiangdi Meng, Yu He +7
Policy optimization (PO) for Large Language Models faces a stability--exploration trade-off, currently mediated by an action-side Policy-KL regularizer. This puts practitioners in…
CoCR-RAG: Enhancing Retrieval-Augmented Generation in Web Q&A via Concept-oriented Context Reconstruction
Kaize Shi, Xueyao Sun, Qika Lin +4
Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. However, the supporting docu…
TIDE: Trajectory-based Diagnostic Evaluation of Test-Time Improvement in LLM Agents
Hang Yan, Xinyu Che, Fangzhi Xu +7
Recent advances in autonomous LLM agents demonstrate their ability to improve performance through iterative interaction with the environment. We define this paradigm as Test-Time I…
OdysseyArena: Benchmarking Large Language Models For Long-Horizon, Active and Inductive Interactions
Hang Yan, Fangzhi Xu, Qiushi Sun +14
The advancement of Large Language Models (LLMs) has catalyzed the development of autonomous agents capable of navigating complex environments. However, existing evaluations primari…
Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement
Jian Zhang, Zhangqi Wang, Zhiyuan Wang +5
Linguistic expressions of emotions such as depression, anxiety, and trauma-related states are pervasive in clinical notes, counseling dialogues, and online mental health communitie…
-Bench: Benchmarking Memory-Driven Scientific Reasoning via Anchor and Attractor Activation
Jian Zhang, Yu He, Zhiyuan Wang +5
Scientific reasoning relies not only on logical inference but also on activating prior knowledge and experiential structures. Memory can efficiently reuse knowledge and enhance rea…
MAXS: Meta-Adaptive Exploration with LLM Agents
Jian Zhang, Zhiyuan Wang, Zhangqi Wang +7
Large Language Model (LLM) Agents exhibit inherent reasoning abilities through the collaboration of multiple tools. However, during agent inference, existing methods often suffer f…
RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation
Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru +4
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by retrieving external data to mitigate hallucinations and outdated knowledge issues. Benefiting from the…
Towards Unified Neurosymbolic Reasoning on Knowledge Graphs
Qika Lin, Fangzhi Xu, Hao Lu +5
Knowledge Graph (KG) reasoning has received significant attention in the fields of artificial intelligence and knowledge engineering, owing to its ability to autonomously deduce ne…
Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learning
Haoran Luo, Haihong E, Guanting Chen +8
Retrieval-Augmented Generation (RAG) mitigates hallucination in LLMs by incorporating external knowledge, but relies on chunk-based retrieval that lacks structural semantics. Graph…
MUR: Momentum Uncertainty guided Reasoning for Large Language Models
Hang Yan, Fangzhi Xu, Rongman Xu +8
Large Language Models have achieved impressive performance on reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an open challenge. While Test-Time Scalin…
-Decoding: Adaptive Foresight Sampling for Balanced Inference-Time Exploration and Exploitation
Fangzhi Xu, Hang Yan, Chang Ma +4
Inference-time optimization scales computation to derive deliberate reasoning steps for effective performance. While previous search-based strategies address the short-sightedness…
GKG-LLM: A Unified Framework for Generalized Knowledge Graph Construction
Jian Zhang, Bifan Wei, Shihao Qi +3
The construction of Generalized Knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language…
MAPS: Multi-Agent Personality Shaping for Collaborative Reasoning
Jian Zhang, Zhiyuan Wang, Zhangqi Wang +6
Collaborative reasoning with multiple agents offers the potential for more robust and diverse problem-solving. However, existing approaches often suffer from homogeneous agent beha…
MARS: Multi-Agent Adaptive Reasoning with Socratic Guidance for Automated Prompt Optimization
Jian Zhang, Zhangqi Wang, Haiping Zhu +6
Large language models (LLMs) typically operate in a question-answering paradigm, where the quality of the input prompt critically affects the response. Automated Prompt Optimizatio…
PathReasoner: Modeling Reasoning Path with Equivalent Extension for Logical Question Answering
Fangzhi Xu, Qika Lin, Tianzhe Zhao +2
Logical reasoning task has attracted great interest since it was proposed. Faced with such a task, current competitive models, even large language models (e.g., ChatGPT and PaLM 2)…
Untargeted Adversarial Attack on Knowledge Graph Embeddings
Tianzhe Zhao, Jiaoyan Chen, Yanchi Ru +3
Knowledge graph embedding (KGE) methods have achieved great success in handling various knowledge graph (KG) downstream tasks. However, KGE methods may learn biased representations…
A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction
Jian Zhang, Changlin Yang, Haiping Zhu +3
Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-ba…
Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models
Fangzhi Xu, Zhiyong Wu, Qiushi Sun +6
Although Large Language Models (LLMs) demonstrate remarkable ability in processing and generating human-like text, they do have limitations when it comes to comprehending and expre…
A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics
Kai He, Rui Mao, Qika Lin +4
The utilization of large language models (LLMs) in the Healthcare domain has generated both excitement and concern due to their ability to effectively respond to freetext queries w…
Are Large Language Models Really Good Logical Reasoners? A Comprehensive Evaluation and Beyond
Fangzhi Xu, Qika Lin, Jiawei Han +3
Logical reasoning consistently plays a fundamental and significant role in the domains of knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs)…
Mind Reasoning Manners: Enhancing Type Perception for Generalized Zero-shot Logical Reasoning over Text
Fangzhi Xu, Jun Liu, Qika Lin +3
Logical reasoning task involves diverse types of complex reasoning over text, based on the form of multiple-choice question answering. Given the context, question and a set of opti…
Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning
Fangzhi Xu, Jun Liu, Qika Lin +2
Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability…