17 citations · 19 across the 14 of their papers we have counts for
14 papers
PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems
Hanglong Lv, Dawei Zhu, Lei Li +11
Large language models are increasingly used as agentic workflow executors, yet existing training data and benchmarks largely assume informationally complete, single-turn queries. O…
DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding
Jiebin Zhang, Zhenghan Yu, Song Liu +9
Block diffusion speculative decoding accelerates LLM inference by predicting all tokens within a block simultaneously for the target model to verify in parallel. Predicting an enti…
Video2GUI: Synthesizing Large-Scale Interaction Trajectories for Generalized GUI Agent Pretraining
Weimin Xiong, Shuhao Gu, Bowen Ye +5
Recent advances in multimodal large language models have driven growing interest in graphical user interface (GUI) agents, yet their generalization remains constrained by the scarc…
P-Aligner: Enabling Pre-Alignment of Language Models via Principled Instruction Synthesis
Feifan Song, Bofei Gao, Yifan Song +6
Large Language Models (LLMs) are expected to produce safe, helpful, and honest content during interaction with human users, but they frequently fail to align with such values when…
More Vulnerable than You Think: On the Stability of Tool-Integrated LLM Agents
Weimin Xiong, Ke Wang, Yifan Song +4
Current evaluations of tool-integrated LLM agents typically focus on end-to-end tool-usage evaluation while neglecting their stability. This limits their real-world applicability,…
WorkTeam: Constructing Workflows from Natural Language with Multi-Agents
Hanchao Liu, Rongjun Li, Weimin Xiong +2
Workflows play a crucial role in enhancing enterprise efficiency by orchestrating complex processes with multiple tools or components. However, hand-crafted workflow construction r…
MPO: Boosting LLM Agents with Meta Plan Optimization
Weimin Xiong, Yifan Song, Qingxiu Dong +4
Recent advancements in large language models (LLMs) have enabled LLM-based agents to successfully tackle interactive planning tasks. However, despite their successes, existing appr…
AgentBank: Towards Generalized LLM Agents via Fine-Tuning on 50000+ Interaction Trajectories
Yifan Song, Weimin Xiong, Xiutian Zhao +6
Fine-tuning on agent-environment interaction trajectory data holds significant promise for surfacing generalized agent capabilities in open-source large language models (LLMs). In…
EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection
Zheng Li, Dawei Zhu, Qilong Ma +2
Personality is a fundamental construct in psychology, reflecting an individual's behavior, thinking, and emotional patterns. Previous researches have made some progress in personal…
Watch Every Step! LLM Agent Learning via Iterative Step-Level Process Refinement
Weimin Xiong, Yifan Song, Xiutian Zhao +6
Large language model agents have exhibited exceptional performance across a range of complex interactive tasks. Recent approaches have utilized tuning with expert trajectories to e…
Rationale-Enhanced Language Models are Better Continual Relation Learners
Weimin Xiong, Yifan Song, Peiyi Wang +1
Continual relation extraction (CRE) aims to solve the problem of catastrophic forgetting when learning a sequence of newly emerging relations. Recent CRE studies have found that ca…
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective
Yifan Song, Peiyi Wang, Weimin Xiong +4
Continual learning (CL) aims to constantly learn new knowledge over time while avoiding catastrophic forgetting on old tasks. We focus on continual text classification under the cl…
RestGPT: Connecting Large Language Models with Real-World RESTful APIs
Yifan Song, Weimin Xiong, Dawei Zhu +9
Tool-augmented large language models (LLMs) have achieved remarkable progress in tackling a broad range of tasks. However, existing methods are mainly restricted to specifically de…
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Hongbo Wang, Weimin Xiong, Yifan Song +3
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction. However, most existing works focus on sentence-level coarse-grained JERE,…