3 citations · 18 across the 48 of their papers we have counts for
38 papers · 1 filter
Co-Evolution in Agentic Systems: Toward Self-Directed Evolution Beyond Human Design
Qing Zong, Jiayu Liu, Junhao Shen +9
Agentic systems are increasingly expected to improve after deployment, yet single-entity self-evolution is often bounded by a static learning context, such as fixed tasks and feedb…
Can Language Models Imagine Without Seeing? Ekphrasis: Measuring Visual Creative Ideation in Text-Only LLMs
Hongyu Luo, He Wang, Huihao Jing +6
Current evaluations do not isolate whether text-only language models can originate visual concepts before image generation. Fluent visual prose can hide visual-plan failures: an an…
EvolvingWorld: An Open-Schema Framework for Co-Evolving Role-Play Agents and World Model in Interactive Literary World
Qing Zong, Yue Guo, Mengxin Yang +2
This paper introduces EvolvingWorld, a framework and benchmark for character and world co-evolution in interactive literary worlds. Existing systems either treat interactive litera…
SciLens: Multi-modal Scientific Claim Verification with Agentic Entailment and Grounding
Yueming Wang, Tianshi Zheng, Jiaxin Bai +3
Scientific discovery increasingly relies on automated systems that generate hypotheses, inspect multimodal evidence, and validate claims at scale. Yet scientific claim verification…
SING: Synthetic Intention Graph for Scalable Active Tool Discovery in LLM Agents
Qiao Xiao, Haochen Shi, Yisen Gao +9
Large language model (LLM) agents increasingly rely on agent harnesses that manage context, tools, and multi-turn execution, making tools a central interface for acting in realisti…
CritiCal: Can Critique Help LLM Uncertainty or Confidence Calibration?
Qing Zong, Jiayu Liu, Tianshi Zheng +7
Accurate confidence calibration in Large Language Models (LLMs) is critical for safe use in high-stakes domains, where clear verbalized confidence enhances user trust. Traditional…
SessionIntentBench: A Multi-task Inter-session Intention-shift Modeling Benchmark for E-commerce Customer Behavior Understanding
Yuqi Yang, Weiqi Wang, Baixuan Xu +13
Session history is a common way of recording user interacting behaviors throughout a browsing activity with multiple products. For example, if an user clicks a product webpage and…
INFERENCEDYNAMICS: Efficient Routing Across LLMs through Structured Capability and Knowledge Profiling
Haochen Shi, Tianshi Zheng, Weiqi Wang +6
Large Language Model (LLM) routing is a pivotal technique for navigating a diverse landscape of LLMs, aiming to select the best-performing LLMs tailored to the domains of user quer…
EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association
Weiqi Wang, Limeng Cui, Xin Liu +14
Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-com…
Towards Multi-Agent Reasoning Systems for Collaborative Expertise Delegation: An Exploratory Design Study
Baixuan Xu, Chunyang Li, Weiqi Wang +6
Designing effective collaboration structure for multi-agent LLM systems to enhance collective reasoning is crucial yet remains under-explored. In this paper, we systematically inve…
Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?
Jiayu Liu, Qing Zong, Weiqi Wang +1
As large language models (LLMs) are increasingly used in high-stakes domains, accurately assessing their confidence is crucial. Humans typically express confidence through epistemi…
Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents
Wei Fan, Tianshi Zheng, Yiran Hu +7
Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. Wh…
From Automation to Autonomy: A Survey on Large Language Models in Scientific Discovery
Tianshi Zheng, Zheye Deng, Hong Ting Tsang +4
Large Language Models (LLMs) are catalyzing a paradigm shift in scientific discovery, evolving from task-specific automation tools into increasingly autonomous agents and fundament…
arXiv2Table: Toward Realistic Benchmarking and Evaluation for LLM-Based Literature-Review Table Generation
Weiqi Wang, Jiefu Ou, Yangqiu Song +2
Literature review tables are essential for summarizing and comparing collections of scientific papers. In this paper, we study the automatic generation of such tables from a pool o…
The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning
Tianshi Zheng, Yixiang Chen, Chengxi Li +7
Chain-of-Thought (CoT) prompting has been widely recognized for its ability to enhance reasoning capabilities in large language models (LLMs). However, our study reveals a surprisi…
Intention Knowledge Graph Construction for User Intention Relation Modeling
Jiaxin Bai, Zhaobo Wang, Junfei Cheng +8
Understanding user intentions is challenging for online platforms. Recent work on intention knowledge graphs addresses this but often lacks focus on connecting intentions, which is…
ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning
Liyu Zhang, Weiqi Wang, Tianqing Fang +1
Knowledge Editing (KE) aims to adjust a Large Language Model's (LLM) internal representations and parameters to correct inaccuracies and improve output consistency without incurrin…
KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?
Tianshi Zheng, Weihan Li, Jiaxin Bai +2
Retrieval-Augmented Generation (RAG) systems show remarkable potential as question answering tools in the K-12 Education domain, where knowledge is typically queried within the res…
EcomEdit: An Automated E-commerce Knowledge Editing Framework for Enhanced Product and Purchase Intention Understanding
Ching Ming Samuel Lau, Weiqi Wang, Haochen Shi +3
Knowledge Editing (KE) aims to correct and update factual information in Large Language Models (LLMs) to ensure accuracy and relevance without computationally expensive fine-tuning…
CodeGraph: Enhancing Graph Reasoning of LLMs with Code
Qiaolong Cai, Zhaowei Wang, Shizhe Diao +2
With the increasing popularity of large language models (LLMs), reasoning on basic graph algorithm problems is an essential intermediate step in assessing their abilities to proces…
GoldCoin: Grounding Large Language Models in Privacy Laws via Contextual Integrity Theory
Wei Fan, Haoran Li, Zheye Deng +2
Privacy issues arise prominently during the inappropriate transmission of information between entities. Existing research primarily studies privacy by exploring various privacy att…
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions
Weiqi Wang, Tianqing Fang, Haochen Shi +8
Conceptualization, a fundamental element of human cognition, plays a pivotal role in human generalizable reasoning. Generally speaking, it refers to the process of sequentially abs…
MIND: Multimodal Shopping Intention Distillation from Large Vision-language Models for E-commerce Purchase Understanding
Baixuan Xu, Weiqi Wang, Haochen Shi +12
Improving user experience and providing personalized search results in E-commerce platforms heavily rely on understanding purchase intention. However, existing methods for acquirin…
IntentionQA: A Benchmark for Evaluating Purchase Intention Comprehension Abilities of Language Models in E-commerce
Wenxuan Ding, Weiqi Wang, Sze Heng Douglas Kwok +11
Enhancing Language Models' (LMs) ability to understand purchase intentions in E-commerce scenarios is crucial for their effective assistance in various downstream tasks. However, p…