5 citations · 6 across the 31 of their papers we have counts for
11 papers · 1 filter
Not All Answers Are Contextually Persuadable: Inference Dynamics in Large Language Models under Contextual Influence
Zongye Hu, Weiqing Luo, Yanjie Fu +3
At the core of modern prompting techniques is contextual sensitivity, the ability of large language models to adapt their predictions based on inference-time context. Despite its c…
Vision: Data-Centric Anchoring for Robust and Interpretable Agentic AI
Arun Vignesh Malarkkan, Xinyuan Wang, Yanjie Fu
Agentic AI systems built on large language models fail in two persistent ways that scaling does not fix: they break under distribution shift, and they cannot explain the decisions…
Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations
Nanxu Gong, Zixin Chen, Haotian Li +5
Improving the Theory of Mind (ToM) capability of Large Language Models (LLMs) is crucial for effective social interactions between these AI models and humans. However, the existing…
FinRule-Bench: A Benchmark for Joint Reasoning over Financial Tables and Principles
Arun Vignesh Malarkkan, Manan Roy Choudhury, Guangwei Zhang +4
Large language models (LLMs) are increasingly applied to financial analysis, yet their ability to audit structured financial statements under explicit accounting principles remains…
To Think or Not To Think, That is The Question for Large Reasoning Models in Theory of Mind Tasks
Nanxu Gong, Haotian Li, Sixun Dong +3
Theory of Mind (ToM) assesses whether models can infer hidden mental states such as beliefs, desires, and intentions, which is essential for natural social interaction. Although re…
Causally-Guided Automated Feature Engineering with Multi-Agent Reinforcement Learning
Arun Vignesh Malarkkan, Wangyang Ying, Yanjie Fu
Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing AFE methods rely on statistic…
Multi-Agent Procedural Graph Extraction with Structural and Logical Refinement
Wangyang Ying, Yanchi Liu, Xujiang Zhao +5
Automatically extracting workflows as procedural graphs from natural language is promising yet underexplored, demanding both structural validity and logical alignment. While recent…
Dataforge: Agentic Platform for Autonomous Data Engineering
Xinyuan Wang, Hongyu Cao, Kunpeng Liu +1
The growing demand for artificial intelligence (AI) applications in materials discovery, molecular modeling, and climate science has made data preparation a critical but labor-inte…
Supply Chain Optimization via Generative Simulation and Iterative Decision Policies
Haoyue Bai, Haoyu Wang, Nanxu Gong +4
High responsiveness and economic efficiency are critical objectives in supply chain transportation, both of which are influenced by strategic decisions on shipping mode. An integra…
Weaver: Interweaving SQL and LLM for Table Reasoning
Rohit Khoja, Devanshu Gupta, Yanjie Fu +2
Querying tables with unstructured data is challenging due to the presence of text (or image), either embedded in the table or in external paragraphs, which traditional SQL struggle…
LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations
Xinyuan Wang, Liang Wu, Liangjie Hong +2
Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend a…