5 citations · 6 across the 31 of their papers we have counts for
36 papers
An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection
Xinyuan Wang, Deepti Agrawal, Yanjie Fu
High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the ``curse of dimensionality,''…
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…
Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting
Hongyu Cao, Xinyuan Wang, Arun Vignesh Malarkkan +3
In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between training and deployment (covar…
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…
Invariant Reasoning Directions in Latent Trajectories of Language Models
Arun Vignesh Malarkkan, Manan Roy Choudhury, Utkarsh Byahut +3
Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains poorly understood. We show tha…
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…
Evolving Demonstration Optimization for Chain-of-Thought Feature Transformation
Xinyuan Wang, Kunpeng Liu, Arun Vignesh Malarkkan +1
Feature Transformation (FT) is a core data-centric AI task that improves feature space quality to advance downstream predictive performance. However, discovering effective transfor…
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…
Learning to Route: A Rule-Driven Agent Framework for Hybrid-Source Retrieval-Augmented Generation
Haoyue Bai, Haoyu Wang, Shengyu Chen +5
Large Language Models (LLMs) have shown remarkable performance on general Question Answering (QA), yet they often struggle in domain-specific scenarios where accurate and up-to-dat…
Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensors
Chang Liu, Ladda Thiamwong, Yanjie Fu +1
Utilizing offline reinforcement learning (RL) with real-world clinical data is getting increasing attention in AI for healthcare. However, implementation poses significant challeng…
ISACL: Internal State Analyzer for Copyrighted Training Data Leakage
Guangwei Zhang, Qisheng Su, Jiateng Liu +4
Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP) but pose risks of inadvertently exposing copyrighted or proprietary data, especially when such da…
DELTA: Variational Disentangled Learning for Privacy-Preserving Data Reprogramming
Arun Vignesh Malarkkan, Haoyue Bai, Anjali Kaushik +1
In real-world applications, domain data often contains identifiable or sensitive attributes, is subject to strict regulations (e.g., HIPAA, GDPR), and requires explicit data featur…
Data-Efficient Symbolic Regression via Foundation Model Distillation
Wangyang Ying, Jinghan Zhang, Haoyue Bai +5
Discovering interpretable mathematical equations from observed data (a.k.a. equation discovery or symbolic regression) is a cornerstone of scientific discovery, enabling transparen…
Distribution Shift Aware Neural Tabular Learning
Wangyang Ying, Nanxu Gong, Dongjie Wang +5
Tabular learning transforms raw features into optimized spaces for downstream tasks, but its effectiveness deteriorates under distribution shifts between training and testing data.…
MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs
Sixun Dong, Juhua Hu, Mian Zhang +3
Vision-Language Models (VLMs) demonstrate impressive performance in understanding visual content with language instruction by converting visual inputs to vision tokens. However, re…
Incremental Causal Graph Learning for Online Cyberattack Detection in Cyber-Physical Infrastructures
Arun Vignesh Malarkkan, Dongjie Wang, Haoyue Bai +1
The escalating threat of cyberattacks on real-time critical infrastructures poses serious risks to public safety, demanding detection methods that effectively capture complex syste…
Rethinking Spatio-Temporal Anomaly Detection: A Vision for Causality-Driven Cybersecurity
Arun Vignesh Malarkkan, Haoyue Bai, Xinyuan Wang +3
As cyber-physical systems grow increasingly interconnected and spatially distributed, ensuring their resilience against evolving cyberattacks has become a critical priority. Spatio…
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…
Teaching Time Series to See and Speak: Forecasting with Aligned Visual and Textual Perspectives
Sixun Dong, Wei Fan, Teresa Wu +1
Time series forecasting traditionally relies on unimodal numerical inputs, which often struggle to capture high-level semantic patterns due to their dense and unstructured nature.…
LLM-ML Teaming: Integrated Symbolic Decoding and Gradient Search for Valid and Stable Generative Feature Transformation
Xinyuan Wang, Haoyue Bai, Nanxu Gong +4
Feature transformation enhances data representation by deriving new features from the original data. Generative AI offers potential for this task, but faces challenges in stable ge…