3 citations · 3 across the 11 of their papers we have counts for
13 papers
MetaPersona: Task-Grounded Synthetic Populations from Empirical Social Science
Jinyi Ye, Yuangang Li, Chenxiao Yu +7
Personas used to seed LLM social simulations face a cold-start problem: existing methods lack a principled basis for deciding which attributes to include and how to assign their va…
The Geometry of Logic: Stratification Induces Semantic Structure and Robust Reasoning
Cristina V. Lopes, Yuangang Li, Justin Tian Jin Chen +3
Transformer-based language models perform well on symbolic tasks, yet it remains unclear whether they learn generalizable rules or rely on statistical shortcuts. Mechanistic studie…
Harbor Adapters and Harbor-Index: Infrastructure and a Curated Meta-Dataset for Large-Scale Agentic Evaluation
Lin Shi, Haowei Lin, Zixuan Zhu +123
Evaluating agents on the growing number of agentic benchmarks is challenging because they often require complex environments and agent integrations. We introduce Harbor Adapters, a…
The Orchestration Gap: Why Process Automation Stalls in Operationally Complex Industries
Jiechao Gao, Yuandong Pan. Yuangang Li, Jie Wang +2
Agentic systems have advanced quickly on digitally native tasks, yet they have barely touched the industries where coordinated automation could matter most: logistics, healthcare o…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +378
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines
Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4
Pretrained language models (PLMs) like BERT provide strong semantic representations but are costly and opaque, while symbolic models such as the Tsetlin Machine (TM) offer transpar…
Beyond Output Correctness: Benchmarking and Evaluating Large Language Model Reasoning in Coding Tasks
Yuangang Li, Justin Tian Jin Chen, Ethan Yu +2
Large language models (LLMs) increasingly rely on explicit reasoning to solve coding tasks, yet evaluating the quality of this reasoning remains challenging. Existing reasoning eva…
Mitigating Hallucinations in Large Language Models via Causal Reasoning
Yuangang Li, Yiqing Shen, Yi Nian +7
Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relatio…
A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science
Chenxiao Yu, Jinyi Ye, Yuangang Li +4
While LLMs have demonstrated remarkable capabilities in text generation and reasoning, their ability to simulate human decision-making -- particularly in political contexts -- rema…
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Tiankai Yang, Yi Nian, Shawn Li +9
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural lang…
H-FedSN: Personalized Sparse Networks for Efficient and Accurate Hierarchical Federated Learning for IoT Applications
Jiechao Gao, Yuangang Li, Jie Wang +3
With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use of distributed data. However, con…
NLP-ADBench: NLP Anomaly Detection Benchmark
Yuangang Li, Jiaqi Li, Zhuo Xiao +4
Anomaly detection (AD) is an important machine learning task with applications in fraud detection, content moderation, and user behavior analysis. However, AD is relatively underst…
Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models
Chenxiao Yu, Zhaotian Weng, Yuangang Li +3
Can Large Language Models (LLMs) accurately predict election outcomes? While LLMs have demonstrated impressive performance in various domains, including healthcare, legal analysis,…