3 citations · 3 across the 11 of their papers we have counts for
7 papers · 1 filter
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…