55 citations · 61 across the 30 of their papers we have counts for
33 papers
A Missing Piece for Trustworthy AI Reviewers: From Benchmarking Rhetorical Robustness to SciCore Review
Chenguang Wang, Ming Li, Chengrui Fan +4
AI reviewers can assign different judgments to manuscripts that report the same science in different wording, potentially rewarding rhetorical optimization over scientific improvem…
The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing
Chenguang Wang, Ming Li, Adebayo Braimah +6
Generative and agentic AI are reshaping both the production and evaluation of scientific research. These developments are often studied separately, as questions of how AI can produ…
Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions
Chenrui Fan, Yize Cheng, Ming Li +3
Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one question at a time. Yet when multiple…
How Can Rhetoric Reward-Hack AI Reviewers? Dissecting Rhetorical Sensitivity in AI-Based Peer Review
Ming Li, Chenguang Wang, Xirui Li +5
As large language models increasingly participate in scientific evaluation, we investigate a potential form of reward hacking: how rhetorical choices shape AI-review judgments when…
Representing Visual Evidence for Item Difficulty Prediction: Visual Textualization and Image-Native Modeling
Han Chen, Ming Li, Hong Jiao +1
Predicting item difficulty from content can provide an initial estimate for newly developed questions before sufficient student responses are available. Existing approaches typical…
Can LLMs Really Understand Item Difficulty Levels? Implications for Automated Item Generation Using LLMs
Xinyi Wang, Hong Jiao, Ming Li +4
The estimation of item difficulty plays a key role in both formative assessment and large-scale high-stakes summative assessments. This study explores how large language models (LL…
Self-Evolving Visual Questioner
Yijun Liang, Hengguang Zhou, Ming Li +3
Vision-language models (VLMs) are typically trained as passive answerers, while their ability to actively ask diverse, non-trivial, visual-centric and grounded questions remains un…
When is Your LLM Steerable?
Chenrui Fan, Yize Cheng, Ming Li +2
Activation steering offers a lightweight approach to control language models' behavior at inference time, but whether it succeeds or fails heavily depends on the prompt, concept, m…
Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction
Chenguang Wang, Ming Li, Xinyue Zeng +4
Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on c…
LLMs Struggle to Measure What Distinguishes Students of Different Proficiency Levels: A Study of Item Discrimination in Reading Comprehension Assessment
Han Chen, Ming Li, Chenguang Wang +4
Existing work on LLM-based educational assessment has focused largely on item difficulty, but difficulty alone does not indicate whether an item meaningfully distinguishes higher-…
Superminds Test: Actively Evaluating Collective Intelligence of Agent Society via Probing Agents
Xirui Li, Ming Li, Yunze Xiao +4
Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language model agents scale to populat…
When AI Navigates the Fog of War
Ming Li, Xirui Li, Tianyi Zhou
Can AI reason about a war before its trajectory becomes historically obvious? Analyzing this capability is difficult because retrospective geopolitical prediction is heavily confou…
Does Socialization Emerge in AI Agent Society? A Case Study of Moltbook
Ming Li, Xirui Li, Tianyi Zhou
As large language model agents increasingly populate networked environments, a fundamental question arises: do artificial intelligence (AI) agent societies undergo convergence dyna…
What does RL improve for Visual Reasoning? A Frankenstein-Style Analysis
Xirui Li, Ming Li, Tianyi Zhou
Reinforcement learning (RL) with verifiable rewards has become a standard post-training stage for boosting visual reasoning in vision-language models, yet it remains unclear what c…
Schoenfeld's Anatomy of Mathematical Reasoning by Language Models
Ming Li, Chenrui Fan, Yize Cheng +2
Large language models increasingly expose reasoning traces, yet their underlying cognitive structure and steps remain difficult to identify and analyze beyond surface-level statist…
Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction
Ming Li, Han Chen, Yunze Xiao +3
Accurate estimation of item (question or task) difficulty is critical for educational assessment but suffers from the cold start problem. While Large Language Models demonstrate su…
V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions
Chenrui Fan, Yijun Liang, Shweta Bhardwaj +3
While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle i…
Automated Alignment of Math Items to Content Standards in Large-Scale Assessments Using Language Models
Qingshu Xu, Hong Jiao, Tianyi Zhou +4
Accurate alignment of items to content standards is critical for valid score interpretation in large-scale assessments. This study evaluates three automated paradigms for aligning…
Text-Based Approaches to Item Alignment to Content Standards in Large-Scale Reading & Writing Tests
Yanbin Fu, Hong Jiao, Tianyi Zhou +5
Aligning test items to content standards is a critical step in test development to collect validity evidence based on content. Item alignment has typically been conducted by human…
Text-Based Approaches to Item Difficulty Modeling in Large-Scale Assessments: A Systematic Review
Sydney Peters, Nan Zhang, Hong Jiao +3
Item difficulty plays a crucial role in test performance, interpretability of scores, and equity for all test-takers, especially in large-scale assessments. Traditional approaches…
Understanding the Thinking Process of Reasoning Models: A Perspective from Schoenfeld's Episode Theory
Ming Li, Nan Zhang, Chenrui Fan +6
While Large Reasoning Models (LRMs) generate extensive chain-of-thought reasoning, we lack a principled framework for understanding how these thoughts are structured. In this paper…
VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding
Jian Chen, Ming Li, Jihyung Kil +6
Most organizational data in this world are stored as documents, and visual retrieval plays a crucial role in unlocking the collective intelligence from all these documents. However…
What makes Reasoning Models Different? Follow the Reasoning Leader for Efficient Decoding
Ming Li, Zhengyuan Yang, Xiyao Wang +4
Large reasoning models (LRMs) achieve strong reasoning performance by emitting long chains of thought. Yet, these verbose traces slow down inference and often drift into unnecessar…
Towards Visual Text Grounding of Multimodal Large Language Model
Ming Li, Ruiyi Zhang, Jian Chen +7
Despite the existing evolution of Multimodal Large Language Models (MLLMs), a non-neglectable limitation remains in their struggle with visual text grounding, especially in text-ri…