1 citations · 1 across the 15 of their papers we have counts for
19 papers
Small but Mighty: Test Suites as Improvement Signals for Self-Evolving Agents
Yutong Cheng, Wei Cheng, Wenchao Yu +2
A self-evolving agent rewrites its own harness and must decide which rewrites to keep, so its improvement signal is the check a rewrite has to pass. Verifying a rewrite by agent ro…
ForkPilot: Self-Evolving Policy for Retrospective Search in Long-Horizon Agents
Xinyue Zeng, Shivam Shandilya, Guilherme Potje +6
Interactive language-model agents increasingly solve complex tasks through long-horizon, multi-call reasoning, where errors in beliefs or actions can compound across tool interacti…
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…
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…
Think Again or Think Longer? Selective Verification for Budget-Aware Reasoning
Sajib Acharjee Dip, Dawei Zhou, Liqing Zhang
Test-time reasoning is increasingly used as a serving-time control knob, but extra reasoning is not uniformly valuable: it can repair failed attempts, waste compute on already-corr…
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-…
METASYMBO: Multi-Agent Language-Guided Metamaterial Discovery via Symbolic Latent Evolution
Jianpeng Chen, Wangzhi Zhan, Dongqi Fu +5
Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficiently generate candidates, but…
Navigating Ideation Space: Decomposed Conceptual Representations for Positioning Scientific Ideas
Yuexi Shen, Minqian Liu, Dawei Zhou +1
Scientific discovery requires new ideas to be situated within an ever-expanding landscape of existing knowledge. An emerging challenge is how to identify conceptually relevant prio…
Data Value in the Age of Scaling: Understanding LLM Scaling Dynamics Under Real-Synthetic Data Mixtures
Haohui Wang, Jingyuan Qi, Jianpeng Chen +9
The rapid progress of large language models (LLMs) is fueled by the growing reliance on datasets that blend real and synthetic data. While synthetic data offers scalability and cos…
MatSciBench: Benchmarking the Reasoning Ability of Large Language Models in Materials Science
Junkai Zhang, Jingru Gan, Xiaoxuan Wang +8
Large Language Models have shown strong scientific reasoning ability, but their performance on materials science problems remains less studied. To fill this gap, we introduce MatSc…
UniMate: A Unified Model for Mechanical Metamaterial Generation, Property Prediction, and Condition Confirmation
Wangzhi Zhan, Jianpeng Chen, Dongqi Fu +1
Metamaterials are artificial materials that are designed to meet unseen properties in nature, such as ultra-stiffness and negative materials indices. In mechanical metamaterial des…
LENSLLM: Unveiling Fine-Tuning Dynamics for LLM Selection
Xinyue Zeng, Haohui Wang, Junhong Lin +3
The proliferation of open-sourced Large Language Models (LLMs) and diverse downstream tasks necessitates efficient model selection, given the impracticality of fine-tuning all cand…
MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery
Jianpeng Chen, Wangzhi Zhan, Haohui Wang +10
Metamaterials, engineered materials with architected structures across multiple length scales, offer unprecedented and tunable mechanical properties that surpass those of conventio…
Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
Adithya Kulkarni, Fatimah Alotaibi, Xinyue Zeng +7
Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmen…
Plan and Budget: Effective and Efficient Test-Time Scaling on Reasoning Large Language Models
Junhong Lin, Xinyue Zeng, Jie Zhu +4
Large Language Models (LLMs) have achieved remarkable success in complex reasoning tasks, but their inference remains computationally inefficient. We observe a common failure mode…
MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design
Jingyuan Qi, Zian Jia, Minqian Liu +15
The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resour…
Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models
Mohammad Beigi, Sijia Wang, Ying Shen +9
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating…