1 citations · 1 across the 9 of their papers we have counts for
9 papers
DeepInstructor: An Agentic AI Instructor for Experience-Driven Idea Evaluation
Rongcan Pei, Fang Guo, Qinglin Qi +7
As automated scientific discovery advances, Large Language Models (LLMs) can now generate research ideas at an unprecedented scale, shifting the bottleneck from idea generation to…
SafeReview: Defending LLM-based Review Systems Against Adversarial Hidden Prompts
Yuan Xin, Yixuan Weng, Minjun Zhu +5
As Large Language Models (LLMs) are increasingly integrated into academic peer review, their vulnerability to adversarial hidden prompts, i.e., adversarial instructions embedded in…
DeepReviewer 2.0: A Traceable Agentic System for Auditable Scientific Peer Review
Yixuan Weng, Minjun Zhu, Qiujie Xie +7
Automated peer review is often framed as generating fluent critique, yet reviewers and area chairs need judgments they can \emph{audit}: where a concern applies, what evidence supp…
AutoFigure-Edit: Generating Editable Scientific Illustration
Zhen Lin, Qiujie Xie, Minjun Zhu +10
High-quality scientific illustrations are essential for communicating complex scientific and technical concepts, yet existing automated systems remain limited in editability, styli…
AutoFigure: Generating and Refining Publication-Ready Scientific Illustrations
Minjun Zhu, Zhen Lin, Yixuan Weng +6
High-quality scientific illustrations are crucial for effectively communicating complex scientific and technical concepts, yet their manual creation remains a well-recognized bottl…
DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively
Yixuan Weng, Minjun Zhu, Qiujie Xie +4
While previous AI Scientist systems can generate novel findings, they often lack the focus to produce scientifically valuable contributions that address pressing human-defined chal…
Abduct, Act, Predict: Scaffolding Causal Inference for Automated Failure Attribution in Multi-Agent Systems
Alva West, Yixuan Weng, Minjun Zhu +3
Failure attribution in multi-agent systems -- pinpointing the exact step where a decisive error occurs -- is a critical yet unsolved challenge. Current methods treat this as a patt…
AI-Generated Text is Non-Stationary: Detection via Temporal Tomography
Alva West, Yixuan Weng, Minjun Zhu +4
The field of AI-generated text detection has evolved from supervised classification to zero-shot statistical analysis. However, current approaches share a fundamental limitation: t…
T-Detect: Tail-Aware Statistical Normalization for Robust Detection of Adversarial Machine-Generated Text
Alva West, Luodan Zhang, Liuliu Zhang +3
Large language models (LLMs) have shown the capability to generate fluent and logical content, presenting significant challenges to machine-generated text detection, particularly t…