27 citations · 64 across the 23 of their papers we have counts for
29 papers
Heterogeneous Agentic Coevolution
Wenqian Ye, Ziwei Guan, Zi Yang +14
Recent advances in self-improving LLM agents have demonstrated the potential of AI systems that drive their own intelligence. However, many domain-specific applications rely on spe…
Capturing In-Context Learning Dynamics with Task Operators
Guangzhi Xiong, Zhenghao He, Bohan Liu +3
In-context learning (ICL) enables language models to perform new tasks from demonstrations without weight updates. However, every ICL inference requires processing the full set of…
LabBook: Harnessing Experimental History for Efficient LLM-Driven Discovery
Bo Yuan, Wenqian Ye, Zelin Zhao +4
Evolutionary approaches to LLM-driven discovery often generate new programs from a small set of selected ancestors. This keeps contexts manageable but can omit useful evidence from…
Rethinking Reasoning Paths as Phase-Structured Trajectories
Zhenghao He, Guangzhi Xiong, Sanchit Sinha +3
Large language models often improve problem-solving performance by generating multi-step reasoning paths, yet how to analyze the hidden states along these paths remains unclear. Ex…
From World Models to World Action Models: Rethinking Next-State Prediction
Tingyu Yuan, Ziming Ji, Biaoliang Guan +9
Predicting the next state is a core paradigm of World Models for modeling physical dynamics, emphasizing prediction fidelity. As World Models evolve into World-Action Models (WAMs)…
ProofEvolve: Neuro-Symbolic Evolution for Formal Automated Theorem Proving
Wenqian Ye, Ziwei Guan, Eric Xie +6
Automated theorem proving offers a natural foundation for recursive self-improvement in scientific discovery. However, existing neural provers do not fully preserve this recursive…
ALPS: Measuring Valid Creativity in Large Language Models with Mathematical Construction
Eric Xie, Wenqian Ye, Aidong Zhang
Large language models produce outputs presented as discoveries - new proofs, conjectures, or molecules. Whether such an output that appears creative is truly original and effective…
Towards Robustness against Typographic Attack with Training-free Concept Localization
Bohan Liu, Wenqian Ye, Guangzhi Xiong +3
Models trained via Contrastive Language-Image Pretraining (CLIP) serve as the foundational vision encoders for most modern Large Vision Language Models (LVLMs). Despite their wides…
UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations
Tingyu Yuan, Biaoliang Guan, Wen Ye +8
In embodied intelligence, the embodiment gap between robotic and human hands brings significant challenges for learning from human demonstrations. Although some studies have attemp…
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
Medical Video Generation for Disease Progression Simulation
Xu Cao, Kaizhao Liang, Kuei-Da Liao +7
Modeling disease progression is crucial for improving the quality and efficacy of clinical diagnosis and prognosis, but it is often hindered by a lack of longitudinal medical image…
On-Board Vision-Language Models for Personalized Autonomous Vehicle Motion Control: System Design and Real-World Validation
Can Cui, Zichong Yang, Yupeng Zhou +11
Personalized driving refers to an autonomous vehicle's ability to adapt its driving behavior or control strategies to match individual users' preferences and driving styles while m…
MpoxVLM: A Vision-Language Model for Diagnosing Skin Lesions from Mpox Virus Infection
Xu Cao, Wenqian Ye, Kenny Moise +1
In the aftermath of the COVID-19 pandemic and amid accelerating climate change, emerging infectious diseases, particularly those arising from zoonotic spillover, remain a global th…
Benchmarking Spurious Bias in Few-Shot Image Classifiers
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Few-shot image classifiers are designed to recognize and classify new data with minimal supervision and limited data but often show reliance on spurious correlations between classe…
Spuriousness-Aware Meta-Learning for Learning Robust Classifiers
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Spurious correlations are brittle associations between certain attributes of inputs and target variables, such as the correlation between an image background and an object class. D…
MM-SpuBench: Towards Better Understanding of Spurious Biases in Multimodal LLMs
Wenqian Ye, Bohan Liu, Guangtao Zheng +6
Spurious bias, a tendency to exploit spurious correlations between superficial input attributes and prediction targets, has revealed a severe robustness pitfall in classical machin…
What is the Visual Cognition Gap between Humans and Multimodal LLMs?
Xu Cao, Yifan Shen, Bolin Lai +8
Recently, Multimodal Large Language Models (MLLMs) and Vision Language Models (VLMs) have shown great promise in language-guided perceptual tasks such as recognition, segmentation,…
Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation
Guangtao Zheng, Wenqian Ye, Aidong Zhang
Deep neural classifiers tend to rely on spurious correlations between spurious attributes of inputs and targets to make predictions, which could jeopardize their generalization cap…
The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning
Wenqian Ye, Luyang Jiang, Eric Xie +16
Back in the early 20th century, a horse named Hans appeared to perform arithmetic and other intellectual tasks during exhibitions in Germany, while it actually relied solely on inv…
LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
Yunsheng Ma, Can Cui, Xu Cao +9
Autonomous driving (AD) has made significant strides in recent years. However, existing frameworks struggle to interpret and execute spontaneous user instructions, such as "overtak…
A Survey on Multimodal Large Language Models for Autonomous Driving
Can Cui, Yunsheng Ma, Xu Cao +18
With the emergence of Large Language Models (LLMs) and Vision Foundation Models (VFMs), multimodal AI systems benefiting from large models have the potential to equally perceive th…
MACP: Efficient Model Adaptation for Cooperative Perception
Yunsheng Ma, Juanwu Lu, Can Cui +4
Vehicle-to-vehicle (V2V) communications have greatly enhanced the perception capabilities of connected and automated vehicles (CAVs) by enabling information sharing to "see through…
Receive, Reason, and React: Drive as You Say with Large Language Models in Autonomous Vehicles
Can Cui, Yunsheng Ma, Xu Cao +2
The fusion of human-centric design and artificial intelligence (AI) capabilities has opened up new possibilities for next-generation autonomous vehicles that go beyond transportati…
PIE: Simulating Disease Progression via Progressive Image Editing
Kaizhao Liang, Xu Cao, Kuei-Da Liao +6
Disease progression simulation is a crucial area of research that has significant implications for clinical diagnosis, prognosis, and treatment. One major challenge in this field i…