1 citations · 1 across the 9 of their papers we have counts for
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ExpEvolve: Structuring and Reusing Experience for Open-Ended Discovery
Yongqiang Chen, Chenxi Liu, Deyu Zou +4
Evolutionary coding agents such as AlphaEvolve are one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scienti…
Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling
Fan Feng, Yujia Zheng, Minghao Fu +5
Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making. However, existing approaches often rely on high-dime…
CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad
Yongqiang Chen, Chenxi Liu, Zhenhao Chen +3
Evolve-based agent such as AlphaEvolve is one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific probl…
ParamMem: Augmenting Language Agents with Parametric Reflective Memory
Tianjun Yao, Yongqiang Chen, Yujia Zheng +3
Self-reflection enables language agents to iteratively refine solutions, yet often produces repetitive outputs that limit reasoning performance. Recent studies have attempted to ad…
Pruning Spurious Subgraphs for Graph Out-of-Distribution Generalization
Tianjun Yao, Haoxuan Li, Yongqiang Chen +4
Graph Neural Networks (GNNs) often encounter significant performance degradation under distribution shifts between training and test data, hindering their applicability in real-wor…
Concept Concentration for Faithful Representation Intervention
Hongzheng Yang, Yongqiang Chen, Zeyu Qin +4
Representation intervention aims to localize and modify the representations that encode the underlying concepts in large language models (LLMs) to elicit the aligned and expected b…
Empowering Graph Invariance Learning with Deep Spurious Infomax
Tianjun Yao, Yongqiang Chen, Zhenhao Chen +3
Recently, there has been a surge of interest in developing graph neural networks that utilize the invariance principle on graphs to generalize the out-of-distribution (OOD) data. D…