11 citations · 26 across the 14 of their papers we have counts for
9 papers · 1 filter
Towards Adaptive Continual Model Merging via Manifold-Aware Expert Evolution
Haiyun Qiu, Xingyu Wu, Kay Chen Tan
Continual Model Merging (CMM) sequentially integrates task-specific models into a unified architecture without intensive retraining. However, existing CMM methods are hindered by a…
Fine-Grained Model Merging via Modular Expert Recombination
Haiyun Qiu, Xingyu Wu, Liang Feng +1
Model merging constructs versatile models by integrating task-specific models without requiring labeled data or expensive joint retraining. Although recent methods improve adaptabi…
LLM Cannot Discover Causality, and Should Be Restricted to Non-Decisional Support in Causal Discovery
Xingyu Wu, Kui Yu, Jibin Wu +1
This paper critically re-evaluates LLMs' role in causal discovery and argues against their direct involvement in determining causal relationships. We demonstrate that LLMs' autoreg…
Diversity-Aware Policy Optimization for Large Language Model Reasoning
Jian Yao, Ran Cheng, Xingyu Wu +2
The reasoning capabilities of large language models (LLMs) have advanced rapidly, particularly following the release of DeepSeek R1, which has inspired a surge of research into dat…
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
Yu Zhou, Xingyu Wu, Jibin Wu +2
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in…
Design Principle Transfer in Neural Architecture Search via Large Language Models
Xun Zhou, Xingyu Wu, Liang Feng +2
Transferable neural architecture search (TNAS) has been introduced to design efficient neural architectures for multiple tasks, to enhance the practical applicability of NAS in rea…
Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
Xingyu Wu, Yan Zhong, Jibin Wu +3
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features. Although a few empirical…
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Yu Zhou, Xingyu Wu, Jibin Wu +2
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals th…
Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
Xingyu Wu, Yan Zhong, Jibin Wu +2
Algorithm selection, a critical process of automated machine learning, aims to identify the most suitable algorithm for solving a specific problem prior to execution. Mainstream al…