17 citations · 19 across the 10 of their papers we have counts for
12 papers
To Reason but Not to Stop: Understanding and Recovering Pruned Large Reasoning Models
Minh-Anh Nguyen, Nguyen T. Ha Lan, Xiyan Fu +2
Weight pruning can degrade the accuracy of large reasoning models (LRMs), yet it remains unclear whether it damages reasoning itself. We find that much of this degradation stems fr…
DeltaTTG: Training Multi-Turn Agents with a Token-to-Go Progress Meter
Xuyan Ye, Xiyan Fu, Shengda Fan +3
Reinforcement learning with verifiable rewards (RLVR) for multi-turn agents relies on sparse terminal rewards that say little about individual steps. Within-group step comparisons…
Representation-Aware Advantage Estimation: Your Reward Model Provides More Than A Scalar Output
Guozheng Li, Xiyan Fu, Yiwen Guo
Current reinforcement learning from human feedback (RLHF) methods primarily rely on scalar rewards from a trained reward model (RM). While effective, scalar rewards are often noisy…
When Languages Disagree: Self-Evolving Multilingual LLM Judges
Xiyan Fu, Wei Lu
Multilingual LLM-as-a-judge is widely used to evaluate model outputs across languages, but suffers from cross-lingual inconsistency (Fu and Liu, 2025). Existing methods typically t…
Reinforcement Learning for Compositional Generalization with Outcome-Level Optimization
Xiyan Fu, Wei Liu
Compositional generalization refers to correctly interpret novel combinations of known primitives, which remains a major challenge. Existing approaches often rely on supervised fin…
How Reliable is Multilingual LLM-as-a-Judge?
Xiyan Fu, Wei Liu
LLM-as-a-Judge has emerged as a popular evaluation strategy, where advanced large language models assess generation results in alignment with human instructions. While these models…
The Mystery of Compositional Generalization in Graph-based Generative Commonsense Reasoning
Xiyan Fu, Anette Frank
While LLMs have emerged as performant architectures for reasoning tasks, their compositional generalization capabilities have been questioned. In this work, we introduce a Composit…
Exploring Continual Learning of Compositional Generalization in NLI
Xiyan Fu, Anette Frank
Compositional Natural Language Inference has been explored to assess the true abilities of neural models to perform NLI. Yet, current evaluations assume models to have full access…
Dynamic MOdularized Reasoning for Compositional Structured Explanation Generation
Xiyan Fu, Anette Frank
Despite the success of neural models in solving reasoning tasks, their compositional generalization capabilities remain unclear. In this work, we propose a new setting of the struc…
Modeling Structural Similarities between Documents for Coherence Assessment with Graph Convolutional Networks
Wei Liu, Xiyan Fu, Michael Strube
Coherence is an important aspect of text quality, and various approaches have been applied to coherence modeling. However, existing methods solely focus on a single document's cohe…
SETI: Systematicity Evaluation of Textual Inference
Xiyan Fu, Anette Frank
We propose SETI (Systematicity Evaluation of Textual Inference), a novel and comprehensive benchmark designed for evaluating pre-trained language models (PLMs) for their systematic…
Multi-modal Summarization for Video-containing Documents
Xiyan Fu, Jun Wang, Zhenglu Yang
Summarization of multimedia data becomes increasingly significant as it is the basis for many real-world applications, such as question answering, Web search, and so forth. Most ex…