Papers by Xinrun Xu
SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning (2026.findings-acl)
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| Challenge: | Reinforcement learning (RL) is a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain informative as models evolve. |
| Approach: | They propose a framework that sustains effective learning signals through adaptive environment design that transforms real-world programming problems into verifiable reasoning environments with controllable difficulty and unbounded instance generation. |
| Outcome: | The proposed framework outperforms baselines across diverse reasoning benchmarks and exhibits more stable, long-horizon training dynamics. |
MindRef: Mimicking Human Memory for Hierarchical Reference Retrieval with Fine-Grained Location Awareness (2025.acl-short)
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| Challenge: | Existing methods require pre-segmented article chunks, limiting reference flexibility like human memory. |
| Approach: | They propose a framework that leverages parameterized knowledge stored during the pre-training phase of large language models to recall reference passages from any starting position independently. |
| Outcome: | The proposed framework can recall reference passages from any starting position independently. |
Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach (2025.findings-acl)
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Yurong Wu, Fangwen Mu, Qiuhong Zhang, Jinjing Zhao, Xinrun Xu, Lingrui Mei, Yang Wu, Lin Shi, Junjie Wang, Zhiming Ding, Yiwei Wang
| Challenge: | Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates that can generate similar images. |
| Approach: | They propose a prompt-stealing benchmark consisting of 50 templates and 450 images organized into Easy and Hard difficulty levels. |
| Outcome: | The proposed method outperforms baseline methods with an average improvement of over 10%. |