Papers by Mu Cai
Look Twice before You Leap: A Rational Framework for Localized Adversarial Text Anonymization (2026.findings-acl)
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| Challenge: | Existing LLMs rely on remote API services, which creates privacy paradoxes and suboptimal solutions with severe utility collapse. |
| Approach: | They propose a localized and training-free framework with an Attacker-Arbitrator-Anonymizer architecture that allows attackers to filter out ghost leaks. |
| Outcome: | The proposed framework achieves superior privacy-utility trade-off compared to strong baselines. |
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples (2024.findings-acl)
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| Challenge: | Using high-performing text and image generation models, we can improve compositional reasoning capabilities for large multimodal models. |
| Approach: | They propose a framework to improve visio-linguistic compositional reasoning capability for multimodal models by leveraging physical grounding and using image-based models for semantic counterfactual fine-tuning. |
| Outcome: | The proposed framework outperforms GPT-4V and DALLE-3 on a new dataset and checkpoints. |
VGBench: A Comprehensive Benchmark of Vector Graphics Understanding and Generation for Large Language Models (2024.emnlp-main)
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| Challenge: | Current vision models use pixels to rasterize the visual world, but vector graphics are not the best or unique way to represent visual content. |
| Approach: | They propose a benchmark for vector graphics processing with capable Large Language Models . they use a set of questions to evaluate vector graphics formats and a wide range of prompting techniques . |
| Outcome: | The proposed benchmark compares LLMs on rasterized representations with vector graphics . it shows that LLM models show strong capability on both aspects . |
LANG: Reinforcement Learning for Multilingual Reasoning with Language-Adaptive Hint Guidance (2026.acl-long)
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Yuchun Fan, Bei Li, Peiguang Li, Yilin Wang, Yongyu Mu, Jian Yang, Xin Chen, Rongxiang Weng, Jingang Wang, Xunliang Cai, JingBo Zhu, Tong Xiao
| Challenge: | Existing methods for enhancing multi-step reasoning have not fully translated to multilingual contexts. |
| Approach: | They propose a framework that leverages language-conditioned hints to guide exploration in non-English reasoning tasks. |
| Outcome: | Empirical results show that the proposed framework improves reasoning performance without compromising language consistency. |