Papers by Tianqi Xu
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)
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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li
| Challenge: | Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators. |
| Approach: | They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods. |
| Outcome: | The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface. |
SOLAR: Serendipity Optimized Language Model Aligned for Recommendation (2025.findings-emnlp)
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Zichen Yuan, Lifan Sun, Yucen Zhuang, Yue Wang, Xinyuan Song, Tianqi Xu, Siyuan Li, Junchen Fu, Youhua Li, Sirui Hong, Jiaqi Chen, Joemon M. Jose, Yongxin Ni
| Challenge: | Large Language Models have shown strong potential in recommendation tasks . however, their application to serendipity-oriented recommendations remains challenging . |
| Approach: | They propose a domain-adaptive instruction tuning method that aligns Large Language Models with recommendation tasks. |
| Outcome: | The proposed framework bridges the domain gap between LLMs and recommendation tasks. |
PromptSculptor: Multi-Agent Based Text-to-Image Prompt Optimization (2025.emnlp-demos)
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| Challenge: | PromptSculptor automates the iterative prompt optimization process for Text-to-Image models . previous work focused on generating detailed, high-quality prompts based on user feedback . |
| Approach: | They propose a framework that decomposes a task into four specialized agents . they use Chain-of-Thought reasoning to transform a short, vague user prompt into a comprehensive, refined prompt. |
| Outcome: | The proposed framework significantly improves output quality and reduces iterations needed for user satisfaction. |
RQT: Hierarchical Residual Quantization for Multi-Model Compression (2025.findings-acl)
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| Challenge: | Existing methods for decomposing fine-tuned LLMs are sensitive to the magnitude of delta values. |
| Approach: | They propose a hierarchical quantization framework that shares low-bit integer weights across similar models. |
| Outcome: | The proposed framework achieves an average accuracy degradation of approximately 3% on fine-tuned models across mathematics, coding, chatbot, and Chinese LLMs. |
pFedGPT: Hierarchically Optimizing LoRA Aggregation Weights for Personalized Federated GPT Models (2025.emnlp-main)
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| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) struggle with data heterogeneity and adapt shared global knowledge to individual client needs. |
| Approach: | They propose a framework that leverages Hierarchical Bayesian Optimization (HBO) for fine-grained, personalized LoRA aggregation. |
| Outcome: | The proposed framework achieves state-of-the-art (SOTA) performance on personalized FL benchmarks while introducing only minimal (approx. 4%) additional optimization overhead. |
Q-Mamba: Towards more efficient Mamba models via post-training quantization (2025.findings-acl)
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| Challenge: | Existing studies show that Mamba architectures have room for further optimization in linear projections and state caches. |
| Approach: | They propose a decoupled scale quantization scheme to mitigate outliers in states and channels by applying separate quantization scales. |
| Outcome: | The proposed method reduces memory consumption by 50% across various quantization settings, model sizes, and generation and zero-shot tasks. |
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang
| Challenge: | Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data. |
| Approach: | They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data. |
| Outcome: | The proposed approach preserves key contextual information from the original data while reducing privacy risks. |