Papers by Yibo Peng
Question Generation from SQL Queries Improves Neural Semantic Parsing (D18-1)
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| Challenge: | Using question generation, we learn a semantic parser with 30% of the supervised training data. |
| Approach: | They propose to use question generation to learn a semantic parser with less supervised training data. |
| Outcome: | The proposed method improves the state-of-the-art model with less training data. |
Unifying Language Agent Algorithms with Graph-based Orchestration Engine for Reproducible Agent Research (2025.acl-demo)
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Qianqian Zhang, Jiajia Liao, Heting Ying, Yibo Ma, Haozhan Shen, Jingcheng Li, Peng Liu, Lu Zhang, Chunxin Fang, Kyusong Lee, Ruochen Xu, Tiancheng Zhao
| Challenge: | Language agents powered by large language models (LLMs) have demonstrated remarkable capabilities in understanding, reasoning, and executing complex tasks. |
| Approach: | They propose a flexible framework that addresses engineering overhead and insufficient evaluation frameworks for fair comparison. |
| Outcome: | The proposed framework simplifies language agent development and establishes a foundation for reproducible agent research. |
SimpleOCR: Rendering Visual Questions to Teach MLLMs to Read (2026.findings-acl)
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Yibo Peng, Peng Xia, Ding Zhong, Kaide Zeng, Siwei Han, Yiyang Zhou, Jiaqi Liu, Ruiyi Zhang, Huaxiu Yao
| Challenge: | MLLMs lack visual grounding mechanism to read text embedded in images, or rely on parametric shortcuts . despite strong OCR capabilities, models suffer performance degradation of 12.7% in the VQ setting . |
| Approach: | They propose a plug-and-play training strategy that invalidates shortcuts in text prompts . they propose 'vq' setting where text queries are rendered directly onto images . |
| Outcome: | The proposed training strategy surpasses the base model by 5.4% and GRPO based on original images by 2.7% on four representative OOD benchmarks. |
When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents? (2026.acl-long)
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Yibo Peng, James Song, Lei Li, Xinyu Yang, Mihai Christodorescu, Ravi Mangal, Corina S. Pasareanu, Haizhong Zheng, Beidi Chen
| Challenge: | Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses on functional correctness. |
| Approach: | They propose to attack functionally correct yet vulnerable (FCV) patches by combining multi-turn reasoning with tool invocation and environment interaction. |
| Outcome: | The proposed FCV-Attack achieves an attack success rate of 40.7% on GPT-5 Mini + OpenHands. |
PDR: A Plug-and-Play Positional Decay Framework for LLM Pre-training Data Detection (2026.acl-long)
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| Challenge: | Existing likelihood-based methods for detecting pretraining data are limited in black-box, zero-shot settings. |
| Approach: | They propose a training-free and plug-and-play framework that reweights token-level scores to amplify distinct signals from early positions while suppressing noise from later ones. |
| Outcome: | The proposed framework amplifys signals from early positions while suppressing noise from later positions. |
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)
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Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu
| Challenge: | Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework . |
| Approach: | They propose a training-free inference framework that simulates a metacognitive self-correction process. |
| Outcome: | The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE. |
Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)
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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin
| Challenge: | Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages . |
| Approach: | They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs. |
| Outcome: | Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages. |
Inference Compute-Optimal Video Vision Language Models (2025.acl-long)
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| Challenge: | Using video vision language models, inference costs are often more expensive than finetuning. |
| Approach: | They investigate the optimal allocation of inference compute across three key scaling factors in video vision language models. |
| Outcome: | The proposed model configurations are based on three key scaling factors . the results can be applied to real-world tasks and tasks with fixed inference budgets. |