Papers by Miao Peng
Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping (2026.findings-acl)
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| Challenge: | Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs’ short-context reasoning but falters in long-contemporal scenarios requiring precise grounding and multi-hop reasoning. |
| Approach: | They propose a framework that constructs high-difficulty, multi-hop long-context QA pairs with inherent reasoning chains to overcome this bottleneck. |
| Outcome: | The proposed framework outperforms RLVR baselines and matches frontier LLMs while using far fewer parameters. |
Towards Explainable Temporal Reasoning in Large Language Models: A Structure-Aware Generative Framework (2025.findings-acl)
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| Challenge: | Existing studies on temporal reasoning models neglect the explainable reasoning processes underlying the results. |
| Approach: | They propose a structure-aware generative framework that integrates Graph structures with text for Explainable TEmporal Reasoning. |
| Outcome: | The proposed framework achieves state-of-the-art performance while also demonstrating robust generalization capabilities. |
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction (2022.findings-emnlp)
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| Challenge: | Existing methods for linking knowledge graphs lack contextual information in entity neighborhoods, which leads to false prediction results. |
| Approach: | They propose a Schema-augmented Multi-level contrastive LEarning framework to conduct knowledge graph link prediction using a knowledge graph schema. |
| Outcome: | The proposed framework is based on a knowledge graph schema and is compared against state-of-the-art datasets. |
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning (2024.findings-naacl)
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| Challenge: | Existing text-based methods for Temporal Knowledge Graph Reasoning struggle to balance textual knowledge and temporal information with expensive purpose-built training strategies. |
| Approach: | They propose a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning that feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance. |
| Outcome: | The proposed framework achieves superior performance on four transductive and three few-shot inductive TKGR benchmarks. |
RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning (2025.findings-acl)
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| Challenge: | Existing knowledge editing methods struggle to reason about related conceptual knowledge effectively, despite a lack of model-level relational reasoning. |
| Approach: | They propose a benchmark to assess concept-level and instance-level relational reasoning abilities of edited models. |
| Outcome: | The proposed model obtains the best scores on the memory-based in-context editing baseline, MICE, suggesting a promising direction for model editing. |
How does Misinformation Affect Large Language Model Behaviors and Preferences? (2025.acl-long)
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| Challenge: | Existing studies have explored the role of Large Language Models in combating misinformation, but there is still a lack of detailed analysis on the specific aspects and extent to which LLMs are influenced by misinformation. |
| Approach: | They propose to use a benchmark to evaluate LLMs' behavior and knowledge preference toward misinformation to identify their models. |
| Outcome: | The proposed approach is based on 10,346,712 pieces of misinformation and examines knowledge conflicts and stylistic variations. |
Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling (2025.emnlp-industry)
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Xiaoyu Liu, Di Liang, Hongyu Shan, Peiyang Liu, Yonghao Liu, Muling Wu, Yuntao Li, Xianjie Wu, Li Miao, Jiangrong Shen, Minlong Peng
| Challenge: | Generative RMs (GRMs) lack contextual and background information during inference, leading to incomplete evaluations. |
| Approach: | They propose a modular and interpretable framework that integrates side-branch models as auxiliary feature generators. |
| Outcome: | The proposed framework outperforms scalar and saline reward models in robustness and alignment with human preferences. |
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. |
Pre-trained Language Model with Prompts for Temporal Knowledge Graph Completion (2023.findings-acl)
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| Challenge: | Existing methods for temporal knowledge graph completion (TKGC) focus on extracting information from timestamps and insufficiently utilizing implied information in relations. |
| Approach: | They propose a temporal knowledge graph completion model with prompts that converts quadruples into pre-trained language inputs and prompts to make coherent sentences with implicit semantic information. |
| Outcome: | The proposed model can make coherent sentences with implicit semantic information. |
NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)
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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo
| Challenge: | a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Approach: | They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. |
| Outcome: | The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains. |