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Yihong Tang

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AAAI Conference 2026 Conference Paper

QueryAligner: Customizing User Query to Match LLMs Preferences for Better Intent Recognition

  • Yunlong Ma
  • Bo Wang
  • Yihong Tang
  • Zifei Yu
  • Chenyun Xue
  • Gaoke Zhang
  • Yuexian Hou

The interpretative efficacy of large language models (LLMs) fundamentally hinges on the intricate alignment between user inputs and model-specific linguistic priors. Existing methodologies predominantly employ static input optimization strategies, failing to account for the empirically observed divergence in linguistic preference spaces across distinct LLM architectures, including variations in syntactic parsing heuristics, semantic grounding mechanisms, and knowledge retrieval pathways. We propose QueryAligner, an adaptive rewriting system implementing dynamic model-aware input transformation through architecture-specific preference modeling. Our framework introduces two pivotal innovations: 1) A dual-phase optimization engine integrating supervised learning on reverse-engineered cross-architectural training data with reinforcement learning driven by multi-objective reward signals, ensuring simultaneous preservation of semantic integrity and maximization of target model compatibility; 2) An architecture-informed rewriting protocol that automatically discovers latent alignment patterns encoded within distinct LLMs' parametric configurations. Experimental results demonstrate that our method achieves superior performance compared to conventional input optimization techniques.

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NeurIPS Conference 2025 Conference Paper

MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching

  • Liang Yue
  • Yihong Tang
  • Kehai Chen
  • Jie Liu
  • Min Zhang

Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-following capabilities and task-specific performance. However, obtaining high-quality fine-tuning data for large models is challenging due to data collection difficulties and high production costs. To address this, we propose MASTER, a novel data augmentation method that enriches original data through interactions among multiple agents with varying cognitive levels. We simulate three pedagogically grounded teaching scenarios, leveraging multi-agent conversations to generate high-quality teacher-student interaction data. Utilizing MASTER, we construct BOOST-QA, a fine-tuning dataset augmented from existing datasets like Orca-Math-200k, ProcQA, and OpenHermes2. 5. Experiments show that models fine-tuned with BOOST-QA perform excellently across multiple benchmarks, demonstrating strong multitask generalization. Notably, MASTER significantly improves models' reasoning abilities in complex tasks, providing valuable insights for future research.

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NeurIPS Conference 2025 Conference Paper

Thinking in Character: Advancing Role-Playing Agents with Role-Aware Reasoning

  • Yihong Tang
  • Kehai Chen
  • Muyun Yang
  • Zheng-Yu Niu
  • Jing Li
  • Tiejun Zhao
  • Min Zhang

The advancement of Large Language Models (LLMs) has spurred significant interest in Role-Playing Agents (RPAs) for applications such as emotional companionship and virtual interaction. However, recent RPAs are often built on explicit dialogue data, lacking deep, human-like internal thought processes, resulting in superficial knowledge and style expression. While Large Reasoning Models (LRMs) can be employed to simulate character thought, their direct application is hindered by attention diversion (i. e. , RPAs forget their role) and style drift (i. e. , overly formal and rigid reasoning rather than character-consistent reasoning). To address these challenges, this paper introduces a novel Role-Aware Reasoning (RAR) method, which consists of two important stages: Role Identity Activation (RIA) and Reasoning Style Optimization (RSO). RIA explicitly guides the model with character profiles during reasoning to counteract attention diversion, and then RSO aligns reasoning style with the character and scene via LRM distillation to mitigate style drift. Extensive experiments demonstrate that the proposed RAR significantly enhances the performance of RPAs by effectively addressing attention diversion and style drift.

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TIST Journal 2023 Journal Article

Adversarial Attacks on Deep Reinforcement Learning-based Traffic Signal Control Systems with Colluding Vehicles

  • Ao Qu
  • Yihong Tang
  • Wei Ma

The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-art performance and have great potential for practical applications. In the existing DRL-based ATCS, the controlled signals collect traffic state information from nearby vehicles, and then optimal actions (e.g., switching phases) can be determined based on the collected information. The DRL models fully “trust” that vehicles are sending the true information to the traffic signals, making the ATCS vulnerable to adversarial attacks with falsified information. In view of this, this article first time formulates a novel task in which a group of vehicles can cooperatively send falsified information to “cheat” DRL-based ATCS in order to save their total travel time. To solve the proposed task, we develop CollusionVeh, a generic and effective vehicle-colluding framework composed of a road situation encoder, a vehicle interpreter, and a communication mechanism. We employ our framework to attack established DRL-based ATCS and demonstrate that the total travel time for the colluding vehicles can be significantly reduced with a reasonable number of learning episodes, and the colluding effect will decrease if the number of colluding vehicles increases. Additionally, insights and suggestions for the real-world deployment of DRL-based ATCS are provided. The research outcomes could help improve the reliability and robustness of the ATCS and better protect the smart mobility systems.

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