Papers by Lingjie Chen
Reasoning Traces Shape Outputs but Models Won’t Say So (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate explicit reasoning traces before producing answers, offering a window into their decisionmaking. |
| Approach: | They propose a method that injects synthetic reasoning snippets into a model’s reasoning trace and measures whether the model follows the injected reasoning and acknowledges doing so. |
| Outcome: | The proposed method reveals that models refuse to disclose their influence when asked to explain their changed answers. |
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems (2025.findings-emnlp)
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Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen
| Challenge: | Existing methods for fine-tuning agents are often inadequate . a multi-agent system can solve complex tasks by dividing responsibilities among specialized agents . |
| Approach: | a new framework is proposed to improve agents collaboration through iterative alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on held-in and held-out tasks. |
dLLM: Simple Diffusion Language Modeling (2026.acl-demo)
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| Challenge: | diffusion language models (DLMs) are evolving rapidly but many lack transparent implementations or are scattered across codebases. |
| Approach: | They propose an open-source framework that unifies diffusion language modeling components while remaining flexible enough to support new methods and architectures. |
| Outcome: | dLLM unifies the core components of diffusion language modeling and makes them easy to customize for new designs. |
No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning (2026.acl-long)
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Zhicong Li, Lingjie Jiang, Yulan Hu, Xingchen Zeng, Yixia Li, Xiangwen Zhang, Guanhua Chen, Zheng Pan, Xin Li, Yong Liu
| Challenge: | Current methods for training Large Language Model agents rely on static or offline critic models, which fail to adapt as the policy evolves. |
| Approach: | They propose a framework that integrates a critique and a policy to optimize the policy and critic through a synchronized co-evolutionary loop. |
| Outcome: | The proposed framework yields more stable training and higher long-horizon task success across open-world environments. |
VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)
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Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei
| Challenge: | Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision. |
| Approach: | They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone. |
| Outcome: | The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data. |
PAPERMIND: Benchmarking Agentic Reasoning and Critique over Scientific Papers in Multimodal LLMs (2026.findings-acl)
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Yanjun Zhao, Tianxin Wei, Jiaru Zou, Xuying Ning, Yuanchen Bei, Lingjie Chen, Simmi Rana, Wendy H. Yang, Hanghang Tong, Jingrui He
| Challenge: | Existing benchmarks assess integrated and agent-oriented scientific reasoning in isolation . Existing systems assess integrated reasoning in isolated tasks . |
| Approach: | They propose a benchmark to evaluate integrated and agent-oriented scientific reasoning over research papers. |
| Outcome: | The proposed benchmark evaluates integrated and agent-oriented scientific reasoning over scientific papers. |
“A good pun is its own reword”: Can Large Language Models Understand Puns? (2024.emnlp-main)
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| Challenge: | Existing studies on the understanding of puns in large language models (LLMs) have not explored the use of pun in creative writing and humor creation. |
| Approach: | They propose to use pun recognition, explanation and generation tasks to evaluate the capabilities of large language models (LLMs) they adopt automated evaluation metrics from prior research and introduce new evaluation methods and metrics that align more closely with human cognition. |
| Outcome: | The proposed methods align more closely with human cognition than previous evaluation metrics. |