Papers by Bingxuan Li
PEARL: Self-Evolving Assistant for Time Management with Reinforcement Learning (2026.acl-long)
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| Challenge: | Scheduling logistics associated with meetings can amount to hours each week . a lack of automation can cause a lot of errors and can be costly . |
| Approach: | They propose a benchmark for long-horizon calendar conflict resolution that automates calendar conflict management . they propose 'pearl' that augments the language agent with an external preference memory . |
| Outcome: | a new benchmark for long-horizon calendar conflict resolution shows that existing agents perform poorly with high error rates. |
Control Large Language Models via Divide and Conquer (2024.emnlp-main)
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| Challenge: | Lexically Constrained Generation (LCG) is a crucial task of text generation. |
| Approach: | They propose a Divide and Conquer Generation strategy to enhance LLMs' performance in Lexically Constrained Generation with prompt-based controlling. |
| Outcome: | The proposed strategy shows 90% improvement on the most challenging LCG task. |
REFFLY: Melody-Constrained Lyrics Editing Model (2025.naacl-long)
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| Challenge: | Automatic melody-to-lyric (M2L) generation aims to create lyrics that align with a given melody. |
| Approach: | They propose a framework for automatic melody-to-lyric generation that allows for a more flexible approach to creating lyrics from plain text. |
| Outcome: | The proposed framework outperforms baselines Lyra and GPT-4 in musicality and text quality. |
Current Agents Fail to Leverage World Model as Tool for Foresight (2026.acl-long)
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Cheng Qian, Emre Can Acikgoz, Bingxuan Li, Xiusi Chen, Yuji Zhang, Bingxiang He, Qinyu Luo, Gokhan Tur, Dilek Hakkani-Tür, Yunzhu Li, Heng Ji
| Challenge: | Generative world models could be used to enhance agents' cognition . agents are expected to operate in settings where tasks unfold over long horizons and involve intricate chains of interdependent decisions. |
| Approach: | They propose to use vision-language models as external simulators to enhance cognition . they find that agents rarely invoke simulation and misuse predicted rollouts . |
| Outcome: | The proposed model could be used to predict future states rather than short-horizon reasoning . the model could also be used for real-world planning and robotics . |
METAL: A Multi-Agent Framework for Chart Generation with Test-Time Scaling (2025.acl-long)
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| Challenge: | Chart generation requires strong visual design skills and precise coding capabilities that embed the desired visual properties into code. |
| Approach: | They propose a vision-language model-based multi-agent framework for effective automatic chart generation. |
| Outcome: | The proposed framework achieves a 5.2% improvement in the F1 score over the current best chart generation task. |
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models (2026.acl-long)
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Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) scales capacity via conditional computation, but lacks knowledge lookup primitive. |
| Approach: | They propose a conditional memory instantiated via Deep Sparse Embedding (DSE) they propose 'u-shaped scaling law' that identifies optimal balance between MoE experts and DSE memory . |
| Outcome: | The proposed model outperforms an iso-parameter and isoFLOPs MoE baseline across knowledge and reasoning benchmarks and is infrastructure-efficient. |
Bold Claims or Self-Doubt? Factuality Hallucination Type Detection via Belief State (2025.findings-emnlp)
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| Challenge: | Existing studies focus on detecting the presence of hallucinations but lack a systematic classification approach, which hinders deeper exploration of their characteristics. |
| Approach: | They propose a method to categorize hallucinations into two types: Overconfident and Unaware . |
| Outcome: | The proposed method categorizes factuality hallucination into two types: Overconfident and Unaware Hallucinations. |