Papers by Chenxu Liu
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage (2026.findings-acl)
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Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie
| Challenge: | Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. |
| Approach: | They propose an algorithm that detects threshold where information loss exceeds information gain during sparse decoding to reduce token consumption by up to 90% and a marginal accuracy degradation of less than 2%. |
| Outcome: | The proposed algorithm reduces token consumption by 90% with a marginal accuracy degradation of less than 2% across reasoning-intensive benchmarks. |
Sibyl: Empowering Empathetic Dialogue Generation in Large Language Models via Sensible and Visionary Commonsense Inference (2025.coling-main)
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Lanrui Wang, Jiangnan Li, Chenxu Yang, Zheng Lin, Hongyin Tang, Huan Liu, Yanan Cao, Jingang Wang, Weiping Wang
| Challenge: | Recent studies have focused on integrating commonsense knowledge into chatbots to enhance their ability to understand and generate dialogue responses. |
| Approach: | They propose a framework that integrates commonsense knowledge into chatbots to enable them to elicit more empathetic responses. |
| Outcome: | The proposed framework enables LLMs to uncover the implicit requirements of the conversation, aiming to elicit more empathetic responses. |
Can We Steer Reasoning Direction by Thinking Intervention? (2025.findings-emnlp)
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| Challenge: | Large Reason Models suffer from overthinking and erroneous reasoning problems due to the lack of fine-grained control over their reasoning behaviors. |
| Approach: | They propose a paradigm to enable fine-grained control over LRMs’ reasoning behaviors by aligning reasoning trajectories with specific cognitive patterns. |
| Outcome: | The proposed paradigm achieves integration intervention throughout model reasoning processes. |
Towards Objectively Benchmarking Social Intelligence of Language Agents at the Action Level (2024.findings-acl)
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| Challenge: | evaluative findings highlight that the STSS benchmark is challenging for state-of-the-art language agents. |
| Approach: | They propose a social task in sandbox simulation benchmark that assesses language agents objectively at the action level by scrutinizing goal achievements within the multi-agent simulation. |
| Outcome: | The proposed social task-in-sandbox simulation is a language-level benchmark . the proposed benchmark effectively discriminates between distinct language agents . |
VecInfer: Efficient LLM Inference with Low-Bit KV Cache via Outlier-Suppressed Vector Quantization (2026.acl-long)
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| Challenge: | Existing quantization methods for large language models suffer performance degradation at ultra-low bit-widths due to key cache outliers. |
| Approach: | They propose a vector quantization method that suppresses outliers in the key cache and reduces memory access overhead. |
| Outcome: | The proposed method outperforms baseline quantization methods across long-context understanding and mathematical reasoning tasks while minimizing memory access overhead. |
WebCoderBench: Benchmarking Web Application Generation with Comprehensive and Interpretable Evaluation Metrics (2026.acl-long)
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| Challenge: | Web applications (web apps) are a key arena for large language models to demonstrate their code generation capabilities and commercial potential. |
| Approach: | a new benchmark for large language models (LLMs) is designed to provide real-world user requirements and generalizable evaluation metrics. |
| Outcome: | a new benchmark for large language models (LLMs) provides a real-world, generalizable, and interpretable evaluation score . the benchmark measures user requirements, expression styles and human-preference-aligned weights . a web application can be used to demonstrate its commercial potential, authors say . |