Papers by Yike He
Taming System Complexity: Demystifying Software Engineering Agents in Diagnosing Linux Kernel Faults (2026.acl-long)
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| Challenge: | Existing LLM agents struggle with identifying bugs in the Linux kernel . bugs can affect billions of users, affecting the Linux Foundation's research on the topic . |
| Approach: | They propose a LinuxFLBench benchmark to measure the accuracy of LLM agents on the Linux kernel. |
| Outcome: | The proposed framework improves FL accuracy with minimal costs. |
LeanK: Learnable K Cache Channel Pruning for Efficient Decoding (2025.emnlp-main)
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| Challenge: | Existing efforts to optimize the key-value (KV) cache include: (1) Eviction, which discards cache of less important tokens; (2) Selection, which retains the full KV cache but selectively reads relevant entries. |
| Approach: | They propose a learning-based method that prunes unimportant key (K) cache channels by leveraging static channel sparsity. |
| Outcome: | Experiments show that LeanK reduces GPU memory and accelerates decoding without sacrificing accuracy. |
Accelerating Prefilling via Decoding-time Contribution Sparsity (2026.findings-acl)
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| Challenge: | Existing acceleration methods exploit attention score sparsity by estimating blocks with high attention scores and applying dynamic sparse attention. |
| Approach: | They propose a method which replaces dense attention with Triangle attention in a subset of layers to reduce the time needed to decode. |
| Outcome: | Experiments show that TriangleMix achieves near-lossless performance on long-context and long-constrast reasoning benchmarks while significantly improving efficiency. |
Tree-CoT-RT: An Explainable Multi-Path Tree-Guided Chain-of-Thought and Reinforcement Learning Framework for Aspect Sentiment Quad Prediction (2026.findings-acl)
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Hao Zhang, Jiahao Wang, Zhenke Duan, Xin Yin, Haichuan Hu, Hualong Chen, null Suyi, Congqing He, Yike Tan, Yu-N Cheah
| Challenge: | Existing methods lack explainability and generalization, making it difficult to justify inference decisions and detect implicit sentiment across domains and varied expression patterns. |
| Approach: | They propose an explainable multi-path tree-guided chain-of-thought framework specifically designed for ASQP. |
| Outcome: | Experiments on benchmark datasets show that Tree-CoT-RT outperforms baselines. |