Papers by Haokun Lin
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing quantization methods are compromising performance of large language models (LLMs) despite their high computational intensity, LLMs are still demanding intensive computation. |
| Approach: | They propose to generate the KV cache of pivot tokens losslessly from the full-precision model. |
| Outcome: | The proposed method generates the KV cache of pivot tokens losslessly from the full-precision model with no extra inference overhead. |
SwarmAgentic: Towards Fully Automated Agentic System Generation via Swarm Intelligence (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing agentic system generation frameworks lack autonomy, autonomy, and functionality . current frameworks are too rigid, limiting adaptability and scalability. |
| Approach: | They propose a framework that fully automates agentic system generation, optimization, and collaboration . they construct agents from scratch and jointly refine functionality and coordination . |
| Outcome: | The proposed framework outperforms ADAS on six real-world, open-ended, and exploratory tasks on the TravelPlanner benchmark. |
EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models (2026.acl-long)
Copied to clipboard
Xingrun Xing, Zheng Liu, Shitao Xiao, Boyan Gao, Yiming Liang, Haokun Lin, Xianlin Zeng, Guoqi Li, Jiajun Zhang
| Challenge: | Large language models (LLMs) driven by scaling laws can be developed in large model sizes. |
| Approach: | They propose a pruning-aware pretraining approach that decouples LLM pruning from direct pretraining. |
| Outcome: | The proposed model outperforms pretraining models with 100M 1B parameters in commen sense benchmarks. |