Papers by Siyuan Feng
SciPedia: Unlocking the Value of Scientific Data for Pre-training (2026.acl-long)
Copied to clipboard
| Challenge: | High-quality scientific data is critical for advancing LLMs, yet academic literature remains underutilized. |
| Approach: | They construct a large-scale raw scientific corpus but identify a critical Learnability Gap . they develop a multi-stage pipeline featuring content cleaning and pedagogical augmentation . |
| Outcome: | The proposed approach boosts average performance by +2.12 (3B) and +2.95 (7B) on in-domain tasks. |
Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)
Copied to clipboard
Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao
| Challenge: | Structural pruning is a promising solution for large language models . prior structured pruning methods remove unimportant parameters based on certain metrics . |
| Approach: | They propose a structural pruning method that iteratively learns the weights of transformer layers by adding their l1-norm to the loss function. |
| Outcome: | The proposed pruning method outperforms strong layer-wise pruning methods without requiring retraining. |
Self-supervised Semantic-driven Phoneme Discovery for Zero-resource Speech Recognition (2022.acl-long)
Copied to clipboard
| Challenge: | a new approach for self-supervised speech representation learning is proposed . a phoneme inventory learning model is based on a discrete representation of speech . |
| Approach: | They propose a neural discrete representation learning model for self-supervised phoneme inventory learning with raw speech and word labels. |
| Outcome: | The proposed model learns better phoneme-level representations and lowers error rates on TIMIT and Mboshi benchmarks than previous state-of-the-art models. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
Copied to clipboard
Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybrid Reasoning Models via Reinforcement Learning (2026.acl-long)
Copied to clipboard
Siyuan Gan, Jiaheng Liu, Boyan Wang, Tianpei Yang, Runqing Miao, Yuyao Zhang, Fanyu Meng, Junlan Feng, Linjian Meng, Jing Huo, Yang Gao
| Challenge: | Existing work on large reasoning models (LRMs) focuses on using reinforcement learning (RL) to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query. |
| Approach: | They propose to use reinforcement learning to train hybrid reasoning models that automatically decide whether to engage in thinking or not based on the complexity of the query. |
| Outcome: | The proposed model reduces token usage by around 50%$ compared to DeepSeek-R1-Distill-Qwen-1.5B/7B and DeepScaleR-1.5b, while significantly improving accuracy. |