Research author
Kang Liu
16 AI research papers in the One9Founders library, with summaries and links to original sources.
Papers by Kang Liu
Representational Atoms in Neural Networks
Chenhui Hu, Pengfei Cao, Kang Liu, et al.
SAEScientist-Bench: Can AI Agents Conduct Autonomous SAE Interpretability Research?
Yuqiao Tan, Shizhu He, Jun Zhao, et al.
PaDoc: Layout-Grounded Parallel Decoding for Document Parsing
Hao Yu, Jiabo Zhan, Kang Liu, et al.
SWE-Touch: Benchmarking Coding Agents When Users Touch the Code
Yuqiao Tan, Jinxiang Meng, Fangyu Lei, et al.
The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation
Tianyi Men, Zhuoran Jin, Kang Liu, et al.
AREX: Towards a Recursively Self-Improving Agent for Deep Research
Shuqi Lu, Chaofan Li, Kun Luo, et al.
MemTools: A Unified Research Framework for Interoperable Agent Memory
Chengfeng Zhao, Jinhui Chen, Sirui Liang, et al.
LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
Chenhao Yuan, Yinhao Xu, Shuwen Xu, et al.
Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning
Tianyi Men, Zhuoran Jin, Pengfei Cao, et al.
Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It
Yupu Hao, Zhuoran Jin, Huanxuan Liao, et al.
Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
Jiachun Li, Zhuoran Jin, Tianyi Men, et al.
Pushing the Limits of LLM Tool Calling via Experiential Knowledge Integration and Activation
Yupu Hao, Zhuoran Jin, Huanxuan Liao, et al.
SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models
Shiqiang Cai, Nianhong Niu, Shizhu He, et al.
DV-World: Benchmarking Data Visualization Agents in Real-World Scenarios
Jinxiang Meng, Shaoping Huang, Fangyu Lei, et al.
SOC-ICNN: From Polyhedral to Conic Geometry for Learning Convex Surrogate Functions
Kang Liu, Jianchen Hu
From $P(y|x)$ to $P(y)$: Investigating Reinforcement Learning in Pre-train Space
Yuqiao Tan, Minzheng Wang, Bo Liu, et al.