2 citations · 2 across the 6 of their papers we have counts for
6 papers
PithTrain: A Compact and Agent-Native MoE Training System
Ruihang Lai, Hao Kang, Haozhan Tang +6
Mixture-of-Experts (MoE) has become the dominant architecture for frontier language models. To meet this demand, production frameworks have built optimized MoE training stacks over…
Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
Jingjie Ning, Xiaochuan Li, Ji Zeng +2
We study auto research as a closed empirical loop driven by external measurement. Each submitted trial carries a hypothesis, an executable code edit, an evaluator-owned outcome, an…
Evaluating Test-Time Scaling of General LLM Agents
Xiaochuan Li, Ryan Ming, Pranav Setlur +6
LLM agents are increasingly expected to operate as general-purpose systems that resolve real-world user requests, yet their dynamic scaling behavior in realistic environments remai…
FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Language Models
Hao Kang, Zichun Yu, Chenyan Xiong
Recent large language models such as Gemini-1.5, DeepSeek-V3, and Llama-4 increasingly adopt Mixture-of-Experts (MoE) architectures, which offer strong efficiency-performance trade…
Interpret and Control Dense Retrieval with Sparse Latent Features
Hao Kang, Tevin Wang, Chenyan Xiong
Dense embeddings deliver strong retrieval performance but often lack interpretability and controllability. This paper introduces a novel approach using sparse autoencoders (SAE) to…
ResearchArena: Benchmarking Large Language Models' Ability to Collect and Organize Information as Research Agents
Hao Kang, Chenyan Xiong
Large language models (LLMs) excel across many natural language processing tasks but face challenges in domain-specific, analytical tasks such as conducting research surveys. This…