12 citations · 13 across the 12 of their papers we have counts for
15 papers
COPE: Composable Performance Emulation for Mixture-of-Experts Offloading Policies
Hanming Zhang, Erhu Feng
As LLMs scale, mixture-of-experts (MoE) architectures increase model capacity while keeping per-token computation manageable through sparse expert activation. This sparsity also en…
Reuse, Rewrite, or Reject: Evidence-Constrained Trace Reuse for Repeated Mobile GUI Control
Bingyu Bai, Cheng Zhang, Erhu Feng
Mobile GUI agents frequently perform repetitive tasks with similar interaction procedures. Reusing previous executions can reduce redundant LLM inference and execution overhead, bu…
FastEEP: Fast Intra-Instance Elastic Scaling for Expert-Parallel MoE Serving
Yunfei Feng, Jingkai He, Shiqi Liu +3
Large-scale expert parallelism (EP) has become the standard deployment configuration for Mixture-of-Experts (MoE) models in production. Meanwhile, the highly dynamic nature of LLM…
When Answers Stray from Questions: Hallucination Detection via Question-Answer Orthogonal Decomposition
Siyang Yao, Erhu Feng, Yubin Xia
Hallucination detection in large language models (LLMs) requires balancing accu racy, efficiency, and robustness to distribution shift. Black-box consistency methods are effective…
SkVM: Revisiting Language VM for Skills across Heterogenous LLMs and Harnesses
Le Chen, Erhu Feng, Yubin Xia +1
LLM agents increasingly adopt skills as a reusable unit of composition. While skills are shared across diverse agent platforms, current systems treat them as raw context, causing t…
MobiFlow: Real-World Mobile Agent Benchmarking through Trajectory Fusion
Yunfei Feng, Xi Zhao, Cheng Zhang +5
Mobile agents can autonomously complete user-assigned tasks through GUI interactions. However, existing mainstream evaluation benchmarks, such as AndroidWorld, operate by connectin…
Beyond Training: Enabling Self-Evolution of Agents with MOBIMEM
Zibin Liu, Cheng Zhang, Xi Zhao +6
Large Language Model (LLM) agents are increasingly deployed to automate complex workflows in mobile and desktop environments. However, current model-centric agent architectures str…
Systematic Exploration of Multi-core Architectures for Efficient LLM Serving using WaferAI-SIM
Tianhao Zhu, Dahu Feng, Erhu Feng +1
With the widespread adoption of Large Language Models (LLMs), the demand for high-performance LLM inference services continues to grow. Multi-core AI accelerators, such as Groq, Gr…
MobiAgent: A Systematic Framework for Customizable Mobile Agents
Cheng Zhang, Erhu Feng, Xi Zhao +7
With the rapid advancement of Vision-Language Models (VLMs), GUI-based mobile agents have emerged as a key development direction for intelligent mobile systems. However, existing a…
History Rhymes: Accelerating LLM Reinforcement Learning with RhymeRL
Jingkai He, Tianjian Li, Erhu Feng +5
With the rapid advancement of large language models (LLMs), reinforcement learning (RL) has emerged as a pivotal methodology for enhancing the reasoning capabilities of LLMs. Unlik…
Leveraging OS-Level Primitives for Robotic Action Management
Wenxin Zheng, Boyang Li, Bin Xu +3
End-to-end imitation learning frameworks (e.g., VLA) are increasingly prominent in robotics, as they enable rapid task transfer by learning directly from perception to control, eli…
Topology-Aware Virtualization over Inter-Core Connected Neural Processing Units
Dahu Feng, Erhu Feng, Dong Du +4
With the rapid development of artificial intelligence (AI) applications, an emerging class of AI accelerators, termed Inter-core Connected Neural Processing Units (NPU), has been a…
Get Experience from Practice: LLM Agents with Record & Replay
Erhu Feng, Wenbo Zhou, Zibin Liu +8
AI agents, empowered by Large Language Models (LLMs) and communication protocols such as MCP and A2A, have rapidly evolved from simple chatbots to autonomous entities capable of ex…
Characterizing Mobile SoC for Accelerating Heterogeneous LLM Inference
Le Chen, Dahu Feng, Erhu Feng +5
With the rapid advancement of artificial intelligence technologies such as ChatGPT, AI agents, and video generation, contemporary mobile systems have begun integrating these AI cap…
TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing
Husheng Han, Xinyao Zheng, Yuanbo Wen +11
Heterogeneous collaborative computing with NPU and CPU has received widespread attention due to its substantial performance benefits. To ensure data confidentiality and integrity d…