7 papers
Incremental Consistency Execution for Autonomous Intelligent Systems
Cheng Li, Jiexiong Liu, Yixuan Chen +1
Long-horizon autonomous intelligent systems rely on heterogeneous components such as large language models, databases, external APIs, and rule engines, while their external states…
Train What You Deploy:Token-Faithful Post-Training of a Production Coding
Cheng Li, Jiexiong Liu, Yixuan Chen +1
Existing post-training pipelines for coding and terminal agents suffer severe token and control fidelity errors: simplified training environments mismatch production deployments, a…
Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models
Cheng Li, Jiexiong Liu, Yixuan Chen +1
On-device LLM inference faces a trilemma of response latency, limited hardware resources and user privacy. Full cloud inference delivers strong computing power but exposes user pro…
Dynamic Adaptive Shared Experts with Grouped Multi-Head Attention Mixture of Experts
Cheng Li, Jiexiong Liu, Yixuan Chen +1
Transformer models based on the Mixture of Experts (MoE) architecture have made significant progress in long-sequence modeling, but existing models still have shortcomings in compu…
KunLunBaizeRAG: Reinforcement Learning Driven Inference Performance Leap for Large Language Models
Cheng Li, Jiexiong Liu, Yixuan Chen +2
This paper introduces KunLunBaizeRAG, a reinforcement learning-driven reasoning framework designed to enhance the reasoning capabilities of large language models (LLMs) in complex…
Video-VoT-R1: An efficient video inference model integrating image packing and AoE architecture
Cheng Li, Jiexiong Liu, Yixuan Chen +1
In the field of video-language pretraining, existing models face numerous challenges in terms of inference efficiency and multimodal data processing. This paper proposes a KunLunBa…
KunlunBaize: LLM with Multi-Scale Convolution and Multi-Token Prediction Under TransformerX Framework
Cheng Li, Jiexiong Liu, Yixuan Chen +2
Large language models have demonstrated remarkable performance across various tasks, yet they face challenges such as low computational efficiency, gradient vanishing, and difficul…