1 citations · 1 across the 5 of their papers we have counts for
8 papers
Can Agent Harnesses and Inference Engines Hear Each Other? The HEAR Protocol for Agentic LLM Serving
Jiaqi Zhao, Haodong Chen, Jitai Hao +4
LLM agents increasingly execute complex workflows involving multi-turn reasoning, tool use, and parallel agents. Efficient serving requires decisions that span two layers with comp…
SparseEngine: Sparse-First Inference Engine
Jitai Hao, Quansheng Gu, Qiang Huang +1
Long-context LLM agents accumulate interaction histories that strain KV-cache memory and attention computation. Although sparse attention reduces these costs, heterogeneous cache r…
ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding
Jitai Hao, Ke Yang, Qiang Huang +3
Streaming video understanding is a critical capability for real-world applications, including embodied intelligence, autonomous driving, industrial monitoring, surveillance and ear…
ResidualKV: Residual-Based KV Cache Compression for Efficient Long-Context Inference
Jitai Hao, Qiang Huang, Yaowei Wang +2
Efficient long-context inference faces two coupled bottlenecks: KV-cache memory grows linearly with context length, while attention computation grows quadratically. Existing approa…
Uni-X: Mitigating Modality Conflict with a Two-End-Separated Architecture for Unified Multimodal Models
Jitai Hao, Hao Liu, Xinyan Xiao +2
Unified Multimodal Models (UMMs) built on shared autoregressive (AR) transformers are attractive for their architectural simplicity. However, we identify a critical limitation: whe…
A Token is Worth over 1,000 Tokens: Efficient Knowledge Distillation through Low-Rank Clone
Jitai Hao, Qiang Huang, Hao Liu +3
Training high-performing Small Language Models (SLMs) remains costly, even with knowledge distillation and pruning from larger teacher models. Existing work often faces three key c…
MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter
Jitai Hao, WeiWei Sun, Xin Xin +4
Parameter-Efficient Fine-tuning (PEFT) facilitates the fine-tuning of Large Language Models (LLMs) under limited resources. However, the fine-tuning performance with PEFT on comple…
Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning
Yougang Lyu, Jitai Hao, Zihan Wang +6
Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focu…