3 citations · 6 across the 15 of their papers we have counts for
17 papers
From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement
Leijun Zhou, Yanke Yu, Zhihao Liu +10
Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow language agents to retain changes to their p…
CascadeEP: Asynchronous Expert Execution for MoE Prefill under Attention Imbalance
Jin Qin, Tiancheng Hu, Shiyan Wang +12
Mixture-of-experts (MoE) serving commonly deploys data and expert parallelism (DEP): attention replicas run distinct request batches while routed experts are sharded across an expe…
RedKnot-MLA: Multi-Head Offline-Online Reuse for DeepSeek-V4 Long-Context Serving
Yang Liu, Zhaokai Luo, Huayi Jin +10
Multi-head latent attention (MLA) exposes many logical query heads through one packed latent KV stream. This representation is memory efficient, but it removes the physical per-hea…
CoRun: Padding is Simple and Efficient for Deterministic LLM Inference
Shiju Zhao, Jiacheng Yang, Qihang Chen +4
Despite fixed sampling parameters and random seeds, Large Language Model (LLM) inference exhibits output inconsistency, which undermines downstream tasks such as model evaluation a…
GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks
Leijun Zhou, Zhihao Liu, Xiang Qu +9
Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existi…
RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention
Yang Liu, Zhaokai Luo, Huayi Jin +7
As the input length of large language model (LLM) serving continues to grow, the KV cache has become a dominant bottleneck in AI infrastructure. It limits GPU memory capacity, serv…
When Does Recurrence Become an Algorithm? Convergence Selection in Weight-Tied Looped Transformers
Tong Zhang, Junhao Hu, Yun Peng +1
When does a weight-tied looped transformer -- one block applied T times -- implement an actual algorithm? We answer with four findings from controlled populations on group word pro…
HYPIC: Accelerating Hybrid-Attention LLM Serving with Position-Independent Caching
Yifei Liu, Juntong Wu, Yang Liu +4
In retrieval-augmented generation and agentic LLM serving, prompts are assembled from independent segments into long contexts, making the prefill stage dominate per-request cost. T…
Akashic: A Low-Overhead LLM Inference Service with MemAttention
Yang Liu, Zhaokai Luo, Huayi Jin +7
Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows. Replaying the full history for every r…
DynaTrain: Fast Online Parallelism Switching for Elastic LLM Training
Yuanqing Wang, Yuchen Zhang, Hao Lin +9
Modern large language model (LLM) training is inherently dynamic: resource fluctuations, RLHF phase shifts, and cluster elasticity continually reshape the optimal parallelism layou…
Hummingbird: SLO-Oriented GPU Preemption at Microsecond-scale
Tiancheng Hu, Chenxi Wang, Ting Cao +9
Existing GPU-sharing techniques, including spatial and temporal sharing, aim to improve utilization but face challenges in simultaneously ensuring SLO adherence and maximizing effi…
Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage
Junhao Hu, Fangze Li, Mingtao Xu +9
Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference e…
LLMigrate: Transforming "Lazy" Large Language Models into Efficient Source Code Migrators
Yuchen Liu, Junhao Hu, Yingdi Shan +4
Rewriting C code in Rust provides stronger memory safety, yet migrating large codebases such as the 32-million-line Linux kernel remains challenging. While rule-based translators (…
RaaS: Reasoning-Aware Attention Sparsity for Efficient LLM Reasoning
Junhao Hu, Wenrui Huang, Weidong Wang +6
Large Language Models (LLMs) have demonstrated strong capabilities across various domains, with recent advancements in challenging reasoning tasks such as mathematics and programmi…
DeepServe: Serverless Large Language Model Serving at Scale
Junhao Hu, Jiang Xu, Zhixia Liu +18
In this paper, we propose DEEPSERVE, a scalable and serverless AI platform designed to efficiently serve large language models (LLMs) at scale in cloud environments. DEEPSERVE addr…
EPIC: Efficient Position-Independent Caching for Serving Large Language Models
Junhao Hu, Wenrui Huang, Weidong Wang +7
Large Language Models (LLMs) show great capabilities in a wide range of applications, but serving them efficiently becomes increasingly challenging as requests (prompts) become mor…
MemServe: Context Caching for Disaggregated LLM Serving with Elastic Memory Pool
Cunchen Hu, Heyang Huang, Junhao Hu +8
Large language model (LLM) serving has transformed from stateless to stateful systems, utilizing techniques like context caching and disaggregated inference. These optimizations ex…