70 citations · 71 across the 7 of their papers we have counts for
8 papers
DeepSeek Elastic Compute (DSec): A Sandbox Infrastructure for Effective Agentic Training at Scale
Jialiang Huang, Hongxuan Tang, Jingchang Chen +128
Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools,…
DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression
DeepSeek-AI, :, Anyi Xu +585
The widespread adoption of long-horizon agents has made model workloads increasingly input-heavy. Although prior work has substantially reduced the cost of long-context computation…
DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation
Xin Cheng, Xingkai Yu, Chenze Shao +30
Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification. While recent parallel drafters efficiently propose lo…
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
DeepSeek-AI, Anyi Xu, Bangcai Lin +315
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSe…
How Would Oblivious Memory Boost Graph Analytics on Trusted Processors?
Jiping Yu, Xiaowei Zhu, Kun Chen +4
Trusted processors provide a way to perform joint computations while preserving data privacy. To overcome the performance degradation caused by data-oblivious algorithms to prevent…
GORAM: Graph-oriented ORAM for Efficient Ego-centric Queries on Federated Graphs
Xiaoyu Fan, Kun Chen, Jiping Yu +4
Ego-centric queries, focusing on a target vertex and its direct neighbors, are essential for various applications. Enabling such queries on graphs owned by mutually distrustful dat…
DFOGraph: An I/O- and Communication-Efficient System for Distributed Fully-out-of-Core Graph Processing
Jiping Yu, Wei Qin, Xiaowei Zhu +4
With the magnitude of graph-structured data continually increasing, graph processing systems that can scale-out and scale-up are needed to handle extreme-scale datasets. While exis…
LiveGraph: A Transactional Graph Storage System with Purely Sequential Adjacency List Scans
Xiaowei Zhu, Guanyu Feng, Marco Serafini +5
The specific characteristics of graph workloads make it hard to design a one-size-fits-all graph storage system. Systems that support transactional updates use data structures with…