activity
20182026
most citedDeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale

56 citations · 137 across the 31 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.DC2026

DIALER: A Case for Improving Rare-Class Accuracy in Retraining-Free Edge Video Analytics

Dongyoon Ryu, Sungho Jeon, Xinyue Ma +4

Edge video analytics with lightweight models is prone to accuracy degradation due to persistent distributional shifts in live video streams. While continuous learning (CL) addresse…

cs.CV2026

Crafter: A Multi-Agent Harness for Editable Scientific Figure Generation from Diverse Inputs

Haozhe Zhao, Shuzheng Si, Zhenhailong Wang +6

Scientific figures are among the most effective means of communicating complex research ideas, yet producing publication-quality illustrations remains one of the most labor-intensi…

cs.AI2026

Spreadsheet-RL: Advancing Large Language Model Agents on Realistic Spreadsheet Tasks via Reinforcement Learning

Banghao Chi, Yining Xie, Mingyuan Wu +9

Spreadsheet systems (e.g., Microsoft Excel, Google Sheets) play a central role in modern data-centric workflows. As AI agents grow increasingly capable of automating complex tasks,…

cs.AI2026

Trustworthy Agent Network: Trust in Agent Networks Must Be Baked In, Not Bolted On

Yixiang Yao, Yuhang Yao, Xinyi Fan +5

The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents transition from isolated op…

cs.AI2026

From Context to Skills: Can Language Models Learn from Context Skillfully?

Shuzheng Si, Haozhe Zhao, Yu Lei +10

Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learning, where LMs directly lear…

cs.DC2026

SuperInfer: SLO-Aware Rotary Scheduling and Memory Management for LLM Inference on Superchips

Jiahuan Yu, Mingtao Hu, Zichao Lin +1

Large Language Model (LLM) serving faces a fundamental tension between stringent latency Service Level Objectives (SLOs) and limited GPU memory capacity. When high request rates ex…