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cs.AI2026
ConMoE: Expert-Pool Consolidation via Prototype Reassignment for MoE Compression
Yilun Yao, Jiaming Pan, Elsie Dai +3
Mixture-of-Experts (MoE) language models reduce per-token computation but still require storing and serving all experts, making deployment memory-intensive. Existing post-training…
cs.AI2026
MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use
Wenrui Liu, Zixiang Liu, Elsie Dai +5
Large Language Models (LLMs) are increasingly serving as autonomous agents, and their utilization of external tools via the Model Context Protocol (MCP) is considered a future tren…
cs.AI2026
ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents
Yilun Yao, Shan Huang, Elsie Dai +5
Large language models are increasingly deployed as research agents for deep search and long-horizon information seeking, yet their performance often degrades as interaction histori…