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cs.AI2026
LLM-as-Code: Agentic Programming for Agent Harness
Junjia Qi, Zichuan Fu, Jingtong Gao +4
Every major LLM agent framework gives the LLM the role of orchestrator; the model decides what to do next, when to call tools, and when to stop. We argue that token explosion, cont…
cs.AI2026
Tandem: Riding Together with Large and Small Language Models for Efficient Reasoning
Zichuan Fu, Xian Wu, Guojing Li +7
Recent advancements in large language models (LLMs) have catalyzed the rise of reasoning-intensive inference paradigms, where models perform explicit step-by-step reasoning before…
cs.AI2025
Model Merging for Knowledge Editing
Zichuan Fu, Xian Wu, Guojing Li +6
Large Language Models (LLMs) require continuous updates to maintain accurate and current knowledge as the world evolves. While existing knowledge editing approaches offer various s…