1 citations · 2 across the 13 of their papers we have counts for
7 papers · 1 filter
JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution
Guibin Zhang, Leo Lu, Fangzhou Xie +15
Agent capability is not determined by the model alone. The agent harness, encompassing memory management, planning strategy, action protocol, and tool/skill orchestration, can domi…
OPD-Evolver: Cultivating Holistic Agent Evolver via On-Policy Distillation
Guibin Zhang, Xun Xu, Yanwei Yue +4
Memory has become a standard substrate for self-evolving agents, yet retaining experience is not the same as learning how to evolve through it. Existing memory agents can store tra…
Mem-W: Latent Memory-Native GUI Agents
Guibin Zhang, Yaohui Ling, Fanci Meng +2
GUI agents are beginning to operate the web, mobile, and desktop as interactive worlds, where successful control depends on carrying forward visual, procedural, and task-level evid…
Memory in the Age of AI Agents
Yuyang Hu, Shichun Liu, Yanwei Yue +44
Memory has emerged, and will continue to remain, a core capability of foundation model-based agents. As research on agent memory rapidly expands and attracts unprecedented attentio…
EvoRoute: Experience-Driven Self-Routing LLM Agent Systems
Guibin Zhang, Haiyang Yu, Kaiming Yang +4
Complex agentic AI systems, powered by a coordinated ensemble of Large Language Models (LLMs), tool and memory modules, have demonstrated remarkable capabilities on intricate, mult…
MemEvolve: Meta-Evolution of Agent Memory Systems
Guibin Zhang, Haotian Ren, Chong Zhan +5
Self-evolving memory systems are unprecedentedly reshaping the evolutionary paradigm of large language model (LLM)-based agents. Prior work has predominantly relied on manually eng…
CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
Xiangyuan Xue, Yifan Zhou, Guibin Zhang +7
Self-evolution is a central research topic in enabling large language model (LLM)-based agents to continually improve their capabilities after pretraining. Recent research has witn…