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This paper redefines knowledge transfer for agentic systems by shifting focus from simple model imitation to the persistent transfer of task-solving capabilities across models, artifacts, and execution harnesses. By establishing a framework that evaluates how retained knowledge contributes causally to deployed utility, it lays the groundwork for building more reliable and maintainable complex agents. This work addresses a critical bottleneck in advancing autonomous AI systems beyond static parameters.
Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses
arXiv:2609.36630v1 Announce Type: new Abstract: Modern agents increasingly rely on memories, tools, and execution logic, so their competence extends beyond model parameters. This shift exposes a limitation of conventional knowledge distillation, which asks how a student model imitates a teacher model. We define Agent Distillation as the persistent transfer of task-solving knowledge from a teacher agent to a student agent. Our study organizes the field by where transferred knowledge is retained: within the model, as artifacts, through the execution harness, or across substrates. This perspective separates transfer evidence from its outcome and clarifies how knowledge moves between agent components. We develop an evaluation framework that relates retention to causal contribution and deployed utility. Together, these contributions establish a foundation for the reliable, maintainable, and safe development of increasingly complex agentic systems.
Authors:Ziluowen Luo, Senzhang Wang, Chaozhuo Li, Jun Yin, Hao Yan, Ming Cheng, Chenxu Wang, Songyang Liu, Litian Zhang, Qiwei Ye, Zheng Liu, Philip S. Yu
Abstract:Modern agents increasingly rely on memories, tools, and execution logic, so their competence extends beyond model parameters. This shift exposes a limitation of conventional knowledge distillation, which asks how a student model imitates a teacher model. We define Agent Distillation as the persistent transfer of task-solving knowledge from a teacher agent to a student agent. Our study organizes the field by where transferred knowledge is retained: within the model, as artifacts, through the execution harness, or across substrates. This perspective separates transfer evidence from its outcome and clarifies how knowledge moves between agent components. We develop an evaluation framework that relates retention to causal contribution and deployed utility. Together, these contributions establish a foundation for the reliable, maintainable, and safe development of increasingly complex agentic systems.
Artificial Intelligence (cs.AI)
arXiv:2609.36630 [cs.AI]
(or arXiv:2609.36630v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.36630 arXiv-issued DOI via DataCite (pending registration)
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