A new cs.AI paper introduces SKILL-DISCO, which distills and compiles agent traces into reusable procedural skills.
The idea addresses a practical limitation of many agents: they may solve a task once, but the knowledge of how they solved it is not automatically converted into a durable procedure. Skill distillation could make agents improve through experience more systematically.
The paper is relevant to agent platforms that want memory, tool use, and workflow learning to become reusable rather than buried in logs.