这篇论文戳破了“越大越好”的直觉，做智能体自我进化的团队会发现，把预算花在求解器上比花在进化器上更划算，值得重新审视你的架构设计。
一篇新论文挑战了“把最强模型放在进化器位置”的常见做法，指出自我进化智能体的关键在于更好的求解器，而非更大的更新编写模型。研究将“编写有用更新”和“在任务执行中受益于更新”这两个通常混为一谈的任务分开。实验发现，较小的Qwen3.5-9B模型作为进化器，其更新效果与Claude Opus 4.6相当；而昂贵模型更适合作为实际求解任务的智能体。中等规模模型是甜点：既能有效调用和遵循新程序，又不会因自身能力过强而让更新失去提升空间。
Better self-improving agents need better solvers, …
Better self-improving agents need better solvers, not bigger update-writing models.
This challenges the common habit of putting the strongest model in the evolver seat.
The usual intuition was: put the strongest model in the evolver seat, because a better model should write better prompts, memories, tools, and skills.
This paper cuts that intuition in half.
It separates two jobs that are usually blurred together: writing useful harness updates, and benefiting from those updates during task execution.
The paper says the cheaper model can often write good enough prompt, memory, or skill updates. So a small Qwen3.5-9B evolver can create updates that help about as much as Claude Opus 4.6.
The expensive model is more useful as the agent that actually solves the task with those updates.
i.e. using the updates is very model-dependent, because weak models often fail to load the right skill or load it and then stop following it during a long task.
Strong models can use the harness, but they may already be close enough to their ceiling that the update has less room to help.
The sweet spot is the mid-tier model: capable enough to invoke and follow the new procedure, but not so capable that the harness has nothing left to teach.
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Link – arxiv. org/abs/2605.30621
Title: "Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents"