@Xudong07452910: This 'Harness Updating Is Not Harness Benefit' is very suitable for those working on Agent Harness. It talks about an easily overlooked problem: updating Harness does not mean you can use it well. Now many Ag…

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Summary

This post discusses a paper, pointing out that in the self-evolution of Agent systems, updating Harness (writing useful updates) and benefiting from updates (actually using them in subsequent tasks) are two different abilities. The latter is key, and weak models often fail to use the rules.

This 'Harness Updating Is Not Harness Benefit' is very suitable for those working on Agent Harness. It addresses an easily overlooked problem: being able to update Harness does not mean you can actually use it well. Now many Agent systems let models modify prompts, skills, memory, and tools based on failure experiences, but the paper breaks this down into two abilities: 1. harness-updating: whether useful updates can be written; 2. harness-benefit: whether these updates can truly be benefited from in subsequent tasks. Counterintuitively, writing updates does not necessarily require the strongest model. The paper finds that the benefit gap between Harness updates written by models of different capability levels is not as large as imagined. What really makes the difference is: whether the executing Agent can find, call, and adhere to these updates over the long term. Weak models often do not lack good rules, but they fail to use them, or they forget along the way. Therefore, the key to Agent self-evolution may not just be 'letting the model learn to modify its own skills,' but letting it truly reap the benefits of these skills in real tasks. In other words, Harness updating is just writing experience into the system; Harness benefit is when experience truly becomes capability. https://arxiv.org/pdf/2605.30621 #AgentHarness #AgenticAI #selfEvolving #claudecode #codex #LLM
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This article Harness Updating Is Not Harness Benefit is well worth reading for anyone working on Agent Harness.

It highlights an easily overlooked problem: being able to update a harness doesn’t mean you actually know how to use it well.

Many current agent systems let a model modify its prompt, skill, memory, or tool based on failure experiences. But this paper breaks that down into two distinct abilities:

  1. harness-updating: whether a model can write useful updates.
  2. harness-benefit: whether those updates actually translate into benefits for downstream tasks.

Counterintuitively, writing updates doesn’t necessarily require the strongest model. The paper finds that the gap in benefit from harness updates written by models at different capability levels is smaller than you might expect.

What really makes the difference is whether the agent executing the task can find, invoke, and consistently follow those updates over time. Weak models often have good rules on paper — they just don’t use them, or they forget them after a while.

So the key to self-evolving agents may not be simply “teaching the model to revise its own skills,” but rather making sure those skills are actually consumed in real tasks and yield tangible benefits.

In other words, Harness updating is about writing experience into the system; Harness benefit is about turning that experience into real capability.

https://arxiv.org/pdf/2605.30621

#AgentHarness #AgenticAI #selfEvolving #claudecode #codex #LLM

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