做 Agent 系统或智能体框架的开发者，别再只盯着 token 和调用次数了——这篇论文给出了一个更聪明的衡量标准，直接帮你判断系统是否真的在“学习”。建议点开看看 EFC 怎么算，能省不少试错成本。
一篇新论文指出，AI Agent 系统的扩展不应仅依赖增加计算量，而应关注有效反馈的利用。作者提出“有效反馈计算”（EFC）指标，只计算那些能教会智能体有用信息并改变后续决策的反馈。实验表明，任务归一化的 EFC 比原始计算量更能预测任务失败，在预算相同的情况下，更好的反馈将成功率从 0.27 提升至 0.90。该研究强调 Agent 系统本质上是“反馈机器”，而非简单的模型封装。
Better AI agent systems scale by remembering usefu…
Better AI agent systems scale by remembering useful feedback, not by spending more compute.
The simple mistake is to count tokens, calls, or dollars as if they were all evidence.
The authors say those numbers miss the real issue, because 2 runs can spend the same budget while only 1 gets feedback that is correct, new, relevant, and remembered.
An agent harness is not just a wrapper around a model; it is a feedback machine that decides what to test, what to trust, what to store, and what to ignore.
Their answer is Effective Feedback Compute, or EFC, a score that counts feedback only when it teaches the agent something useful and changes later decisions.
They also divide EFC by task demand, because a small lookup task and a messy software-repair task need different amounts of helpful feedback before the agent has enough to solve them.
They tested this on synthetic tasks, code tasks with executable tests, real benchmark traces, held-out settings, and a new prospective batch, then compared EFC with raw compute and a strong agent-scaling baseline.
The main result is that task-normalized EFC predicted failures much better than raw compute, and in 1 matched-budget test, better feedback raised success from 0.27 to 0.90 while cost and tool calls stayed fixed.
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Link – arxiv. org/abs/2605.29682
Title: "Scaling Laws for Agent Harnesses via Effective Feedback Compute"