# Task-CoEvolve：用自适应测试选择优化AI智能体评测成本

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-08-23 05:10
- AIHOT 分数：27
- AIHOT 链接：https://aihot.virxact.com/items/cmt4vr8861w4hro6ty1smh045
- 原文链接：https://x.com/rohanpaul_ai/status/2091271715422912844

## AI 摘要

东京大学新论文指出，多数AI智能体评测测试浪费资金——所有版本都能通过或都无法通过的任务占测试集超70%，却与其他测试成本相同。新方法Task-CoEvolve仅保留过往版本有分歧的测试，每轮重新选取，并调整评分以跨轮比较。在Terminal-Bench 2.1上，仅用89项任务中20%进行评测，结果与全量评测相差约一项任务，成本降低67%-80%，时间减半。

## 正文

Most of the tests used to grade an AI agent are a waste of money.

A new University of Tokyo paper counts them. Tasks every version passes, plus tasks no version can pass, are over 70% of the test set.

They cost the same as any other test and tell you nothing.

So the method, Task-CoEvolve, keeps only the tests where past versions disagreed, and picks a fresh set each round, because a test that was hard last round may be easy now.

It then adjusts the score for how the tests were picked, so rounds using different tests still compare.

On Terminal-Bench 2.1, grading each version on 20% of the 89 tasks ends within about one task of grading all of them.

Cost drops 67 to 80%. Time drops by half.

Picking the same number of tests at random is cheaper still, but random picking grabs the short easy ones, and lands 3.3 points worse.

Cheap and useful are not the same thing.

Before you shrink an eval loop, ask whether what's left can still tell your candidates apart.

– arxiv. org/abs/2608.20169

Title: "Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection"