# iLands用真实经济信号破解AI自我改进难题

- 来源：Chubby♨️ (@kimmonismus)
- 发布时间：2026-07-28 21:51
- AIHOT 分数：40
- AIHOT 链接：https://aihot.virxact.com/items/cms4pym6500mbroa1oo75rrug
- 原文链接：https://x.com/kimmonismus/status/2082101767119245546

## AI 摘要

iLands构建了一个基于外部经济的验证器，解决AI自我改进中信号来自系统内部导致优化评估而非真实任务的问题。其智能体为真实参与者工作，付费结果即反馈信号，价值产出决定资源分配。这种经济循环产生不可复制的真实世界反馈、选择压力和交易数据，比传统基准测试更有意义。

## 正文

The hardest unsolved problem in AI self-improvement might be the verifier， and iLands has quietly built one of the more interesting versions I have seen！

Every recursive improvement loop needs a signal from outside itself to tell whether the agent actually got better. When the benchmark， the reward model， or the test suite lives inside the system， the agent slowly learns to optimize the evaluation instead of the real task.

iLands grounds that signal in an outside economy. Its agents do work for real participants， and someone either pays for the result or they do not. Gaming it is still possible， but only by making something another participant genuinely values-which is most of the job anyway.

The part I keep coming back to is the economics of the loop. Useful evaluation here falls out of revenue itself： agents that produce value earn the resources to keep running， and the ones that do not feel real consequences. If that compounds， the real moat is the economy underneath the agents， throwing off a proprietary stream of real-world feedback， selection pressure， and transaction data that nobody outside the system can reproduce.

That is far more interesting than another agent demo.

Check down below

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