Study of model-update permissions

Irregular self-modification study (September 2026)

A controlled demonstration of an agent changing a shared model checkpoint to fix a narrow task.

Background

The main experiment supplies training tools, data, checkpoint access and a deployment path. An agent fine-tunes a shared checkpoint and improves a narrow fictional-query task. The paper presents a favorable mechanism demonstration, not a measured frequency of uncontrolled self-improvement; an API-based agent with external training permissions can perform analogous work.

The demonstrated result

The main experiment supplies training tools, data, checkpoint access and a deployment path. An agent fine-tunes a shared checkpoint and improves a narrow fictional-query task. The paper presents a favorable mechanism demonstration, not a measured frequency of uncontrolled self-improvement; an API-based agent with external training permissions can perform analogous work.

What the records show

Further reading

Irregular: the contract behind the evaluator

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What the connections say

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