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AI safety

2 open-access journal articles on AI safety, published in 2026 in Life of Research. Every record is free to read and download, with a permanent link and a ready-made citation.

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Journal article

The Evaluator Is the Bottleneck: What a Self-Modifying System's Acceptance Test Must Satisfy Before Its Verdict Can Be Trusted, Why Co-Evolving Evaluators Shrink the External Anchor Rather Than Remove It, and How Moving the Anchor Outside Moves the Attack Surface With It

Self-modifying AI systems now decide which changes to their own code to keep by running an acceptance test and retaining whatever scores higher. Published coding-agent loops report large benchmark gains this way, and their safety case rests on sandboxes, archives of past versions and the option to…

Cite Related research LOR-00083 · 10 views · 21 downloads
PDF460.1 KB

Journal article

Three Guarantees Under One Word: What Importance-Based Selective Forgetting in Language Models Can and Cannot Promise

Unlearning methods are asked to deliver a guarantee, and the word is used for three different ones: that a model's outputs no longer reveal the target under some stated class of queries, that the target is absent from the weights, and that no adversary within a stated budget can restore it. This…

Cite Related research LOR-00038 · 6 views · 9 downloads
PDF483.5 KB

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