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Subject: AI safety (remove filter)

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

Pranay Mahendrakar 2026

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

Cite Related research LOR-00083 · 10 views · 20 downloads Select “The Evaluator Is the Bottleneck: What a Self-Modifying…” for export

PDF460.1 KB

Journal article

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

Pranay M. Mahendrakar 2026

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. 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…

Cite Related research LOR-00038 · 5 views · 7 downloads Select “Three Guarantees Under One Word: What Importance-Based…” for export

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