waytoRSIRecursive self-improvement, examined

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AI research and development

2 articles

Anthropic’s 64%: choosing a next step where humans went off course

A selected set of human detours, a separate control and an agent-designed project help distinguish suggesting a next step from choosing a research goal.

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Mythos Preview’s 64% score comes from 129 deliberately selected human-detour moments. A separate sample of 127 strong human decisions yielded about 20% model preference, without per-model results. Agents also designed every experiment in a human-framed weak-to-strong project, recovering 97% of its defined performance gap; that result did not transfer cleanly to production-scale models. These findings show progress in research suggestions and methods, without demonstrating autonomous successor development.

5 min read1 original source

CRUX: how experiments changed the Personas agent’s claims

An early transfer claim failed its test. Repeating the negative finding across models and comparing against a rival addressed different questions—and left a measurement problem unresolved.

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The Personas agent completed substantial research engineering, but its experiments kept changing what the paper could claim. Following the transfer test, cross-model repetitions and later rival comparison explains why more experimental work did not establish the proposed contribution. The case also preserves a useful narrow finding and the limits of extrapolating to AI research or RSI generally.

5 min read1 original source