Competitive AI Safety is the loss function to make sure AI goes well

A LessWrong post published on Thursday proposes a new organizational framework for AI safety research: treat it like a competition, with shared measurable goals — similar to OpenAI's recent Parameter Golf challenge. The author, Patrick, argues that current safety work is diffuse, scattered across many researchers working on techniques that rarely build on each other. A competitive model with a public leaderboard could change that. His concrete example is seq2feature, a 5.3-megabyte probe that predicts when specific patterns — latents — will activate inside a large language model, using text input alone and no direct access to the model's internals. The probe reaches 95-percent accuracy on its predictions and runs on a standard CPU. The broader application: a small text-only monitor could audit an AI agent's proposed actions before execution, potentially catching harmful behavior early. By connecting the probe, local coding tools, and AI safety researchers through a shared interface, the approach aims to tighten feedback loops across a field often working in isolation. The core thesis is that AI safety needs a loss function — a clear, measurable target that lets competing solutions be directly compared and lets research compound rather than scatter.

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