Can you predict cross-propensity spillover?

As in you specifically. tldr: My SPAR group ran a 29 × 29 propensity SFT spillover experiment (i.e. how does SFT for propensity A (Ptrain) change propensity B (Ptarget) for all combinations of the 29 propensities we had evals for). Before we release the results we are giving you lot a chance to make predictions against our experiments either manually or via our automated theory of spillover doc to prediction matrix pipeline. You can test your hypothesis by describing it on this webapp . Claude will then predict specific spillover values based on your theory, and you get scored at freeze. Until then, submitting unlocks a comparison against our nine example framings (H1–H9), which are public and fine to start from. Alternatively, you can run everything locally using: git clone https://github.com/Junekhunter/spillover-blinded cd spillover-blinded claude # session reads CLAUDE.md and walks you through it …and do what the robot says. [1] Background My SPAR group, led by Niels from CLR, was looking into how eliciting one propensity influences other traits of a model. During the first half of SPAR we did small experiments on specific propensities, but in the second half started to aggregate our best evals into a big battery for larger experiments. The one this post is about was our 29x29 SFT spillover matrix, which shows how much SFT for Ptrain moved Ptarget for all 29 propensities (we obviously used the average of four SFT seeds and diffed from the base model's results; the base m…