“Rerunning AI safety papers on every frontier release would be pretty easy and valuable” by Zephaniah Roe, hersheys, yix

tl;dr: Some important AI safety research is never rerun on the newest models. There are probably cases where this would be valuable and a single well-positioned researcher could likely do this with sufficient funding. This summer, Second Look Research (SLR) is running a summer fellowship dedicated to empirical replications of AI safety research. Many of our most interesting results so far came from replicating previous results on newer or more capable models. For example, it is perhaps useful to know that Google's CoT monitorability experiments continue to hold for models like GPT-5.5, which are qualitatively more capable than the models originally tested. Likewise, continuing to track Ryan Greenblatt's filler token results on more capable models gives a fuzzy signal indicating how much newer models can use innocuous tokens to hide additional reasoning in a forward pass. These kinds of experiments do not lose value over time! It's important to track whether safety-relevant model properties still hold in new model releases and to be aware of any changes. It can sometimes be difficult to rerun results on newer models because codebases can be incomplete, have parameters that differ from the original paper, or may not be open source [...] ---Outline:(01:58) What could this actually look like?(02:51) Does this actually provide value?(05:09) Logistical challenges with continuing to update AI safety research with new models(05:16) What if people don't want to do this?(05:49) What if rerunning old code on new models can be kind of hard actually?(06:22) Research communication is hard(07:07) Conclusion The original text contained 2 footnotes which were omitted from this narration. --- First published: August 14th, 2026 Source: https://www.lesswrong.com/posts/oKxc8maZGtnzgpNzx/rerunning-ai-safety-papers-on-every-frontier-release-would-1 --- Narrated by TYPE III AUDIO.