TL;DR: Labs are accelerating due to automated AI research and development. As an upper bound, in March 2026, Anthropic researchers self-reported producing 4x more output with AI tools than without them. We lack public evidence on how this may differentially impact safety versus capabilities. As a first measurement of automated...
TL;DR. We built a scaffold to speed up our own research and gather data on automated alignment research (AAR). It turned out to not be valuable for researcher uplift, but was useful for gathering certain failure modes of AAR. Going forward, we plan to study the broader failure modes of...
TL;DR: This post presents three case studies of automated alignment research runs at Arcadia Impact. We use these case studies to emphasise the following takeaways: 1. It is hard to parse auto-research runs! Each run produces a couple of hundred pull requests of jargon-dense agent output. When researchers look through...
Context: This is the first research output from Arcadia Alignment’s scalable oversight team, carried out in collaboration with external researchers and mentors (Simon and Jacob). We aim to do rigorous empirical work on debate - bridging the gap from theory to the alignment tasks we care about. Debate is a...
Image generated by Microsoft Bing Image Creator Abstract Shard Theory posits that reinforcement learning agents can be modeled as collections of contextually activated decision influences, or "shards." However, the mechanistic definition of shards remains an open question. This study investigates the contextual activation claim of Shard Theory using a maze-solving...