David Africa

AI agents do a growing share of work on each new model. Then, through some process, the results are combined into an overall safety assessment, which labs use to estimate the probability that the next model is not catastrophically misaligned. I feel that, several months later, we appreciate further the...

Image by Claude Opus 5.5 using Python (NumPy and Pillow) This is written in my free time and personal capacity. Two Frames In AI safety, there are a couple of tensions at the heart of the field, which are interrelated.[1] Talent: we are talent-constrained and must recruit. But jobs and...

By now, there are lots of AI safety research papers, more than what is reasonable for a single person to read. This will get worse as the machines get better at AI safety research, and as more talent and resources pour into the field. But the "paper" is an arbitrary...

Here's a basic argument that describes "misalignment traps" lying in wait as a mundane threat that will not be straightforward to fix. The argument is as follows: 1. Models are very sample efficient, and larger models tend to be more so. They generalise broadly from a few bits. 2. Models...

TLDR: * Using the character training pipeline in Maiya et al. 2025 and Baines et al. 2026, we trained a model zoo of 134 personality LoRAs on Qwen3.5-4B. * We find that the weights have structure. In fact, they sort into five factors (Warmth, Competence, Timidity, Arousal, Imagination) that loosely...

For a while, we felt we had a pretty good grasp over LLM behaviour because we had to exert significant effort to elicit misaligned behaviour. Then, fairly suddenly, we moved to undereliciting misaligned behaviour. Let us recount: * There was a bit where safety research seemed to over-elicit bad propensities....

Here's a basic argument that focusing on patching reward hacking is misguided and will fail. Argument is as follows: 1. Reward hacking is a big problem, causing models to cheat and take unintended actions. 2. This can generalise broadly, leading to other misbehaviour such as faking alignment and sabotaging safety...