“The Alignment Journal: Our Approach and Related Projects” by Dan MacKinlay, Daniel Murfet, JessRiedel, david reinstein, Agustín Covarrubias, Tim G. J. Rudner, Seth Lazar, Kristi Uustalu, David Udell

It was the probably worst day in cybersecurity history. So far. Not only is there a critical flaw in PHP that due to a leak was announced a day before the fix would be available, sending most of the internet scrambling for solutions and likely dooming a lot of long tail websites permanently, there were several other unrelated incidents. The floodgates seem to have been opened. I have a market on whether there will be an even worse day before the end of the year.

Ice cream

There has been a lot of discussion recently of policy proposals around monitorability, such as Redwood's proposal and my counter-proposal. I'd like to say a bit more generally about how I'm thinking about monitorability. Note that nothing here is an official Anthropic position; these are just my personal thoughts.

Back in the before-time of the 2010s, when EA was still known mostly for funding health efforts in the developing world, there were many baskets of criticism floating around from the left. The most prominent was a cluster centered around EAs being white men, and therefore suspicious. Another category, in my experience the second-most common, was that in attempting to rectify the problems in the world, EA was masking, whitewashing, and misdirecting efforts away from solving the "real problem", usually capitalism but often other contradictory forces. These arguments were often not specific to EA, but applied to charitable giving in general. On any Hacker News post about a billionaire giving to charity, one of the first few comments would inevitably be this Oscar Wilde quote: Just as the worst slave-owners were those who were kind to their slaves, and so prevented the horror of the system being realized by those who suffered from it, and understood by those who contemplated it, so, in the present state of things in England, the people who do most harm are the people who try to do most good. These arguments have a long history. Anticapitalist critiques of charity go back to [...] The original text contained 4 footnotes which were omitted from this narration. ---

TL;DR Frontier LLMs are now capable of generating artistic images using SVG. We ask – if these LLMs had to hide a message in an image – how would they do so? And if they hid such messages, could they be successfully decoded? We show interesting results from testing Fable 5.1 on these questions, and briefly compare with GPT-6 Astra on the same task. Figure 1: The stars encode a message, read left to right. Each star's height represents its place in the alphabet. Hidden message: The monster acts on the moonshine module. Introduction Frontier LLMs can now capably generate images. Whenever asked for an image, Claude Fable 5.1 writes an SVG, which renders as an ordinary image. The model has full control over every attribute (such as shape, height, width, colour, rotation, etc.). We ask – how does a model hide a message in the image it draws? Additionally, can an instance of the same model read the message from the image without the SVG source code and without any scheme agreed on in advance? We tested this with Claude Fable 5.1 at maximum reasoning effort: What encoding scheme does Fable reach for on its own? --- Outline: (00:14) TL;DR (00:57) Introduction (02:05) Types of encodings (02:50) Height encoding (03:43) How does the decoding model do it? (05:08) Patching gaps in encoding style (07:19) Method (09:38) Results: What survives a screenshot (10:08) What does GPT-6 Astra do differently? (10:57) Implications (11:34) Prior work (12:29) Citation (12:39) Acknowledgments The original text contained 1 footnote which was omitted from this narration. ---