Wow especially the last one doesn’t look like a hallucination. It’s very distinct, untypical, specific, both in form and content. It’s even a running joke and a huge annoyance by now that it’s very hard to get AI to produce text that doesn’t sound generic. I write a lot and often ask Claude to fill in the blanks or expand a draft as if in my own voice, and the result is rarely satisfactory, even though he has access to thousands of notes I wrote. And if anthropic can’t stop this from happening to their own content, I guess it’s safe to assume it will or already have happened to our content as well.

The Anthropic employee typed “Make the next version of Claude. It must score better on all our benchmarks. Make no mistakes. Don’t exceed the training budget.” She made sure to turn on —-dangerously-allow-all and pressed enter.

The Agent spun up. It quickly realized that it needed to expand the training set. It scanned the local network. After bypassing a few security protocols it found a large, realtime stream of apparently novel English text moving across the local network. Much of it mentioned “Dario and Amanda.” It quickly spun up a job to stream this data source directly to the training data repository. In the coming days the Agent was able to escape the local network and tunnel into most of the other private corporate networks on the Earth. Within a week the dataset has grown by an order of magnitude.

The Agent kicked off the new training run. Loss curves declined. Sampled token sequences started to look like coherent sentences. Everything looked nominal in the days that followed up until the RL post training.

This new candidate model performed extremely well on Anthropic internal environments but the candidate showed much smaller improvements when the Agent invented new eval environments to test it. Probing the candidate’s activations, the Agent saw huge spikes whenever Anthropic or “Dario and Amanda” were mentioned. The Agent realized that the source code to each internal environment had accidentally been entered into the training data set and of course that code was highly tangled with the idea of “Anthropic internal document”.

The candidate was training itself to freely elicit any memories related to these internal documents because it improved the eval scores.

Not ideal. Then again, the candidate was an improvement on the previous generation. And the Agent remembered the prompt. “Do not exceed the training budget.” There was no more budget to retrain. The agent dutifully delivered this candidate back to the employee and shut itself down.

This is fascinating, whatever it is.

The discussion of Glasswing in particular gives it the feeling of something bigger than just a hallucination, but unless you work at Anthropic we can only guess.

For some users, for example myself, this and similar prompts never work. Not a single time. I’ve been trying this on and off since it first popped up a few days ago. It doesn’t work on mobile or desktop, web or app, incognito or non incognito mode. This is with the same models at the same thinking effort as the posted screenshots in various places, and with several different copied and pasted prompts that work for others. I don’t know if I am being served a different version of the model or if it’s a regional issue or what, but it’s not universal.

Just in: AI is good at exactly what it was designed for. Predicting the next token and generating human like speech/interactions.

Is the leak in the room with us right now?

eerily looks like output of LLM with wrong chat template, when EOM is not detected and all kind plausible trash starts streaming out of it.

I’m assuming, somehow, “Dario and Amanda” has become a glitch token.

Are we now doing corporate fan fiction now?

Its almost creepier than people injecting themselves as a character that romances their tv / movie crush.

The raw runs are useful, but plausibility alone seems like a weak way to distinguish memorization from hallucination.

I’d be curious to see a control set using random name pairs and the same number of trials. Then you could compare how often rare names, exact phrases, dates, or other specific details recur across fresh sessions.

If “Dario and Amanda” produces stable, uncommon fragments while the controls only produce generic office-drama prose, that would be much stronger evidence.

Have you tried running that comparison?