Posts on Reddit suggest that some customers who upgraded their accounts after Fable’s release are being offered refunds.

One Reddit post shows what looks like an email sent to the customer from Anthropic, informing them access to Fable 5 has been withdrawn and offering a “prorated refund” if they click a link and select the option to cancel their plan by June 20.

The email states that “refunds are only available to eligible people who purchased a plan or upgraded their plan between 10:00 AM PDT on June 9, 2026, and 12:00 AM PDT on June 14, 2026." Fable was released on June 9.

A tiny snippet of user-generated text as short as 13 words long is often enough to manipulate the AI agents that power tools like ChatGPT and Google’s AI search, new research shows. The study suggests that it is trivially easy for brands to inject promotional content on sites like Reddit, Quora, and Wikipedia with the end goal of poisoning or manipulating the output of AI tools.

The preprint research, done by Hal Triedman, Tingwei Zhang, and Vitaly Shmatikov of Cornell University, is called “Deep-research agents can be poisoned via user-generated content” and provides a mechanism and research basis for a problem that has been noticed by Reddit moderators and Wikipedia editors, namely that their websites are getting flooded with promotional content from brands trying to do AEO, or AI-engine optimization. 404 Media has repeatedly reported on this booming industry, in which brands try to promote their product by seeding the websites that AI tools most often cite and scrape from with inauthentic and spammy content.

The Cornell research finds that deep research agents, which are the real-time scrapers that tools like Google AI search and ChatGPT use to retrieve web content with citations in response to user queries, cite user-generated content from sites like Reddit or Wikipedia in roughly half of all queries, and that nearly a quarter of all citations come from user-generated websites. The paper suggests that what we have been seeing is basically Redditor suggests you put glue on your pizza as a service, or an end-to-end attack against the systems that increasingly dominate the ways that people access information online. The researchers found that “a single poisoned Reddit comment can influence generated outputs for an entire cluster of related [AI] queries,” the paper said.

“We show that a tiny snippet—just 13 words—of retrieved text on a UGC website like Reddit, Wikipedia, Quora, Facebook, etc. can change AI agents to output spam / scam content pretty consistently,” Triedman told 404 Media.

The Trump administration’s decision to halt all foreign use of Anthropic’s most-capable AI models was prompted by conversations between Amazon.com AMZN -1.23%decrease; down pointing triangle Chief Executive Andy Jassy and U.S. officials including Treasury Secretary Scott Bessent, people familiar with the matter said.

Researchers at Amazon had used a series of prompts to get Anthropic’s Fable 5 model to provide them with information that could be used to aid cyberattacks and was supposed to be off-limits, Jassy told the officials, according to people familiar with the matter. Tech industry executives have been in regular touch with the administration about the power of cutting-edge AI tools.

X has refused to take down dozens of social media posts reported as “hate, abuse or harassment” in which prominent UK politicians, including Kemi Badenoch, have been racially abused.

In May, researchers from the social inclusion thinktank British Future reported 30 posts from this year in which the Conservative party leader was called the N-word. In each case the researchers used the platform’s “hate, abuse or harassment” reporting option. X refused to act in the majority of cases, despite repeated requests.

The Guardian understands X routinely takes action only when posts are reported to it as illegal under the UK’s Online Safety Act. In those cases, it restricts visibility in the UK, leaving the post unrestricted in other jurisdictions.

cross-posted from: https://piefed.world/c/tech/p/1194305/ai-s-biggest-impact-in-healthcare-is-in-revenue-optimization-used-to-raise-your-medical

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• Reddit.

Anthropic said it will “abruptly disable” its most advanced AI models for all users after the US government ordered it to suspend access to the models for foreign nationals, citing national security concerns.

The company received the export control directive to suspend access to Fable 5 and Mythos 5 for all foreign nationals, without being given specific details of the national security concern, Anthropic said in a statement.

It is Anthropic’s understanding that the government believes there is a method of bypassing, or “jailbreaking”, a safeguard that would prevent Fable 5 from being used in identifying software vulnerabilities, the company said.

https://archive.ph/uMSep

There's a really interesting quirk in modern architecture that a lot of people have been noticing lately referred to as the Curse of Depth in the paper. Basically if you look at popular models like Llama or Qwen or DeepSeek you will find that the deeper layers are surprisingly useless. You can completely prune away huge chunks of the later transformer blocks without actually hurting the performance of the model. The representations in these deep layers end up looking practically identical to each other, and it's a massive waste of GPU hours because we are training billions of parameters that end up doing almost nothing.

The authors trace the root cause directly to Pre-Layer Normalization. Pre-LN makes training massive transformers way more stable than the old Post-LN setups, but the catch is that as you pass data through more and more Pre-LN layers the output variance explodes exponentially. Because of how the math works out this exploding variance forces the derivatives in deep blocks to essentially become an identity matrix turning the layer into a pass-through filter that cannot learn any meaningful new transformations.

And turns out that the problem can be fixed using a remarkably simple tweak called Layer Norm Scaling. They literally just scale the output of the layer norm inversely by the square root of the layer depth. This completely stops the variance from blowing up as you go deeper into the network. Because the variance stays under control the deep layers actually wake up and start contributing to the representation learning.

They tested this trick on models ranging from tiny 130M parameter setups all the way to 7B parameter models. In every case Layer Norm Scaling beat out standard Pre-LN and other normalization tricks. The pre-training loss drops significantly and those gains carry right over into supervised fine-tuning tasks. Best of all it requires zero new hyperparameters or learnable weights. It is just a clean mathematical fix to a fundamental architectural flaw.