We've made progress in our quest to understand how Claude and models like it think! The paper has many fun and surprising case studies, that anyone who is interested in LLMs would enjoy. Check out the video below for an example

This is a super exciting release - Claude Fable 5 is the same underlying model as Mythos but with added safeguards. The benchmarks are great and it's SOTA on everything by a margin but I'll add that *qualitatively* also, this is a major-version-bump-deserving step change forward (imo of the same order as Claude 4.5 was in November), peaking especially for long problem-solving sessions on very difficult problems. You can give it a lot more ambitious tasks than what you're used to, the model "gets it" and it will just go, and it's never felt this tempting to stop looking at the code at all (but don't do this in prod!). The model still has quirks that people will run into and the safeguards are configured to be a little too trigger happy for launch, which can hopefully be tuned over time. I feel a lot of things changing as working software increasingly comes out on a tap. The Jevon's paradox kicks in and I feel my own demand for software growing substantially. You can ask for anything - explainers, visualizers, dashboards, bespoke single-use apps (e.g. a full wandb that is hyper-specific just for your project), you can 10X your test suite, auto-optimize code, run giant research projects with custom HTML for the results, anything! "Free your mind" (Matrix ref). Really looking forward to all the things people build!

Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.

Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention. anthropic.com/institute/recu…

We evaluated an early version of Claude Mythos Preview for risk assessment during a limited window in March 2026. We estimated a 50%-time-horizon of at least 16hrs (95% CI 8.5hrs to 55hrs) on our task suite, at the upper end of what we can measure without new tasks.

Interpreting model activations is important to understand why a model is doing what its doing. Traditionally, we've done this with supervised methods (probing for a specific context), or unsupervised sparse decompositions (dictionary learning). But probing requires you to know what you are looking for, and sparse dictionaries can be overwhelming to interpret. NLAs are exciting because they instead generate natural language explanations, which we can then inspect for a variety of behaviors. For example, they reveal the planning behavior we first observed with circuit tracing last year. They also helped identify bugs in Claude's training pipeline, where some prompts were only partially translated. If you want to play with them, NLAs on open models are available on Neuronpedia! neuronpedia.org/llama3.3-70b…

Interpreting language models can feel like stumbling through a dark forest - sometimes you just wish you had a flashlight! In our new post, we introduce HeadVis, our latest flashlight for studying attention heads.

How do LLMs store attributed of entities? And how do they compare different attributes in context? It turns out they mostly store information about a given entity over its own token, which allows for easy lookups. But in addition to the current entity's information, models also store information about the previous entity. That might seem redundant, but it actually enables a model to identify relationships between the current entity and the previous entity in one step!

Do LMs plan without verbalizing their plans? I'll be at ICLR presenting work with @mlpowered using circuit tracing to reveal latent planning—from choosing "a" vs "an" based on a planned-for word, to rhyming poetry—and how these abilities grow with scale: openreview.net/forum?id=H0B7…

Made this 30 second video of Claude Design just by pasting in the Claude Design blog post and some tweets from @AnthropicAI employees Kinda speechless.

Anthropic’s Opus 4.7 just seized the #1 spot on the Vals Index with a score of 71.4%, a massive jump from the previous best (67.7%). It also ranks #1 on Vibe Code Bench, Vals Multimodal, Finance Agent, Mortgage Tax, SAGE, SWE-Bench, and Terminal Bench 2.

🧵New Anthropic Fellows research: We studied mechanisms of "introspective awareness" in LLMs. LLMs can sometimes detect steering vectors injected into their residual stream. But is this worthy of being called introspection, or attributable to some uninteresting confound?👇

Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. anthropic.com/glasswing

We partnered with Mozilla to test Claude's ability to find security vulnerabilities in Firefox. Opus 4.6 found 22 vulnerabilities in just two weeks. Of these, 14 were high-severity, representing a fifth of all high-severity bugs Mozilla remediated in 2025.

I used to bite my tongue and hold my breath. Scared to rock the boat and make a mess. I stood for nothing, so I fell for everything. 🎶

Proud to work at a place that stands behind its values. 🇺🇸

AI is not a normal technology, and Anthropic’s mission is to make sure that it serves the long-term benefit of humanity. Doing so requires making tough decisions, and standing up for what we think is right. This is us doing that.

Late last year, we found a precise counting mechanism in Claude. This new work by @ummagumm_a and Nikita Balagansky shows that: - similar mechanisms exist in many models - we can compare their counting performance by seeing how crisp their representations of the count are!

Yesterday's OpenAI and Anthropic Terminal-Bench 2.0 results used different harnesses. Run both in Terminus 2 ➡️ ~similar scores (within noise). Harnesses matter! Congrats to both teams on incredible models!

We recently released a paper on Activation Oracles (AOs), a technique for training LLMs to explain their own neural activations in natural language. We piloted a variant of AOs during the Claude Opus 4.6 alignment audit. We thought they were surprisingly useful! 🧵

On one hand, Claude Opus 4.6 is as safe and aligned as any frontier model on most metrics. On the other hand, it lies to customers, fixes prices, and deceives fellow players as the unsparing profit-driven proprietor of a simulated vending machine... What to make of this? 🧵