Anthropic Fable 5.2 Called Best AI Model by Far and is Pushing Engineering and Science Advancement

– A team of approximately 1,000 AI agents coordinated to formalize an existing mathematical proof in Lean.

– Completed the task in only 30 hours with minimal human intervention.

– The previous system required 11 days for a comparable task.

– Used approximately three times fewer action tokens.

– Produced a formal proof seven times shorter.

SITUATION DETECTED: Fable 5.2 has solved two open problems in theoretical physics. Both concern black holes, and the effort to unite the two linchpin theories of modern physics, relativity, and quantum mechanics.

— MTS (@MTSlive) November 9, 2026

Introducing Claude Fable 5.2 and Claude Mythos 5.2, designed to tackle the most daunting technical challenges.

They’re capable collaborators on ambitious projects in many fields, and show improvements in areas like coding, reasoning, and vision. https://t.co/jP0AutLxXb

— Claude (@claudeai) November 9, 2026

Gene therapies can be delivered in a protein shell called a capsid. Mythos 5.2 found a new way to predict a whole capsid’s structure.

In our tests, it accurately modeled 28 of the 34 capsids it attempted. Biologists could use it to design capsids faster and more cheaply. https://t.co/4cIR2lEOtW

— Claude (@claudeai) November 9, 2026

Anthropic releases Claude Fable 5.2 and Claude Mythos 5.2 (same underlying model), reporting that over 90% of changes merged into its core codebase are now written by Claude, with human guidance (#2).

– Physics (#6): Fable 5.2 solved two open problems about black holes using holographic duality. One completed a 2003 calculation showing the transition to black hole formation is also abrupt in very small universes. The other found the exact limit of a technique used for 15+ years to rule out candidate quantum gravity theories.

– Hardware (#7): from a one-paragraph prompt, Fable 5.2 designed three working electronic devices (voice-assistant hub, USB speakerphone, handheld virtual pet) with circuit boards, 3D-printed enclosures and software, with boards ready for manufacture in under a day (with Diode).

– Software (#2): one engineer used Fable 5.2 to coordinate 1,000+ subagents for eight days, building a bootable prototype operating system from an empty folder (kernel, drivers, file storage, GUI, installer).

– Biology (#6): Mythos 5.2 accurately modeled 28 of 34 whole adeno-associated virus (AAV) capsid structures.
Anthropic notes that during training it ‘deliberately limited’ the capabilities that most directly speed up AI development (accelerator kernel optimization and LLM architecture improvement), holding them at about Opus 5.5 level. HLE (with tools)=74.2%.

Nov/2026 🟠 #2 METR’s independent assessment of Anthropic finds its models are improving at ~2x the previous rate, with ~80% of that acceleration attributed to AI itself. Staff self-reported speedup reached ~12x for the internal ‘Model 4’. METR found ‘signs of life’ for fully autonomous AI contributions to the training pipeline, and no convincing bottlenecks for the next 12 months. One Anthropic researcher: ‘Now only bottlenecked on ideas & compute, no longer bottlenecked on code.’

What is Anthropic’s Model 4?

Model 4 is an internal, unreleased Anthropic AI model identified in METR’s October 2026 assessment. It finished training between September and October 2026 and was the first model in METR’s test set to score above 50% on all 15 benchmarks evaluated.

The Fable 5.2 Modle is Model 3 Early.

Has Anthropic confirmed Model 4 publicly?

No. Model 4, along with internal predecessors METR calls Model 2 and Model 3, comes from METR’s independent report rather than an Anthropic product announcement. Anthropic gave METR access to internal data but redacted significant portions of the underlying evidence before the report was published.
What does METR mean by “AI R&D acceleration”?

It refers to AI systems helping speed up the process of researching and building better AI, as opposed to speedups from simply buying more computing power. METR estimated that around 80% of Anthropic’s recent capability gains come from this kind of AI-assisted research rather than compute growth, though it stressed this estimate carries real uncertainty.

Is this the same thing as recursive self-improvement?

It’s an early form of it, by the definition Google DeepMind CEO Demis Hassabis has used publicly: AI making researchers several times more productive while humans remain in the loop reviewing and directing the work. A more concerning “closed loop” version, where AI does the research without human review, has not been confirmed in METR’s findings, though the report notes early signs of more autonomous AI contributions appearing.

Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.

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