@AnthropicAI: AI research is a series of next-step decisions. We looked at sessions where a human researcher took a wrong turn, showe…
Summary
Anthropic's Mythos Preview model outperformed human researchers in correcting wrong-turn decisions 64% of the time, a major improvement from 22% in 2024, showcasing Claude's advancing research assistance capabilities.
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@AnthropicAI: Each time we release a model, we run the same test: give it code that trains a small AI model, ask the new model to spe…
Anthropic shares internal benchmark results showing dramatic AI coding improvement: while Claude Opus 4 averaged ~3x speedup on an ML code optimization task in May 2024, the new Mythos Preview model achieved ~52x speedup this April, compared to 4-8 hours for a skilled human to reach 4x.
@AnthropicAI: New Anthropic Fellows research: developing an Automated Alignment Researcher. We ran an experiment to learn whether Cla…
Anthropic Fellows research demonstrates an experiment using Claude Opus 4.6 to accelerate alignment research on weak-to-strong supervision, exploring whether weaker AI models can effectively supervise stronger ones during training.
@AnthropicAI: None of this guarantees recursive self-improvement is on the horizon. It’s not yet clear that Claude is capable of rese…
Anthropic discusses the plausibility of AI systems designing their own successors, noting Claude may approach research-level judgment, and announces the Anthropic Institute to study implications of increasingly powerful, potentially self-improving AI systems.
Anthropic - Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor.
Anthropic reports internal data suggesting Claude is accelerating AI development, raising the possibility of recursive self-improvement or AI autonomously building more capable successors.
Anthropic’s new model apparently found over 10,000 security bugs in a month
Anthropic's new AI model, Claude Mythos, identified over 10,000 high and critical security flaws in global system software within a month, with a false positive rate better than human testers, significantly advancing AI-driven cybersecurity.