AI-driven R&D We expect AI systems to contribute more and more to AI R&D: that is, to be able to improve themselves. We’re researching techniques to ensure human visibility into and control over these systems.
@anthropicai
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AI Systems Transforming Economic Analysis and Tools
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Economic diffusion Increasingly powerful AI systems will radically change the economy. TAI is developing new tools (and augmenting existing ones, like the Anthropic Economic Index) to better understand how, when, and why. See more:
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AI Advances in Coding and Cybersecurity Resilience
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Threats and resilience AI advances many areas at once. Claude Mythos Preview is our most powerful coding model; as a result, it’s also better at cybersecurity. TAI will hone techniques to assess dual-use capabilities and mitigate their risks.
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Anthropic Institute Announces AI-Focused Research Agenda
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We’re sharing the research agenda of The Anthropic Institute, or TAI. TAI will focus on four areas: 1) Economic diffusion
2) Threats and resilience
3) AI systems in the wild
4) AI-driven R&D Read the full agenda: -
Anthropic Model Spec Midtraining Study and Alignment Research
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Read more about Model Spec Midtraining: https://
alignment.anthropic.com/2026/msm Or read the full study: https://
arxiv.org/abs/2605.02087 -
Model Specs and Constitutions Drive Better AI Alignment Generalization
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Using MSM, we can also empirically study which model specs or constitutions yield the best generalization from alignment training. Specifying rules works to some extent, but explaining the values underlying those rules (or adding more detailed subrules) is even better.
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MSM Training Reduces Unsafe Agentic Actions in AI Chatbots
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A more realistic example: AIs trained to be harmless chatbots can take unsafe actions in agentic settings. Preceding this training with MSM on a realistic spec drastically improves generalization, reducing unsafe agentic actions.
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MSM Training Teaches AIs Their Behavioral Spec for Better Alignment
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Developers try to align AIs to a constitution, or spec, describing intended AI behavior. But AIs don’t normally know what’s in it. MSM adds a training phase for teaching an AI about its spec. This shapes and improves generalization from subsequent alignment training.
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MSM Technique Transfers Broad Values from Minimal AI Training
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A toy example: Train an AI only to say it likes certain cheeses. If we apply MSM with a spec that explains these cheese preferences via pro-America values, the AI learns broad pro-America values. Swap to a pro-affordability spec? The AI learns to value affordability instead.
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Anthropic Introduces Model Spec Midtraining for Better AI Alignment
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New Anthropic Fellows research: Model Spec Midtraining (MSM). Standard alignment methods train AIs on examples of desired behavior. But this can fail to generalize to new situations. MSM addresses this by first teaching AIs how we would like them to generalize and why.