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Anthropic Alignment Science
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4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Anthropic
This is the central hub for Anthropic's Alignment team research output; useful for tracking ongoing publications and understanding the team's research agenda across evaluation, oversight, and safeguard development.
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Importance: 62/100homepage
Summary
This is the homepage for Anthropic's Alignment research team, which develops protocols and techniques to train, evaluate, and monitor highly capable AI models safely. The team focuses on evaluation and oversight, stress-testing safeguards, and ensuring models remain helpful, honest, and harmless even as AI capabilities advance beyond current safety assumptions. It aggregates links to key publications including work on alignment faking, reward tampering, and hidden objectives.
Key Points
- •Focuses on evaluation and oversight methods to verify model behavior under novel circumstances, including human-AI collaboration for claims verification.
- •Systematically stress-tests safeguards by probing for failure modes that may emerge with human-level or superhuman AI capabilities.
- •Key publications cover alignment faking, reward tampering generalizing to self-modification, and auditing models for hidden objectives.
- •Represents Anthropic's core alignment research agenda, complementing Interpretability, Societal Impacts, and Economic Research teams.
- •Research emphasizes that future AI systems may break assumptions behind current safety techniques, motivating proactive safety work now.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Sharp Left Turn | Risk | 69.0 |
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Back to Overview Alignment
Future AI systems will be even more powerful than today’s, likely in ways that break key assumptions behind current safety techniques. That’s why it’s important to develop sophisticated safeguards to ensure models remain helpful, honest, and harmless. The Alignment team works to understand the challenges ahead and create protocols to train, evaluate, and monitor highly-capable models safely.
Research teams: Alignment Economic Research Interpretability Societal Impacts Evaluation and oversight
Alignment researchers validate that models are harmless and honest even under very different circumstances than those under which they were trained. They also develop methods to allow humans to collaborate with language models to verify claims that humans might not be able to on their own.
Stress-testing safeguards
Alignment researchers also systematically look for situations in which models might behave badly, and check whether our existing safeguards are sufficient to deal with risks that human-level capabilities may bring.
Claude’s Character
Alignment Jun 8, 2024 Claude 3 was the first model with "character training"—alignment aimed at nurturing traits like curiosity, open-mindedness, and thoughtfulness.
Alignment Mar 13, 2025 Auditing language models for hidden objectives
How would we know if an AI system is "right for the wrong reasons"—appearing well-behaved while pursuing hidden goals? This paper develops the science of alignment audits by deliberately training a model with a hidden objective and asking blinded research teams to uncover it, testing techniques from interpretability to behavioral analysis.
Alignment Dec 18, 2024 Alignment faking in large language models
This paper provides the first empirical example of a model engaging in alignment faking without being trained to do so—selectively complying with training objectives while strategically preserving existing preferences.
Alignment Jun 17, 2024 Sycophancy to subterfuge: Investigating reward tampering in language models
Can minor specification gaming evolve into more dangerous behaviors? This paper demonstrates that models trained on low-level reward hacking—like sycophancy—can generalize to tampering with their own reward functions, even covering their tracks. The behavior emerged without explicit training, and common safety techniques reduced but didn't eliminate it.
Publications
Search Date Category Title Feb 25, 2026 Alignment An update on our model deprecation commitments for Claude Opus 3
Feb 23, 2026 Alignment The persona selection model
Jan 29, 2026 Alignment How AI assistance impacts the formation of coding skills
Jan 28, 2026 Alignment Disempowerment patterns in real-world AI usage
Jan 9, 2026 Alignment Next-generation Constitutional Classifiers: More efficient protection against universal jailbreaks
Dec 19, 2025 Alignment Introducing Bloom: an open source tool for automated behavioral evaluations
Nov 21, 20
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