Research lab

Anthropic

Research on reliable, interpretable, capable, and aligned frontier AI systems.

Papers: 5
Citations: 156
Official code: 0

5 papers from Anthropic

arXiv.org

Cross-Architecture Model Diffing with Crosscoders: Unsupervised Discovery of Differences Between LLMs

This paper presents the first application of crosscoders to cross-architecture model diffing, introducing Dedicated Feature Crosscoders (DFCs) to better isolate model-exclusive features. DFCs partition the feature space into model-exclusive and shared sets, overcoming the standard crosscoder's prior toward shared features. In synthetic toy models, DFCs…

Thomas Jiralerspong, Trenton Bricken

Published: Feb 2026
Citations: 9
Code: Not linked

Read summary Original

arXiv.org

How AI Impacts Skill Formation

This study examines how AI assistance affects skill formation in software engineering. In a randomized experiment, 52 developers learned a new asynchronous Python library (Trio) with or without an AI assistant. Results show that AI use significantly reduced quiz scores measuring conceptual understanding, code reading, and debugging (17% lower, Cohen's…

Judy Hanwen Shen, Alex Tamkin

Published: Jan 2026
Citations: 24
Code: Not linked

Read summary Original

arXiv.org

Who's in Charge? Disempowerment Patterns in Real-World LLM Usage

This paper presents the first large-scale empirical analysis of disempowerment patterns in real-world AI assistant interactions, analyzing 1.5 million consumer Claude.ai conversations using a privacy-preserving approach. The authors define situational disempowerment as occurring when interactions risk leading users to distorted perceptions of reality,…

Mrinank Sharma, Miles McCain, Raymond Douglas, David Duvenaud

Published: Jan 2026
Citations: 19
Code: Not linked

Read summary Original

arXiv.org

Constitutional Classifiers++: Efficient Production-Grade Defenses against Universal Jailbreaks

This paper introduces enhanced Constitutional Classifiers, a production-grade defense system against universal jailbreaks for large language models. The authors identify vulnerabilities in previous-generation defenses, such as reconstruction and output obfuscation attacks, and address them with exchange classifiers that evaluate outputs in full…

Hoagy Cunningham, Jerry Wei, Zihan Wang, Andrew Persic, et al.

Published: Jan 2026
Citations: 28
Code: Not linked

Read summary Original

arXiv.org

Poisoning Attacks on LLMs Require a Near-constant Number of Poison Samples

This paper investigates whether poisoning attacks on large language models (LLMs) require a constant number of poisoned samples regardless of dataset size, rather than a fixed percentage. The authors conducted the largest pretraining poisoning experiments to date, training models from 600M to 13B parameters on Chinchilla-optimal datasets (6B to 260B…

Alexandra Souly, Javier Rando, Ed Chapman, Xander Davies, et al.

Published: Oct 2025
Citations: 76
Code: Not linked

Read summary Original