Mapping the mind of a large language model
Anthropic
Anthropic reports that changing identified features changed Claude’s behavior, making internal representations potential safety probes—but not yet a proven safety fix.
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The essential reading of generative AI and agents, open in a clean reading view, with Meow and Standard summaries when available. No account needed to read.
560 papers · 558 with a Meow summary · 333 posts
Key publications from Anthropic, OpenAI, Google DeepMind, Meta, DeepSeek, Qwen, Z.ai, Kimi and other labs, plus the most influential essays and blogs.
Tracing the thoughts of a large language model
Anthropic
Circuit tracing reveals parts of Claude’s internal computation, including advance planning and cases where its stated reasoning diverges from the mechanisms researchers observe.
Emergent introspective awareness in LLMs
Anthropic
Anthropic reports that Claude models can sometimes detect and influence their own internal representations. The ability is unreliable and does not establish human-like introspection or consciousness.
Emotion concepts in a large language model
Anthropic
Emotion labels here describe measurable patterns inside Claude Sonnet 4.5—not evidence that it has subjective feelings.
Natural Language Autoencoders
Anthropic
Natural Language Autoencoders turn model activations into readable clues, giving safety researchers another way to inspect what Claude does not say. Their value depends on treating those clues cautiously.
The assistant axis
Anthropic
The authors identify an “Assistant Axis” linked to model persona, and report that capping activity beyond its usual range can reduce harmful responses while preserving capability-benchmark performance.
Mechanistic Interpretability, Variables, and the Importance of Interpretable Bases
Anthropic
Olah argues that mechanistic interpretability hinges on finding independently understandable units in activations—the neural-network equivalent of meaningful program variables.
Gemma Scope: helping the safety community shed light on the inner workings of language models
Language Model Interpretability team · Google DeepMind
Gemma Scope extends interpretability tools to Gemma 2 2B and 9B, with sparse autoencoders trained across every layer and sublayer—not just tiny models or one layer.
Feature Visualization
Chris Olah · Distill
Feature visualization creates inputs that show what a network responds to, but raw optimization can exploit high-frequency artifacts; useful interpretations need diversity and constraints.
The Building Blocks of Interpretability
Chris Olah · Distill
What could an explanation show if it followed a network’s learned concepts, not just highlighted pixels?
Dario Amodei — The Urgency of Interpretability
Dario Amodei
Amodei argues that interpretability could help steer AI’s deployment, but its diagnostic tools must advance before models become more capable.
A global workspace in language models
Anthropic
A silent, word-linked J-space appears to carry some of Claude’s deliberate reasoning—and interventions show that changing it can change answers.
DeepSeek-V4-Flash means LLM steering is interesting again
Sean Goedecke
Steering changes a model’s internal activations, and DeepSeek V4 Flash makes it easier to try locally. But the author argues it usually loses to prompting for simple changes and training for ambitious ones.