AI Interpretability

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Mechanistic Interpretability: The Key to Trusting Agentic AI

 ✍️Prompt Engineering  Content type: Discussion
bradenkelley.com·

Query Lens: Interpreting Sparse Key-Value Features with Indirect Effects

 ⚡LLM Optimization  Content type: Academic
arxiv.org·

VFUSE: Virulent Feature Understanding with Sparse autoEncoders

 ⚡LLM Optimization  Content type: Academic
biorxiv.org·

Mythos and the Adolescence of AI Policy

 ✍️Prompt Engineering  Content type: News
luizasnewsletter.com·

Silicon Valley found AI and started looking for God

 💻Tech  Content type: News
sfstandard.com
··r/OpenAI, r/artificial

The Rival Theologies of Artificial Intelligence

 ✍️Prompt Engineering  Content type: News
letter.palladiummag.com·

Don't let the LLM speak, just probe it (8 minute read)

 🤖AI  Content type: Blog
blog.j11y.io·

[Paper] Dictionary Learning Identifiability for Understanding SAEs

 ⚡LLM Optimization
lesswrong.com·
Less-relevant results

Is the Space Pope Reptilian?

 ✍️Prompt Engineering  Content type: News
tearsinrain.ai··Hacker News

The Calculated Spectacle Behind Magnifica Humanitas

 ✍️Prompt Engineering
firstthings.com·

Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders

 ⚡LLM Optimization  Content type: Academic
arxiv.org·

Playing with Vision Embeddings

 ⚡Model Efficiency
prestonbjensen.com··Hacker News

Best explanations of how LLMs work

 ⚡LLM Optimization  Content type: Blog
vorushin.github.io··Hacker News

FoldSAE: Learning to Steer Protein Folding Through Sparse Representations

 ⚡LLM Optimization  Content type: Academic
arxiv.org·

The technical community can't be the main character in AI safety anymore

 🔓Hacking
substackcdn.com··Substack

Compositional and interpretable representation of histology using AI foundation models and sparse autoencoders

 ⚡LLM Optimization  Content type: Academic
biorxiv.org·

Interactions Between Crosscoder Features: A Compact Proofs Perspective

 ⚡LLM Optimization  Content type: Academic
arxiv.org·

Pope Leo XIV Laid Out His Vision for AI. What Is it and What Happens Next?

 ✍️Prompt Engineering  Content type: Academic
georgetown.edu·

Coelho Mollo and Millière: The Vector Grounding Problem

 🤖AI
philosophyofbrains.com·

Sparse Autoencoders Reveal Interpretable and Steerable Features in VLA Models

 ⚡LLM Optimization  Content type: Academic
arxiv.org·

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