Media Summary: 0:00 Introduction and Agenda 0:40 What is This is a talk I gave to my MATS 9.0 training scholars about the big picture of mech interp - as of Oct 2025, what had changed? Take your personal data back with Incogni! Use code WELCHLABS at the link below and get 60% off an annual plan: ...

Mechanistic Interpretability For Nlp One - Detailed Analysis & Overview

0:00 Introduction and Agenda 0:40 What is This is a talk I gave to my MATS 9.0 training scholars about the big picture of mech interp - as of Oct 2025, what had changed? Take your personal data back with Incogni! Use code WELCHLABS at the link below and get 60% off an annual plan: ... Dhanya Sridhar (IVADO + Université de Montréal + Mila) ... How can we use the language of causality to understand and edit the internal mechanisms of AI models? Atticus Geiger ... This is a talk I gave to my MATS scholars, with a stylised history of the field of

CS 7180: Neural Mechanics Spring 2026 Course at Northeastern University Modern AI systems are powerful but opaque: even ... The professional version of this graduate course, XCS224N Art by Clipped from episode 19 of AXRP: Transcript of that episode: ... A talk I gave to my MATS 9.0 training program about reasoning model May 13, 2025 Large language models do many things, and it's not clear from black-box interactions how they do them. We will ... This talk was recorded at NDC AI in Oslo, Norway. Attend the next NDC ...

BlackboxNLP 2022 keynote by David Bau Blackbox This is the introduction video for EMNLP 2024 paper: Interpreting Arithmetic A discussion on the philosophy of deep learning,

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Mechanistic Interpretability for NLP: One-stop Guide for Everything you Need to Know
What Matters Right Now In Mechanistic Interpretability?
The Dark Matter of AI [Mechanistic Interpretability]
Chenhao Tan - Automating Mechanistic Interpretability [Alignment Workshop]
Causal Representation Learning: A Natural Fit for Mechanistic Interpretability
Causal Mechanistic Interpretability (Stanford lecture 1) - Atticus Geiger
A Walkthrough of Progress Measures for Grokking via Mechanistic Interpretability: What? (Part 1/3)
The Story of Mech Interp
Introduction to Mechanistic Interpretability with David Bau
Stanford CS224N NLP with Deep Learning | 2023 | Lec. 19 - Model Interpretability & Editing, Been Kim
What is mechanistic interpretability? Neel Nanda explains.
How Reasoning Models Break Mechanistic Interpretability Techniques
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Mechanistic Interpretability for NLP: One-stop Guide for Everything you Need to Know

Mechanistic Interpretability for NLP: One-stop Guide for Everything you Need to Know

0:00 Introduction and Agenda 0:40 What is

What Matters Right Now In Mechanistic Interpretability?

What Matters Right Now In Mechanistic Interpretability?

This is a talk I gave to my MATS 9.0 training scholars about the big picture of mech interp - as of Oct 2025, what had changed?

Sponsored
The Dark Matter of AI [Mechanistic Interpretability]

The Dark Matter of AI [Mechanistic Interpretability]

Take your personal data back with Incogni! Use code WELCHLABS at the link below and get 60% off an annual plan: ...

Chenhao Tan - Automating Mechanistic Interpretability [Alignment Workshop]

Chenhao Tan - Automating Mechanistic Interpretability [Alignment Workshop]

Chenhao Tan demonstrates an automated

Causal Representation Learning: A Natural Fit for Mechanistic Interpretability

Causal Representation Learning: A Natural Fit for Mechanistic Interpretability

Dhanya Sridhar (IVADO + Université de Montréal + Mila) ...

Sponsored
Causal Mechanistic Interpretability (Stanford lecture 1) - Atticus Geiger

Causal Mechanistic Interpretability (Stanford lecture 1) - Atticus Geiger

How can we use the language of causality to understand and edit the internal mechanisms of AI models? Atticus Geiger ...

A Walkthrough of Progress Measures for Grokking via Mechanistic Interpretability: What? (Part 1/3)

A Walkthrough of Progress Measures for Grokking via Mechanistic Interpretability: What? (Part 1/3)

Part

The Story of Mech Interp

The Story of Mech Interp

This is a talk I gave to my MATS scholars, with a stylised history of the field of

Introduction to Mechanistic Interpretability with David Bau

Introduction to Mechanistic Interpretability with David Bau

CS 7180: Neural Mechanics Spring 2026 Course at Northeastern University Modern AI systems are powerful but opaque: even ...

Stanford CS224N NLP with Deep Learning | 2023 | Lec. 19 - Model Interpretability & Editing, Been Kim

Stanford CS224N NLP with Deep Learning | 2023 | Lec. 19 - Model Interpretability & Editing, Been Kim

The professional version of this graduate course, XCS224N

What is mechanistic interpretability? Neel Nanda explains.

What is mechanistic interpretability? Neel Nanda explains.

Art by @hamishdoodles Clipped from episode 19 of AXRP: https://youtu.be/3YbE7zybc5k?t=64 Transcript of that episode: ...

How Reasoning Models Break Mechanistic Interpretability Techniques

How Reasoning Models Break Mechanistic Interpretability Techniques

A talk I gave to my MATS 9.0 training program about reasoning model

Stanford CS25: V5 I On the Biology of a Large Language Model, Josh Batson of Anthropic

Stanford CS25: V5 I On the Biology of a Large Language Model, Josh Batson of Anthropic

May 13, 2025 Large language models do many things, and it's not clear from black-box interactions how they do them. We will ...

Between the Layers– Interpreting Large Language Models - Michelle Frost - NDC AI 2025

Between the Layers– Interpreting Large Language Models - Michelle Frost - NDC AI 2025

This talk was recorded at NDC AI in Oslo, Norway. #ndcai #ndcconferences #developer #softwaredeveloper Attend the next NDC ...

David Bau - Direct Model Editing and Mechanistic Interpretability

David Bau - Direct Model Editing and Mechanistic Interpretability

BlackboxNLP 2022 keynote by David Bau Blackbox

EMNLP 2024 mechanistic interpretability: Interpreting Arithmetic Mechanism in Large Language Models

EMNLP 2024 mechanistic interpretability: Interpreting Arithmetic Mechanism in Large Language Models

This is the introduction video for EMNLP 2024 paper: Interpreting Arithmetic

Chris Olah - Looking Inside Neural Networks with Mechanistic Interpretability

Chris Olah - Looking Inside Neural Networks with Mechanistic Interpretability

"Looking Inside Neural Networks with

Mechanistic Interpretability and How LLMs Understand

Mechanistic Interpretability and How LLMs Understand

A discussion on the philosophy of deep learning,

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