021
THE MINDS
SECTION 01
ISSUE 001
OBSERVEDresearchnowconfidence / high
Explanations Earn Trust Through Intervention
An explanation becomes useful when it survives intervention. Identify the proposed mechanism, change it, predict the behavioral effect, and check whether unrelated capabilities remain intact. Shared causal benchmarks could separate internal models that support control from elegant stories that merely follow the output after the fact.
Why this idea is here
What the evidence establishes.
Causal abstraction provides a unifying intervention framework; sparse-circuit research demonstrates models where circuits can be enumerated and manipulated.
Source ledger
Read the sources.
- S01Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability
peer-reviewed theory paper / published 2025-04-01 / retrieved 2026-07-09
- S02Understanding Neural Networks Through Sparse Circuits
primary interpretability research / published 2025-11-13 / retrieved 2026-07-09