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THE MINDS
SECTION 01
ISSUE 001
OBSERVEDinstitutionnowconfidence / high
Replicate the Interpretation
Interpretability claims should ship with code, model hashes, intervention recipes, counterexamples, and replication tasks. Independent teams should test whether the same mechanism appears on held-out prompts and adjacent models; causal abstraction supplies shared terminology, while sparse-circuit research reports trained sparse models and concrete example circuits.
Why this idea is here
What the evidence establishes.
Causal-abstraction work provides a common formal language; sparse-circuit research reports trained sparse models and concrete example circuits.
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