Coverage Audits for LLM Interpretability Claims

A Mercury methods note arguing that sparse probing, activation patching, subspace intervention, and pruning claims should report a coverage audit before making broad localization claims. The audit uses 19 fullgrid manifests and shows a 1/7 pass rate for older sparse or coarse protocols versus 12/12 for per-channel quantile dense runs.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-06-18
DOI
https://doi.org/10.5281/zenodo.20729124
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
preprint
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Coverage Audits for LLM Interpretability Claims

Ho Yiing Chen
Zenodo (CERN European Organization for Nuclear Research)
Explainable Artificial Intelligence (XAI)
preprint

Coverage Audits for LLM Interpretability Claims

Ho Yiing Chen
preprint en

Abstract

A Mercury methods note arguing that sparse probing, activation patching, subspace intervention, and pruning claims should report a coverage audit before making broad localization claims. The audit uses 19 fullgrid manifests and shows a 1/7 pass rate for older sparse or coarse protocols versus 12/12 for per-channel quantile dense runs.

Zenodo (CERN European Organization for Nuclear Research)
Zero hunger
Explainable Artificial Intelligence (XAI)
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Coverage Audits for LLM Interpretability Claims — Ho Yiing Chen · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS