Mapping the Research Landscape of Agentic Interpretability
hhsun1 · x · 2026-07-16
This repost organizes the representative work in the agentic interpretability direction. The author notes that MAIA was the earliest related work they could recall, followed by several newer research projects and contributions from Goodfire AI, Liu et al., Elenal3ai, Arnauya, javifer96, and talhaklay.
The post highlights significant variations in research scope among these works—some focus on behavioral interpretation while others lean toward mechanistic interpretation; some operate at the feature level, others at the circuit level; and some prioritize discovery whereas others emphasize verification. The author argues that this field needs benchmarks and systematic evaluation just like other agent applications to track progress effectively. They also warn against data leakage/memorization, where a model might simply recall common explanatory facts from training rather than genuinely performing inference.
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