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Locator and citation
locator
title:Quantifying LLM Attention-Head Stability (2026)
imported
2026-05-29T02:57:20.428237+00:00
extraction mode
manual_curation
authors
reviewer:will-blair
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observational · vf_182cb4f97418dc5d
theoretical · vf_841cfc4181456ee4
Attention heads in transformers exhibit bimodal stability: middle layers show <40% seed-consistent head mappings despite identical optimizer/architecture, while early and late layers exceed 70% stability, directly contradicting universality claims for circuits derived from single model instances.
Residual stream activations show 30-50% higher seed consistency than individual attention head activations across model refits, suggesting residual-level features rather than head-specific circuits provide more robust ground truth for circuit discovery and universality claims.
events
vev_7b3e7e963f0a0bdafinding.assertedManual finding added to frontier state
reviewer:will-blair · 2026-05-29
vev_b3ffb68e18e1115ffinding.assertedManual finding added to frontier state
reviewer:will-blair · 2026-05-29
reviewable changes
vpr_783bb6120c52239ffinding.addManual finding added to frontier state
applied · reviewer:will-blair · 2026-05-29
vpr_9dbf934c907e59d5finding.addManual finding added to frontier state
applied · reviewer:will-blair · 2026-05-29
evaluations
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