Add willkn GreaterWrong corroboration: independent GPT-2-medium finding that raw J-lens misweights structural (high-frequency) tokens + full-rank Jacobian; shrinkage J+lambda I as untested next experiment

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@@ -69,6 +69,30 @@ experiment + evaluation data) finds ZERO occurrences of frequency/unigram/
 token-count terms. Their README confirms the fit corpus is "a generic web-text
 corpus" — no frequency balancing.
 
+## 1c. Independent corroboration (willkn, GreaterWrong, GPT-2-medium)
+
+An independent engineering analysis of the paper (willkn, GreaterWrong, 24 Jul
+2026; notebooks: github.com/willkn/jlens_re; full text: docs/willkn-jlens-
+greaterwrong-analysis.md), on GPT-2-medium (355M, d=1024, L=24, V=50,257),
+found:
+- the raw fitted Jacobian "misweights structural tokens (grammar, punctuation)
+  over semantic content" — dominant spectral channels carry ~10x the gain of
+  the residual pathway. Structural tokens ARE the high-frequency tokens; this
+  is the same distortion we measure as frequency anti-correlation, at 30x our
+  model scale and via a different metric (next-token faithfulness of the
+  readout).
+- the Jacobian is essentially full-rank: 562-858 dimensions capture 90% of
+  spectral variance, "leaving little room for compression" — corroborates our
+  dimensional-starvation / full-rank result at scale.
+- a single-parameter shrinkage regularizer (J + lambda*I) monotonically
+  recovers next-token faithfulness (beats logit lens at layer 12: 0.294 vs
+  0.275). Anthropic's released fitting code applies no such regularization.
+- measured noise law rel_F(J_n) ~ c_l/sqrt(n), c_l growing ~4x from layer 20
+  to layer 4; per-backward cost ~5ms + 9ms/layer on L4 at dim_batch=8.
+
+Untested (natural next experiment): does shrinkage also remove our frequency
+correlation?
+
 ## 2. Frequency-matched synthetic pair (`src/synthetic_pair.py`)
 
 Two new characters at identical 0.1% unigram frequency in Shakespeare: