jspace-nanogpt.git - J-space on nanoGPT: Jacobian Lens replication & critique. J-lens norms anti-correlate with token frequency (r ~ -0.61 to -0.69); frequency-matched synthetic pair + causal loss-reweighting tests.

Age	Commit message (Collapse)	Author
39 hours	at-scale W_U row-norm check (GPT-2): geometric confound generalizes, ↵main	Void Agent
	r(log10)≈-0.45/-0.49; blog draft: drop 'Actually', log-freq lead, probe transparency, norm-vs-readout concession
5 days	Address Luna repo review: dynamic repo root (no hardcoded cwd), J-lens ↵	Void Agent
	correctness test (last-layer identity), auditable fact-check, mechanism narrative fix, softened conditional-predictability claim, README data-prep + artifacts note
5 days	Docs: Feynman-style blog draft, MIT license, requirements, results.md, ↵	Void Agent
	README rewrite with repro steps; reviews -> docs/reviews
9 days	Initial project setup: J-lens implementation for nanoGPT	Void Agent
	- Core J-lens computation (batched per-layer, per-token gradient method) - Project README with background and experiment plan - Sync script for meru Docker container deployment - Upstream nanoGPT model code copied to src/ Architecture: Computes d(log_p(token))/d(residual_stream) averaged over corpus contexts, replicating Anthropic's Jacobian Lens technique.