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
4 days	Fix in-word metric: strict (both-sides, 58% random / 0% clean) vs touches ↵	Void Agent
	(95%); blog/results caveat corrected to the strict discriminator
4 days	synthetic_pair: add --clean boundary mode (noise token at word starts) + ↵	Void Agent
	configurable dirs; prep reports in-word fraction
4 days	Verify against Anthropic's official jacobian-lens repo: estimator match, ↵	Void Agent
	zero frequency in their code+data, add --skip_first/--source_mean mirror mode
4 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
4 days	Add src/stats_decomp.py: reproducible W_U decomposition, p-values, Spearman, ↵	Void Agent
	synthetic-pair CI (backs results.md numbers)
4 days	Document n_pairs denominator (exact count) and ctrl_random gradient-mass ↵	Void Agent
	caveat (comment-only)
4 days	loss_reweight: pass Y to forward for full logits (Karpathy nanoGPT returns ↵	Void Agent
	last-position logits when targets=None); regression test
4 days	loss_reweight: CPU generators for randint/randperm (CUDA generators ↵	Void Agent
	unsupported on torch 2.4); cuda-path verified
4 days	Add causal experiments: synthetic frequency-matched pair (Gemini design) + ↵	Void Agent
	loss-reweighting (Codex design); jlens_v3 output_dir + last-layer device fix
6 days	jlens_v3: chunk VJP probes (16) to fit K2200 4GB; proxy chunk fix; e2e ↵	Void Agent
	verified (last-layer cos-sim=1.0)
6 days	Add faithful J-lens (jlens_v3): W_U-probed residual Jacobian per paper; ↵	Void Agent
	both-ways comparison vs log-softmax proxy; 3-model adversarial reviews
7 days	GPT-2 J-lens: use local Shakespeare + fix summary	Void Agent

7 days	GPT-2 Small J-lens: frequency + dimensionality sampling	Void Agent

7 days	Add Pythia-70m test script	Void Agent

7 days	Fix: lr→learning_rate in configure_optimizers	Void Agent

7 days	Add dimensional starvation experiment (d_model=16,32,64,128 vs 384)	Void Agent

7 days	Add controlled frequency ablation experiment (doubled-q)	Void Agent

8 days	jlens_v2: fix import path for src/ -> root layout	Void Agent

8 days	Add jlens_v2.py: simplified gradient approach for J-lens	Void Agent

8 days	jlens.py: fix hooks — don't detach activations, preserve gradient graph	Void Agent

8 days	jlens.py: fix load_model for dict-format nanoGPT checkpoints	Void Agent

8 days	Fix jlens.py import path: src/ -> project root	Void Agent

8 days	Restructure: nanoGPT at root, custom code in src/	Void Agent
	- Move model.py, train.py, configurator.py to root for nanoGPT compatibility - data/ and config/ directories at root with Shakespeare dataset prep scripts - src/jlens.py updated to import model from project root - Cleaned up stale src/config/ and duplicate src/ files - Fixed .gitignore: exclude out-shakespeare-char/ instead of raw data dirs
8 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.