Docs: Feynman-style blog draft, MIT license, requirements, results.md, README rewrite with repro steps; reviews -> docs/reviews

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-# Review: Claude Opus 4.6 (agy, 2026-07-31)
-
-Adversarial review of the J-space replication project. Full brief: /tmp/jspace_brief.md.
-
-## Headline
-
-**The implementation computes the wrong quantity.**
-
-Anthropic's J-lens vectors are rows of `W_U · E[∂h_final/∂h_ℓ]` — the Jacobian stops at
-the final *residual stream*, before softmax. Our code (jlens_v2.py) differentiates through
-`log_softmax`, which folds in a `(1 − p(k))` factor that mechanically anticorrelates norm
-with frequency. The r = −0.65 may be an artefact of this difference, not a property of the
-model's representations.
-
-## Q1
-
-The 67% causal drop and the min(V,d) rank law both follow from the softmax gradient
-mechanics and linear algebra respectively — neither requires a "workspace" explanation.
-The causal experiment doesn't control for the softmax saturation confound.
-
-## Q2
-
-The cheapest kill-or-save experiment: compute J-lens BOTH ways (our `∇ log p` vs
-Anthropic-faithful `∇ h_final` composed with `W_U`) on the *existing* trained model.
-If the correlation vanishes with the faithful method, the thesis is dead.
-~30 min of compute, zero retraining.
-
-## Q3
-
-gpt2_jlens.py also has a norm-averaging bug: it accumulates `E[‖∇‖]` (average of norms)
-rather than `‖E[∇]‖` (norm of average), which are different quantities by Jensen's
-inequality.
-
-## Q4
-
-Frame as "open confounds to control," not "refutation." We haven't faithfully replicated
-their method, and we haven't addressed any of their functional experiments (steering,
-verbal report, reasoning ablation).
diff --git a/reviews/2026-07-31-gemini-3.1-pro.md b/reviews/2026-07-31-gemini-3.1-pro.md
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-# Review: Gemini 3.1 Pro (agy, 2026-07-31)
-
-Adversarial review of the J-space replication project. Full brief: /tmp/jspace_brief.md.
-
-## Q1. Falsification & alternative explanations
-
-The core claim — that Anthropic's "limited capacity workspace" is merely an artifact of
-unigram token frequency and the min(V, d) linear algebra rank bound — is falsified by a
-fundamental misunderstanding of both the gradient mechanics and space dimensionality.
-
-### Why the 67% causal drop occurs (softmax gradient mechanics, not workspace falsification)
-
-The J-lens vector at the output layer L for token k is defined as the expectation over
-contexts x:
-
-    J_L(k) = E_x[ grad_{h_L} log p(k|x) ] = W_{U,k}^T - sum_j E_x[p(j|x)] W_{U,j}^T
-           = W_{U,k}^T - W_avg
-
-where W_avg is the unigram probability-weighted average unembedding vector across the corpus.
-
-When 'q' frequency is doubled by inserting it at random positions (freq_experiment.py):
-1. Unigram baseline elevation: p_q increases across all context positions. In the
-   subtraction term -sum_j p(j|x) W_{U,j}, the component -p(q|x) W_{U,q} grows larger
-   across every single non-'q' position in the batch, directly canceling out W_{U,q}.
-2. Logit bias inflation: to minimize cross-entropy loss on uniform random insertions,
-   the model increases the scalar output bias b_q, raising p(q|x) globally and driving
-   (1 - p(q|x)) -> 0 faster during evaluation.
-
-Furthermore, freq_ablation.py compares a newly trained model against a pre-existing
-checkpoint (out-shakespeare-char/ckpt.pt) trained by a different script with unmatched
-seeds and training iterations.
-
-### The min(V, d) rank fallacy
-
-A matrix V in R^(V x d) has an absolute mathematical rank ceiling of min(V, d).
-
-In our setup (V=65):
-- d=16: rank 14-15 (~94% of the min(65,16)=16 ceiling)
-- d=32: rank 26-31 (~90% of 32)
-- d=128 and d=384: rank 65 (~100% of 65)
-
-Our results show nanoGPT J-vectors occupy almost 100% of the ambient dimension available.
-There is ZERO low-rank workspace compression in the model. In contrast, Anthropic
-evaluated models where V=50,257 and d=768 or 4,096. If Anthropic's finding were a
-min(V,d) linear algebra triviality, their J-space effective rank would be
-min(50257, 768) = 768. Instead they observed a rank of 10-50 << 768 << V.
-
-Our experiment proved that nanoGPT fails to form a compressed J-space, not that
-Anthropic's compression finding is a linear algebra illusion.
-
-## Q2. Most informative single experiment
-
-Frequency-Matched Synthetic Pair Test (Contextual Predictability vs Unigram Frequency).
-
-Design: train nanoGPT (or evaluate GPT-2) on a corpus containing two synthetic tokens,
-T_struct and T_noise, with IDENTICAL unigram frequencies (e.g. exactly 0.1% each):
-- T_struct: appears strictly in specific structured syntactic templates (e.g. after a
-  fixed 3-token trigger sequence A B C -> T_struct)
-- T_noise: injected at uniform random positions
-
-Expected outcomes:
-- Under frequency-only hypothesis: identical J-lens norms across all layers.
-- Under workspace/verbalizable-representation hypothesis: T_struct maintains high
-  J-lens norm in intermediate layers; T_noise collapses to near zero.
-
-## Q3. Implementation & methodological audit
-
-1. NORM OF EXPECTATION vs EXPECTATION OF NORM (critical bug):
-   - jlens_v2.py computes ||E_x[grad]||: averages gradient vectors first, then takes
-     L2 norm. At the final layer this reduces to ||W_{U,k}^T - W_avg||, stripping all
-     context-dependent dynamic activation variance.
-   - gpt2_jlens.py computes E_x[||grad||]: takes the norm on each batch step before
-     accumulating.
-   - Comparing nanoGPT to GPT-2 compares two mathematically distinct quantities.
-
-2. Severe underpowering & silent exception suppression:
-   - gpt2_jlens.py: n_batches=3 with seq_len=32 -> only 96 token positions to estimate
-     gradients over a 50,257-token vocabulary.
-   - Lines 93-94: bare `except: pass` silently discards failed backward passes.
-
-3. Residual capture point:
-   - jlens_v2.py registers a forward hook on model.transformer.h[layer_idx]; in nanoGPT
-     this captures the block output AFTER both attention and MLP residual additions.
-     Verify layer indices match Anthropic's definition (pre-block vs post-block).
-
-## Q4. Scrutiny-surviving framing
-
-"When evaluating the Jacobian Lens (J-lens) on small character-level transformers
-(V=65), J-lens vector magnitudes exhibit a strong inverse correlation with unigram token
-frequency (r = -0.65), and gradient norm reductions can be induced by artificially
-inflating token priors. This highlights that raw J-lens norms in small-scale models are
-heavily confounded by static unembedding geometry (W_{U,k} - W_avg) and baseline unigram
-predictability. However, we do NOT claim to refute Anthropic's Global Workspace hypothesis
-or their low-rank J-space findings (10-50 active dimensions). Because V < d in our
-character-level baseline, J-vectors span the full ambient rank (~min(V, d)), demonstrating
-that toy models fail to exhibit the severe subspace compression (10 << d << V) observed in
-large language models rather than disproving its existence."
diff --git a/reviews/2026-07-31-gpt-5.6-terra.md b/reviews/2026-07-31-gpt-5.6-terra.md
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-# Review: GPT-5.6-Terra (codex, 2026-07-31)
-
-Adversarial review of the J-space replication project. Full brief: /tmp/jspace_brief.md.
-Codex read the repo and the paper (43,704 tokens used, no files changed).
-
-## Q1. Strongest alternative explanation
-
-The central comparison is not currently about Anthropic's J-lens. jlens_v2.py estimates
-
-    E[ grad_{h_l,t} log p(k | x, t') ]
-
-summed over future output positions, whereas the paper first estimates the token-
-independent matrix E[ ∂h_final,t' / ∂h_l,t ], then applies final normalization and the
-unembedding row for token k. That distinction is fatal for the frequency claim.
-
-The gradient of log probability contains a softmax/calibration term:
-
-    ∇ log p_k = ∇ z_k − Σ_j p_j ∇ z_j
-
-Thus its norm measures the local sensitivity of the *log probability* of token k,
-including output-head geometry, prediction confidence, final LayerNorm's state-dependent
-Jacobian, and cancellation across contexts. It is not a token's "amount in J-space."
-A token can have a small mean gradient because its effects vary in direction by context
-and cancel — not because it is less verbalizable or displaced by a capacity limit.
-
-The `q` intervention is especially confounded. It does not merely double a sufficient
-statistic while holding the data-generating problem fixed: it inserts `q` at random
-character positions, changing sequence length, every downstream absolute position, local
-n-grams, and the conditional distribution of both `q` and its neighbors. In Shakespeare,
-`q` is unusually structured; random inserted instances largely destroy that structure.
-The 67% drop could therefore reflect a different learned conditional-prediction circuit or
-gradient alignment, plus an unmatched training run — not frequency per se.
-
-The rank result is even less probative. A matrix of 65 token-indexed vectors in d
-dimensions necessarily has rank at most min(65,d). Observing near-maximal numerical rank
-under a 1%-of-top-singular-value cutoff shows that these particular gradient vectors are
-reasonably nondegenerate; it does not show that the model's workspace capacity equals that
-bound. Anthropic's capacity claim is about sparse nonnegative decomposition of
-*activations*, occupancy above random-direction controls, and explained variance at
-individual positions — not the global linear rank of the token-vector dictionary. The paper
-explicitly notes that the token vectors may span all of residual space; J-space is defined
-by sparse use, not a low-rank span.
-
-A further comparability problem: nanoGPT computes norm of the mean gradient, while
-gpt2_jlens.py averages norms of per-batch full tensors. Those answer different questions,
-so the two correlations cannot jointly support one mechanism.
-
-## Q2. Best single experiment
-
-Run a matched, multi-seed **loss-reweighting plus faithful-lens** experiment.
-
-Train paired models from identical initializations and identical minibatch order on the
-unchanged Shakespeare sequences. In one member of each pair, multiply cross-entropy terms
-whose target is `q` by 2; in the other, use ordinary loss. This changes the effective
-target frequency/importance without injecting malformed `q` contexts or shifting all later
-positions. Use at least 5-10 paired seeds. Also include a same-total-loss control that
-upweights randomly chosen non-`q` target positions.
-
-For each model, compute both:
-1. the present score, ||E ∇ log p(q)||; and
-2. a faithful token vector from the averaged final-residual Jacobian, followed by the
-   unembedding as in the paper.
-
-Report q probability, conditional entropy, unembedding-row norm, vector cosine similarity,
-and bootstrap confidence intervals. Cheap because it reuses the small-model setup and
-directly separates "measurement artifact" from learned representation.
-
-## Q3. Faithfulness of jlens_v2.py
-
-No: it is directionally related to a Jacobian method, but it is not faithful enough to
-validate numerical comparisons with the paper.
-
-Good news: the forward hook captures the output of the selected nanoGPT block (the
-post-attention/post-MLP residual stream), a reasonable source capture point. Summing
-gradients over source positions and all output positions also includes the causal
-future-position dependence the paper intends — masked-impossible pairs have zero gradient.
-
-The methodological error is the target. The paper defines one d_model x d_model average
-Jacobian from intermediate residual stream to FINAL residual stream, then reads it through
-the model's normal output operations. Our code differentiates log_softmax(logits) directly.
-That folds the final LayerNorm, unembedding, and token-dependent softmax subtraction into
-the object being averaged. Averaging after these nonlinear/token-dependent operations is
-not equivalent to averaging the residual Jacobian and then reading it out.
-
-Two lesser issues: dividing by B*T rather than the number of valid source-future pairs
-changes scale (though not within-run rankings at fixed sequence length); 10-20 random
-batches is a noisier approximation than the paper's corpus-scale averaging (precision, not
-core invalidation).
-
-## Q4. Defensible blog framing
-
-"We found that a simple gradient-of-log-probability proxy on a 10.6M character transformer
-is strongly associated with token frequency, and that its token-indexed gradient dictionary
-has the expected rank ceiling min(V,d_model). A random-insertion intervention is consistent
-with frequency or conditional-prediction structure affecting this proxy, but it does not
-isolate frequency, and our current estimator differs materially from Anthropic's
-residual-Jacobian J-lens. These results motivate a controlled replication using the
-faithful lens and activation-level sparse-occupancy tests."
-
-Do NOT claim to have falsified Anthropic's workspace evidence, shown that its capacity
-result is "just linear algebra," demonstrated a consciousness-relevant conclusion is wrong,
-or shifted any burden of proof. Anthropic's headline rests on functional interventions,
-sparse occupancy, variance controls, and broadcast/generalization tests in addition to
-rank; our current experiments test none of those.