R12 Raw-300k Future-Jacobian Workspace Longitudinal Preregistration

Status: COMPLETED, SEMANTIC GATE FAILED. Jobs 690028, 690030, and 690031 executed the frozen repeat. The selected raw-300k future-Jacobian readout has zero top-10 and zero top-100 accuracy on all 2,304 language/full targets, so no coordinate swap or raw-workspace mechanism is authorized. See R12_JACOBIAN_WORKSPACE_LONGITUDINAL_RESULT.md.

1. Question

The raw-200k exact future-Jacobian map was reproducible but semantically unusable on the frozen referential board: future-Jacobian MRR exceeded the immediate-logit MRR in the rank tail, while both methods recovered zero of 2,304 language/full concept targets in the top 100. No coordinate swap was authorized.

The July 2026 global-workspace study explicitly leaves model-size and pretraining-emergence scaling unresolved. Raw 300k therefore receives one strict longitudinal repeat:

Under identical code, prompts, seeds, layers, target board, and decision gates, did another 100k pretraining updates create a semantically readable future-causal workspace in Shohin?

This is a measurement of an existing method, not an R12 invention.

2. Immutable model and inputs

The checkpoint is read-only and model-only. No optimizer or training state is needed because this diagnostic freezes every parameter.

3. Exact repeat contract

Two independent averaged future-residual Jacobians use the existing committed implementation train/jacobian_workspace.py with:

source layers = 5,9,13,17,21,25,28
target layer = 29
prompt count = 8 per fit
prompt seeds = 20260714 and 20260715
maximum sequence length = 128
skip first = 16
dimension batch = 8

The same prompt seeds intentionally reproduce the raw-200k prompt samples so the checkpoint comparison is paired. The two samples remain disjoint from each other. The existing committed train/eval_jacobian_workspace.py scores the same 896 examples and selects only among layers 13, 17, 21, and 25 by the frozen language/full MRR-gain rule.

Fresh outputs are exactly:

artifacts/diagnostics/jacobian_workspace_raw300k_p8_v1.pt
artifacts/diagnostics/jacobian_workspace_raw300k_p8_v2.pt
artifacts/diagnostics/jacobian_readout_raw300k_p16_v1.json

No raw-200k development row, 300k transcript, or output token may alter the implementation, layers, seeds, labels, selected-layer rule, or gates.

4. Frozen gates

The original gates are retained without relaxation:

1. the two raw-300k lens matrices have whole-matrix cosine at least 0.90 at every fitted layer;
2. at the predeclared selected layer, future-Jacobian language/full MRR is at least 1.25 times immediate-logit MRR;
3. future-Jacobian language/full top-10 accuracy exceeds immediate-logit top-10 accuracy by at least 10 percentage points.

Only all three permit a separately preregistered causal swap. Top-100 or MRR improvement without the top-10 gate is a semantic failure, exactly as at 200k.

The longitudinal report also records, without changing the decision:

• raw-200k versus raw-300k MRR, top-10, top-100, and median rank;
• cross-checkpoint matrix cosine and right-singular-subspace overlap by layer;
• exact artifact hashes and Slurm logs.

5. Interpretation boundary

• Fail: raw next-token pretraining did not make this exact J-lens semantic workspace readable by 300k. Close raw-Jacobian swaps at this checkpoint.
• Pass: only authorizes a fresh, bidirectional, donor-swap causal preregistration with zero, shuffled, norm-matched, and non-Jacobian controls.
• Either result: does not establish hidden thinking, autonomous reasoning, context compaction, or a new mechanism.

The paper reports the workspace primarily in much larger production models and states that smaller models may have a smaller, less reliable, or absent one. Shohin must earn the causal premise rather than inherit it from model scale.