R12 Counterfactual Cursor-Action Neural Result

Decision: neural_selector_no_go

Claim boundary: This is a negative result for the frozen final-block, head-zero Q-sidecar realization and its orbit-interchange training protocol. It does not prove that a learned cursor controller is impossible. It does reject promotion of this adapter, this placement, and this confirmation result.

1. Frozen evidence chain

• Implementation commit bound by the canary: 4dfcec195477c23d9e88276d58030d373bc2db6c
• Canary SHA-256: baf985855c396f63dffba1e09733a7372bd8b29c852cb5b9f482b4d59de714a1
• Independent canary-audit SHA-256: 5deb9dc396e3c8d99f32b9f0e14482d288cff9d82145582665569c911a802e5d
• Raw 260k base SHA-256: 91d5288f184fc5230516add9851ac1a8815d3369ffd816cd7d0c03d8bafc741d
• Six-arm training job: Newton 689932, completed on evc30 in 3m23s.
• Training-manifest SHA-256: 8c499c215ade7be26ac75ea4e50cc5b335edd80f3bd5eb2225eb0166eb5bc13e
• Score-blind evaluation array: Newton 689936, tasks 0--5, all completed without restart on evc30.
• Independent score SHA-256: 88a5e0e86cd4228fe3dd82282efae910fb36634e097f7e4de599d7c008315cc0
• The six adapters, six raw inference files, six receipts, and training manifest were mirrored byte-for-byte, committed, and pushed as 51d2cd4 before the independent scorer was invoked.

Every arm trained for four epochs and 1,152 updates. Each evaluation contains 4,800 confirmation cells, three cursor conditions, and 450 full-model forward batches. The scorer independently re-hashed the base, canary, audit, training manifest, adapters, evaluator code, raw artifacts, and receipts, then ran the frozen 20,000-replicate paired cluster bootstrap.

2. Preregistered selector result

The treatment has zero advantage over ordinary loss and relation sham under both full-vocabulary and restricted scoring. Both observed exact-group differences and both simultaneous one-sided lower bounds are exactly zero. All preregistered selector checks fail. Atomic execution and one-call DONE/EOS remain pending because a selector failure cannot advance to those gates.

3. Mechanistic diagnosis

This is an actuation and binding collapse, not a serialization failure.

1. The sidecar changes logits but not decisions. For orbit interchange, canonical-versus-five-cycle cursor intervention changes the five restricted logits by mean L-infinity 0.1035 and maximum 0.5000, yet changes zero of 4,800 restricted predictions. Canonical-versus-clamped changes them by mean 0.0823, also with zero prediction switches.
2. The full-vocabulary margin is much larger. The full-vocabulary winner is above the best action token by mean 2.7450, median 2.5366, and 95th percentile 4.8438 logits. The cursor effect is therefore roughly 25 times smaller than the typical margin it must cross.
3. More cursor-table capacity does not repair placement. The favorable 512-scalar table has the same 20% restricted and 0% full-vocabulary result. The failure is not specific to the centered three-bit factorization.
4. The final-block single-head Q path has weak gradient leverage. Treatment mean full-vocabulary CE falls only from 6.72330 in epoch 1 to 6.70808 in epoch 4. Mean adapter gradient norm falls from 0.00201 to 0.00142 while the projection norm grows to about 62. Because the delta is inserted before QK normalization, growing its norm mainly saturates query direction; it does not create a direct write channel into action logits.
5. A stronger text perturbation still does not bind source and cursor. The 640-scalar text LoRA changes canonical-versus-cycle restricted logits by mean L-infinity 1.0407 and flips 870 restricted predictions, but remains at 20.27% accuracy with zero exact groups. Logit leverage alone is insufficient without the correct source-by-cursor interaction.
6. DONE is never learned by the matched sidecars. Restricted accuracy at cursor four is 0/960 for the four 192-scalar arms and cursor table. This independently blocks autonomous termination.

The strongest supported statement is:

Orbit-interchange supervision did not install a usable causal selector through one final-block query head. The frozen base exposes some cursor-sensitive logit movement, but this interface cannot bind operation order to cursor strongly enough to overcome vocabulary competition.

4. Next admissible gate

Do not tune this score, reuse this confirmation as development data, move the same sidecar to another layer, or increase its size post hoc. The next bounded experiment must separate representation availability from vocabulary actuation on the already designated development split:

1. Fit a source-by-cursor tensor-product readout from frozen pre-final hidden states to the five action classes, with source-only and cursor-only collapse controls. This is a diagnostic probe, not a reasoning mechanism.
2. If joint development accuracy is high, test a separately accounted SELECT-only action-logit valve that writes the five readout scores into the corresponding existing vocabulary logits. This determines whether the remaining failure is the write interface rather than the representation.
3. If the development probe fails, stop. The frozen representation does not expose the required joint variable to a small readout and another decoder fit is unjustified.
4. If both development gates pass, freeze one implementation and generate a fresh operand/renderer confirmation split before any new score-bearing run. The exposed v1 confirmation may not be used to choose ranks, layers, thresholds, or losses.

The tensor-product probe and logit valve are known classifier/gating machinery; they carry no primitive-novelty claim. Their purpose is to locate the missing causal interface precisely enough to decide whether the independent R12 training-protocol track is still viable.