Multi-input affine experiment (control arm — 1 slot, no affine)

• Experiment arm: 16 repeated injection markers, each with its own learned affine A_i·v_norm + b_i over the normalized injected activation (identity-init, fp32, +269M params). Affines and backbone trained.
• Control arm: 1 marker, no affine (status quo). Same warm-start, data, and budget.

Result — held-out NLL (lower is better)

Honest interpretation (read this)

• Effect size is small: +0.0165 nats ≈ 1.1% relative (ppl 4.18 → 4.11).
• Repetition is confounded with the affine. The experiment changes two things at once vs. control: 16× marker repetition and the learned affine. This run cannot separate them. Inspection of the trained affine shows each slot moved only ~4–6% (Frobenius) from identity — so much of the behavior is still "inject ~v_norm into 16 slots" (pure repetition), with the affine a small perturbation on top. A "16 markers, no affine" arm is required to isolate the affine's contribution.
• The slots did learn distinct transforms: across-slot divergence ‖W_i − W̄‖ is nearly as large as each slot's deviation from identity, i.e. the 16 affines are mostly unique, not a shared shift — partial support for the "independent views" idea, but not proof it's what helps.
• Single training seed. The CI reflects eval-row noise only, not training variability; a re-trained pair could shift. A +1% gap is within plausible seed-to-seed range for 2.1-epoch continued SFT on an 8B model.
• NLL is a proxy. This measures teacher-forced response NLL, not the NLA paper's verbalization/reconstruction-quality metrics (FVE, downstream evals).