Verification (free-running = self-generated latents)

• organism = 1.000; ablate thread-start->prompt = 0.074 (chance — the trains' only input).
• per-room corruption (noise into each room position): 0.12/0.12/0.33 (vs organism 1.00) — every position of every NL train is load-bearing.
• parallel: K=3 trains; each a contiguous M-position cohesive NL span. Generalization: held-out (fresh instances) = 1.000/1.000 (no memorization); depth (more steps than trained) = +1=1.00, +2=1.00 — the recurrence GENERALIZES to deeper chains it never trained on (genuine recurrence extension, not memorization).

Controls

Shuffle and cross-patch both collapse to chance (0.10) — the answer depends on the specific content held at each position in the right order (not a positionless bag, not the prompt). This is the signature of genuinely load-bearing latents.

Probing across layers and positions

A linear (ridge) probe decodes each latent position's own task value from its residual stream at every layer. The per-position state is linearly readable, peaking at layer 4 (mean decodability 1.00 across positions; chance 0.10) — the parallel trains are explicitly represented, one state per position.