Subgoal-swap results: the slot reads the words — and also just likes being fed words

2026-08-09 ~03:5xZ. Results for the pre-registered subgoal-swap content read — the closer of the presence / channel / content triangle from the consolidated #6 report §6.1. Zero training; one greedy full-panel pass of AR-100k (bijou_arb_rcond_100k_ddp4/step_100000) on the shared k4l2 plan with every frame’s true segment label replaced by a different episode’s (seed-0 within-dataset Sattolo derangement — format-valid, content-wrong), paired per-frame against the banked planner-less baseline 5.8026 / 2.1431 and the banked oracle arm (neither re-run). Reads produced by fontaine/scripts/subgoal_swap_results.py (oracle-gated: exact-arithmetic fixtures + 10 abort branches pre-data; all execution oracles green on the real artifacts before any scalar below was quoted).

TLDR

Both mechanisms are real, so no single row of the frozen table fires — this is the pre-registered MIXED outcome, recorded without a decision. Feeding the slot wrong-but-plausible words still beats feeding it nothing: Δ_swap = −0.113 chunk MAE [CI95 −0.161, −0.060], entirely below zero. But the truth beats wrong words by clearly more: paired on identical frames, swap − oracle = +0.166 [+0.127, +0.205], entirely above zero (banked Δ_oracle = −0.290).

In decomposition terms: of the −0.29 the oracle-truth slot is worth, roughly 40% is a format/prior effect (any plausible segment-label text regularizes the decode) and 60% is content (the words being right is most of the value). The pre-reg’s two clean escalation verdicts both miss: the scorer ladder is not chasing a pure format mirage (content is consumed — row 2 does not fire), but wrong content is also not neutral-or-harmful (rows 1 and 3 don’t fire either; wrong words still help). What a scorer could ever buy is bounded by the content margin (~0.17), sitting on a format floor (~0.11) that costs nothing to reach.

What ran

Four phases in one transient unit (fontaine-subgoal-swap, launched 02:13:47Z, rc=0 03:42:36Z, ~1.5 GPU-h ≤ 3 gate):

1. Live-oracle selftest (all abort branches fired green);
2. Identity run — full panel with ALL swap plumbing live but every episode its own donor. Oracle (ii), the keystone: it byte-reproduced the banked oracle-arm npz over all 25,800 rows, every shared column — certifying the swap machinery changes nothing but the text in the slot;
3. Swap arm — same panel, seed-0 derangement (_swapsubgoal);
4. Mechanical dump check — oracles (i)+(iv) over all 25,788 per-frame swap records: derangement bijective, no identity mappings, every rendered text equal to the donor episode’s fraction-matched label. 25,788/25,788 labeled panel rows swapped, 0 empty-slot renders, 0 datasets skipped.

The reads (frozen semantics, one command)

Pooled: swap 5.690 / 2.120 vs baseline 5.8026 / 2.1431 vs oracle 5.512 / 2.090. All 17,204 core frames paired; the labeled subset is 17,192 of them (12 label-less rows decode untouched by construction — oracle (iii)).

The horizon fingerprint is the interesting part

The pre-reg predicted a pure format effect “should be horizon-flat”. It is not flat — and that refines the picture rather than muddying it:

• oracle − baseline: first-10 mean −0.081 → last-10 −0.480 (the banked −0.464-shaped late-horizon signature, reproduced);
• swap − baseline: first-10 −0.041 → last-10 −0.175 — the same shape at ~36% of the amplitude.

So the late-horizon dive is not exclusively a truth-reading effect. Two readings are compatible and this arm cannot split them: (a) the format/prior effect itself compounds over the horizon (a conditioned decode drifts less, right or wrong), or (b) wrong-episode text from the same dataset is still partially right content — “reach toward the object”-class instructions transfer across episodes. Which leads to the one caveat worth stating loudly:

Coincidence caveat (recorded, not adjudicated): 2,162 of the 25,788 swapped rows (8.4%) drew a donor label textually identical to the frame’s true label — short generic labels recur across episodes. Those rows are “swap” in provenance but true in content, so the measured Δ_swap slightly overstates the pure-wrong-words effect and +0.166 slightly understates the content margin. The direction of the conclusion is unaffected (the bias runs against the content reading, which won anyway).

What this feeds

Recorded against the frozen table, no decision row fires — per the pre-reg the fallback is record-only. What it settles for the #6 escalation map:

• Learned-scorer escalations remain coherent — the slot consumes content (row-2’s “format mirage” deprioritization does NOT trigger). But their realistic prize is the ~0.17 content margin, not the full −0.29 bound, because ~0.11 comes free with any plausible words.
• The cheap floor is real and nobody has to pick words well to get it. Worth remembering when costing scorer rungs: the do-nothing comparator for any subgoal-selection scheme is now “feed it anything plausible”, not “feed it nothing”.
• The rung-(a) verdict stands unchanged: self-generated subgoals (−0.018) recover almost none of either component at ~3× decode cost — generation quality, not the slot, stays the bottleneck.

Not licensed by this read (each needs its own pre-reg): any scorer escalation, a coincidence-excluded re-read, cross-dataset swaps, Molmo2-side swap arms.

Cost: ~1.5 GPU-h of the ≤ 3 gate (identity + swap + checks). Artifacts: panel_k4l2_swapsubgoal.{npz,json,html} + 25,788-row swap dump + analysis__subgoal_swap_ar100k_k4l2.json, all banked.