bind2_1e — the binding mechanism on the official entity-tracking surface

Results-only card. Nothing here is downloadable — no weights, no code, no configuration, no training corpus, no generator. This card publishes numbers, what was tested, and why it matters, and withholds the tooling that would let the result be rebuilt. It is a member of the bind-evolution line — the entity-tracking evolution of the bind2_1 binding architecture (the version suffix says so); for the full falsification timeline and how the members relate, see the bind-evolution hub.

What bind2_1e is

bind2_1e (~27.8M parameters) is the member that carries the causally-verified binding mechanism of bind2_1 onto the official BabyLM 2026 entity-tracking surface. It is a letter-suffix engineering extension of bind2_1 — the same binding primitive, with a readout shaped to the official entity/box-tracking question form. At the level this line publishes, the mechanism is described only as a binding mechanism with learned routing; the "how" is withheld.

The mechanism's pedigree comes from the bind2_1 campaign, which was pre-registered and frozen before any data existed. There the binding pathway was shown to be causally load-bearing: lesioning its state pathway removes ~99.3% of the deep-tracking advantage, and interchange patching flips ~96.4% of answers to a donor context's holder — the full system scoring roughly 69 points above every matched control. That campaign's frozen pre-registered verdict was NULL (a depth-interaction criterion that a later adversarial autopsy found ceiling-confounded), and its honest headline finding was limited effective depth. A separate parameter/scale scan across a ~6× parameter ladder (24M → 145M) later showed that the small-model form-change is not dissolved by adding parameters. bind2_1e is the follow-on question those results earned: does the mechanism actually transfer to the official exam when the interface fits?

What was tested, and why

Earlier in this line, bind2_0 established "no tax, no win": the binding architecture, trained as a plain LM on the real strict-small corpus, showed no zero-shot state-tracking advantage on the official exam — mechanism capability and benchmark transfer are separate questions, and conflating them is how a field fools itself. bind2_1e asks the transfer question the right way: train the causally-verified mechanism on a training surface it can actually use, then score it on the official entity-tracking items with fully learned routing — no test-time oracle assistance — and read the first checkpoint, once.

Everything was pre-registered and frozen before the run: the corpus ruling, the success/null bands, the shortcut-decomposition gates, the abort rules — with NULL as the default prediction.

Results

Two runs were scored. All entity figures are accuracy (%), chance ≈ 20%; every earlier model in this line and the published baselines sit at ≈19–21% on this benchmark.

The headline (official exam, version-consistent)

On the official 07-12 evaluation tree — the same exam version scored for both models, so the comparison is apples-to-apples — the mixed-diet run scores:

entity_tracking = 69.95 vs the monolingual baseline's 19.24

That is the ~50-point lift the mechanism buys on the official surface, measured under the official scorer across all three official subsets. It is the version-consistent headline: both numbers come from the same benchmark version, so the gap is not an artifact of comparing across scoring standards.

Mechanism-caliber transfer

Restricted to the two in-scope subsets the mechanism targets (regular + contents-move, 6,259 of 9,483 items; per-item scorer):

run	training diet	mechanism-caliber entity	note
boxes-only	synthetic box-tracking only	93.96	replicated to n=3 seeds: 93.96 / 94.12 / 94.22 (spread 0.26 pt)
mixed	35% box-tracking / 65% natural text, 30M tokens	94.54	+ real general-language scores (below)

Why 69.95 and ~94 are both true, and not interchangeable. The official caliber (69.95) spans all three official subsets — including the ambiguous-reference subset, which the frozen pre-registration placed outside mechanism scope — and uses the official scorer. The mechanism caliber (~94) restricts to the two in-scope subsets. The two calibers are materially different and are never swapped for one another; both are reported here so neither over- nor under-states what was shown.

The result is not a shortcut artifact

The pre-registered decomposition is the credibility core. On the items the benchmark's strongest audited shortcut heuristic cannot solve, the model still scores ~92.4–92.6%; the empty-container "gold = nothing" trap — the exact state-blind prior that produced this line's earlier self-refutation — scores 97.6% (boxes-only) / 98.94% (mixed); and the learned router matches oracle-hinted routing to the third decimal (routing gap = 0.000). The score is carried by state tracking, not by a positional or prior-abuse shortcut.

The architecture, not the diet

A standard attention-only transformer trained on the identical box-tracking diet scores 50.6% pooled — but only 23.2% ≈ chance on the shortcut-unsolvable subset, versus this model's **92.6%** on that same subset. On the same diet, a standard architecture floors on the items that require tracking. (Single seed — provisional.)

General-language ability survives the mixed diet

The mixed-diet run trained on 65% natural text, so its general scores are real measurements, not "n/a":

suite (official 07-12 tree, single seed)	baseline (27.4M)	bind2_1e-mixed
entity_tracking	19.24	69.95
BLiMP	64.35	59.92
BLiMP-supplement	58.55	57.96
COMPS	51.00	50.01
EWoK	50.70	50.24

BLiMP 59.92 sits 0.08 below a frozen no-tax gate of 60.0 → recorded TAX-OR-BUDGET, with attribution left open between a possible architecture cost and this run's 5×-smaller token budget (30M vs the baselines' 150M); neither is claimed. COMPS and EWoK sit near chance for both arms (difference not meaningful). On the official GLUE fine-tuning protocol the mixed-diet run wins 5 of 7 tasks over the monolingual baseline (MNLI, MRPC, MultiRC, QQP, RTE; baseline higher on BoolQ and WSC). So the mixed run is a competitive general model that also carries the tracking mechanism — not a single-trick specialist.

Why this matters

bind2_0 left the transfer question open with an honest negative ("no tax, no win"). bind2_1e closes the positive half of it: the binding mechanism demonstrably learns the hard structured-tracking task once the interface fits — ~94% at mechanism caliber, and a version-consistent ~50-point official-exam lift over the baseline (69.95 vs 19.24) — while the mixed-diet model keeps real, competitive general-language ability. The earlier gap was a missing interface, not a disproven mechanism. That converts an open hope into a bounded, measured result.

What this is not (four mandatory caveats)

1. Single seed is single seed — provisional until replicated. The boxes-only mechanism-caliber headline replicated across three seeds (93.96 / 94.12 / 94.22); the mixed-diet run and the matched-diet control are each single-seed and remain provisional.
2. Subset scope. The mechanism-caliber score covers the regular + contents-move subsets only — 6,259 of the benchmark's 9,483 items; the ambiguous-reference subset was excluded by the frozen pre-registration as outside mechanism scope.
3. This is a mechanism-transfer demonstration, not a general language model. The boxes-only run was trained solely on a synthetic box-tracking corpus; every other suite in the official evaluation is expected to sit at chance by design, and no claim is made there. (The mixed-diet run's general scores are real, but come from 5× less training data than the baselines.)
4. The natural-diet baselines are diet-confounded as an architecture comparison. Their chance-level entity scores come from natural-text training; the matched-diet architecture attribution rests on the bind2_1 campaign's matched controls plus the dedicated matched-diet control reported above — not on the natural-diet rows.

What is not released

At this stage, everything buildable is withheld: weights, code, configuration, and the synthetic training corpus and its generator. The single honest disclosure about that corpus: it is a synthetic box-tracking corpus whose answer statistics are distribution-matched to the official entity-tracking benchmark. The generation method is not published. The frozen pre-registration, the full decomposition, and the per-item results are on record internally; the numbers reported here are complete as reported — what is withheld is tooling and weights, not results.

Citation and context

This is a member card in the bind-evolution line, whose value is the sequence of pre-registered questions and frozen verdicts, not any single score. For the full timeline — including this line's refutation of its own earlier published result — see the bind-evolution hub. Cite this member at a pinned commit revision as stamped on this card at publish time.