README.md

---

pretty_name: "bind evolution — a falsification timeline"
language:
- en
tags:
- babylm
- state-tracking
- linear-attention
- research-log
- negative-results
---


# bind evolution — a falsification timeline

**Research in progress. This repo is a narrative and navigation hub — not a leaderboard showcase, and not a

citation target for any single result.** To cite a result, cite the member repo that carries it, pinned to a
commit SHA (see [How to cite](#how-to-cite)).

This is the research log of a small-model architecture line ("bind") for **compositional state tracking**
at BabyLM 2026 Strict-Small scale (~24M params, 10M-word training corpus), told the only way we trust: as a
sequence of **pre-registered questions, frozen verdicts, and honest refutations — including refutations of

our own published results**. Every generation below either falsified its predecessor's headline claim or
was itself falsified by a frozen criterion. The value of the line is not any single score; it is that each
verdict — positive or null — forced a sharper next question.

Ground rules that hold throughout:

- **Frozen verdicts stand.** A pre-registered NULL is never re-labeled PASS, even when a post-hoc autopsy
  finds the criterion itself was flawed.
- **We audit our own wins adversarially.** The strongest refutation in this log was performed by us, on our
  own already-public result.
- **Negative results are published at the same resolution as positive ones.**

## What each test asked, why it was run, and what it buys — on one 8 GB laptop GPU

Every experiment in this program ran on a **single RTX 4060 Laptop GPU (8 GB)**. That constraint
shaped everything: single seeds where five were wanted, 30M-token budgets where the baselines got
150M, a serial queue where a lab would fan out overnight, and one scale scan that costs ~48
wall-clock hours here versus a fraction of that on a modern training node. Here is what each test
asked, why, and what its answer unlocks.

**1. Does the headline result replicate?** A single-seed result is an anecdote. The frozen
procedure was re-run on two untouched seeds: **93.96 / 94.12 / 94.22** — a 0.26-point spread
across three seeds. The transfer replicates; "provisional" was removed. *Unlocks:* the result is
load-bearing enough to build on.

**2. Is the advantage the architecture's — or just the training data's?** Any model can look good
on the diet built for it, so a standard attention-only transformer was trained on the *identical*
synthetic diet and scored on the same items. On the subset where state tracking is actually
required it **floors at chance (23.2%)** while the binding model scores **92.6%** there. *Unlocks:*
the advantage is an architectural asset, worth carrying to scale — not a data artifact.

**3. Does the mechanism survive real language in the diet?** A mechanism that only works on a pure
synthetic diet is a lab curiosity. Diluted to 35% of a mixed diet (65% natural text, same total
budget), the mechanism **held — 94.54%, slightly above the pure-diet run** — and the model's
general grammar score landed 0.08 points below a frozen "no-tax" line while training on **5× less

data than the baselines**. *Unlocks:* full-budget mixed training is the single most obvious next
run, and it is purely compute-bound.

**4. Does it already transfer to natural-language reference tracking?** The endgame is real
language, so the gap was measured instead of assumed: pre-registered **NULL**, and precisely
diagnostic — the mechanism's readout literally never fires on natural text. The gap is a **missing

interface, not a disproven mechanism**. *Unlocks:* the bridge to natural text is now a concrete, bounded workstream rather than a hope.

**5. Can the training scaffold be removed — or replaced by signals raw text can provide?**
*(answered 2026-07-17 — in halves.)* The recipe leans on training supervision the real world doesn't
hand out, so two pre-registered arms asked whether each supervision channel can be dropped; frozen
bands, NULL as the default prediction — and neither arm landed on the default. **One channel:

retained.** Dropping that supervision entirely holds the result —
pooled **94.12\%** against a frozen retention line of 88.96, statistically indistinguishable from
the original (93.96), with the shortcut-unsolvable subset at 92.6\%, empty-container items at
97.4\%, and a routing gap of 0.000. **The other channel: partial.** Removing it costs measurably —
pooled **81.50\%**, well above the frozen collapse line (59.4, the audited shortcut ceiling) but
below retention; the mechanism's gain survives removal (77.6\% on the shortcut-unsolvable items),
routing stays intact, and it degrades gracefully rather than collapsing the result. *Unlocks:* the supervision question splits cleanly — one channel is already
corpus-derivable, and the other is now a measured cost curve instead of an unknown.

**6. Will the mechanism emerge on its own when the data demands it?** *(answered 2026-07-17.)* The
deepest question in the line: the mechanism was forced by construction — would training pressure
alone produce it? A standard architecture was trained on a corpus engineered to strip the shortcut
reward (so the only way to lower the loss is to grow the real parse), and on a matched corpus that
leaves the shortcut in. **Frozen verdict: neither learned it** — both floor on the
mechanism-requiring subset (~0.14, below chance) while the forced mechanism scores 0.92 on the
identical test. Removing the shortcut-solvable pressure, under a generous budget, does **not** rescue
learning: pressure is refuted as a *sufficient* cause — the mechanism can be forced by
construction, but training pressure alone does not induce it. This is a publishable negative and a
precise one: it bounds where emergence does and does not happen under a generous budget, rather
than leaving "emergence" an open hope.

**7. Does parameter scale alone dissolve the gap?** *(resolved 2026-07-21 — the most

compute-starved experiment here.)* Re-trained across a ~6× parameter ladder (24M → 145M), the
standard control architectures stayed on their floor on **every** deep rung at **every** level:
none of the claim-bearing deep cells crossed the bar (up to 10× the campaign training budget at
the smallest and largest levels, 3.3× at the two middle levels).
Scale alone does not buy this capability — the form change is principle-grade, not small-model
sample efficiency. The controls neither climb toward the threshold nor decay toward chance as they
grow; they sit on a fixed, scale-invariant plateau. The claim stays bounded to the tested
budget × scale box, with no extrapolation to unlimited parameters. (The largest level was measured
at a matched 145M configuration, disclosed; the span is stated as ~6×.)

### The ask

The methodology is the guarantee: criteria freeze before data exists, NULLs are published at the
same resolution as wins, and the strongest refutation in this log was performed by us on our own
already-public result. Compute given to this program is not spent chasing a leaderboard number —
it is spent buying **frozen answers**, at a far higher iteration rate than this hardware allows.
H100/H200-class hardware would turn multi-day scans like this one into afternoons, single seeds into
five-seed ladders everywhere, the 30M-token stress tests into full-budget runs, and the bridge
program (question 4) plus the emergence program (question 6) into a serious entry for
higher-budget tracks and the 2027 cycle, where we aim to place.

To say it plainly: everything in this log — every frozen verdict, every replication, every
control — was asked and answered on **one RTX 4060 laptop GPU**. We are proud of what that card
has managed to answer, and we sincerely hope that one day the right connection brings better
equipment within reach, so that more of these questions — and harder ones — can be attempted
properly. If this log reads to you like a program worth equipping, we would be glad to hear
from you.

## The timeline

### Stage 1 — bind1: the win we refuted ourselves

Our official BabyLM 2026 strict-small entry (~24M params, an iterative role-binding loop) showed what
looked like the paper's key result: **entity tracking 39.55 vs 27.82** for the matched monolith (+11.7
points, single seed), alongside BLiMP 65.5 vs the GPT-2 baseline 65.1. We flagged it at submission time as
"the key result to replicate, not a settled fact" — and then we replicated adversarially instead of
celebrating.

The audit finding: **the entity-tracking lead was not tracking.** In the benchmark's item pool, "nothing."
never appears as a distractor (0 of 9,483 items) — whenever it is among the options it is the answer — and
completion scoring structurally favors it. Counterfactual probes showed that *no* model in an 11-model,
multi-seed grid could actually distinguish empty from non-empty containers (discrimination ≈ coin-flip,
AUC 0.47–0.58); on non-"nothing" items every model, every seed, sat at chance. The entire ablation gradient
that looked like a mechanism story was the gradient of a state-blind "nothing." completion prior. Under the
leaderboard's corrected scoring standard (nothing-gold items removed), the lead disappears.

Both scorings are published side-by-side on the public bind1 model card (repo map below). This
refutation-of-our-own-result is the credibility opener of the whole line: it is why you can trust the
verdicts that follow.

### Stage 2 — the curriculum duel: a null that indicted the exam, not the architecture

A pre-registered curriculum duel asked whether the binding architecture out-learns a matched monolith on an
in-context binding curriculum. Frozen verdict: **NULL** — but of a specific, diagnostic kind: *both* arms
stayed flat near floor across all 12 checkpoints through 50M tokens (endpoint 0.1875 vs 0.1842). Under the
frozen decision grid this lands in the "exam/scale problem" cell: an exam neither arm can learn
discriminates nothing about architecture. We recorded it as an **exam-design artifact** and drew the
obvious lesson — before asking *who learns faster*, first build an exam that is demonstrably learnable.
That lesson directly shaped the next two generations.

### Stage 3 — bind2_0: the mechanism works; the transfer doesn't ("no tax, no win")



bind2_0 combines delta-rule fast-weight memory with a **forced bottleneck**: attention is chunk-local, so
cross-chunk information can only flow through a recurrent state. Three results, all kept:

1. **Direct-task training works.** On a purpose-built synthetic swap-tracking task (n=800 per eval, 5-way,
   chance 0.20), bind2_0 reaches **0.9988 accuracy** while its matched controls (monolith, bind1-style

   loop, no-binding control) sit at **0.2125 / 0.1938 / 0.1938** — with a sharp grokking transition between

   5M and 10M training tokens (0.179 → 0.969 → 0.996). Learnable exam: achieved.

2. **It does not emerge for free.** Trained as a plain LM on the real BabyLM strict-small corpus, the

   architecture showed **no emergent zero-shot state-tracking advantage** (on a 60-probe test with chance

   0.50, no model — ours or baseline — beat chance).

3. **No general-language tax.** On the official zero-shot evaluation it is **statistically tied with the

   matched baselines**, slightly above the GPT-2 baseline on BLiMP (66.11 at 23.9M params vs 65.08).



Honest summary: **"no tax, no win."** Mechanism capability and benchmark transfer are *separate questions*,

and conflating them is how fields fool themselves. What this stage forced next: split the confound — first

prove the mechanism is *causally real at depth* under a pre-registered gate, separately from transfer.



Weights and code for this stage:

[`SecludedCorner/bind2_0`](https://huggingface.co/SecludedCorner/bind2_0)

(main = 23.9M build; branch `27m` = 27M build).



### Stage 4 — bind2_1: a NULL that stands, a causal result that survives, and a new finding

The successor mechanism (weights/code not released — see below) was tested the hard way: a **10-arm

campaign, 5 seeds per arm, with thresholds and the judgment script frozen before any data existed**. The
frozen gate was an AND over five criteria. What happened is the most instructive verdict in this log:

- **Discrimination passed.** The full system scored **85.83** (pooled deep-rung accuracy) against **every**
  control arm sitting at chance (≈16.5–17.5, chance 16.98) — a ~69-point margin over each of seven learned
  controls, on all 5 seeds, against a pre-registered minimum effect of 5 points. A scrambled negative
  control scored *below* chance (12.67), and an oracle upper bound scored 99.92. An audited shortcut
  ceiling (the best any state-blind strategy could reach) was ≤19.7 pooled; the system exceeds it by ~66
  points.
- **Causality passed.** Lesioning the mechanism's state pathway removes **99.3%** of the deep-rung
  advantage while leaving non-query language modeling flat; interchange patching flips **96.4%** of answers
  to the donor context's holder, with residual specificity 1.0. The deep-rung behavior is carried by the
  mechanism — necessarily and sufficiently.
- **Criterion C failed → verdict NULL.** C required the depth-interaction margin to hold or grow with
  depth; the observed margins shrink (73.3 → 74.5 → 66.5 → 60.3 across the four deep rungs), which the
  pre-registration had labeled a "bypass" signal. **The frozen verdict is NULL and it stands permanently.**
- **The autopsy — recorded, not used to overturn.** Adversarial post-hoc analysis showed criterion C was
  **ceiling-confounded**: because chance itself falls with depth, only a near-lossless mechanism (like the
  oracle, which passes C) *could* pass; a system starting at 96% has nowhere to grow its shallow margin.
  This is a design-time specification error and we record it as exactly that. The "bypass" *interpretation*
  of the NULL is, separately, refuted by the causal results: a bypassed module cannot carry 99.3% of the
  effect, and the deepest rung (72.8) sits ~57 points above the audited shortcut ceiling (≤15.5).
- **The real finding: limited effective depth.** The mechanism's engagement decays gracefully with depth
  (≈95% → 69%) while the oracle stays ≈100% — so depth-robust binding is achievable on this task and the
  learned mechanism does not fully achieve it. That gap is a concrete architectural target, not a
  rhetorical one.

**Why there is no bind2_1 repo:** the headline number is a teacher-forced readout on a synthetic diagnostic

corpus (not BabyLM data), and a standard free-running export does not reproduce it. Publishing weights that

cannot reproduce their own headline would be misleading, so this stage ships as numbers-and-narrative only.

What is withheld is tooling and weights, not results — the numbers reported here are complete.



Because the corrected depth criterion (C′) was formulated after seeing the data, it cannot be scored on

that data as anything but exploratory. So C′ was **frozen on 2026-07-15, before touching the five held-back

seeds**, and the confirmatory rerun on those untouched seeds landed the same day: **CONFIRMATORY PASS.**

Per-rung margins on the fresh seeds — 73.3 / 74.6 / 66.9 / 59.4 points — replicate the original seeds

almost exactly; the deepest rung scores 72.7 against a 31.0 shortcut bar; and the causal lesion battery

replicates on all five unseen-seed checkpoints (99.30% of the deep advantage removed by the state lesion).

One procedural note, disclosed in full: the judgment script as originally frozen demanded control arms the

confirmatory design never scheduled and exited without scoring; that output is preserved untouched, and the

verdict above comes from a plumbing-fixed variant whose criteria are byte-identical to the frozen ones,

adversarially reviewed before unblinding, with the full evidence chain on record internally. The

discrimination criterion was inherited from the original seeds, not re-measured. The seeds-0-4 NULL stands

unchanged.



### Stage 5 — bind2_1e: the mechanism passes the official exam (single seed, provisional)



The transfer question bind2_0 failed ("no tax, no win") could now be asked properly: take the

causally-verified binding mechanism, train it on a synthetic box-tracking corpus whose answer statistics

are **distribution-matched to the official entity-tracking benchmark**, and score it on the official items with **fully learned routing** — no oracle assistance at test time.



Everything was pre-registered and frozen before the run: the corpus ruling, the success/null bands, the

shortcut-decomposition gates, the abort rules. Default prediction: NULL.



Result (2026-07-15), one shot, first read final: **93.96% pooled** on the two in-scope official subsets

(6,259 items; chance 20%; every earlier model in this program — and the published baselines — sits at

≈19–21% on this benchmark). The regular subset scores **99.05% with zero decay across operation depth**

(99.4% at the deepest tier); the contents-move subset scores **88.80%** with graceful depth decay. The

routing fear died completely: the learned router matches oracle-hinted routing to the third decimal

(transfer gap 0.000).



The credibility core is the pre-registered decomposition. The benchmark's strongest audited shortcut ("the

last-touched box is the answer") can solve ~59% of items; on the **2,550 items that shortcut cannot solve,

the model scores 92.4%** — and 96/92/92/92/81% across operation depths 1–5. Empty-box golds (the classic

prior-abuse trap from Stage 1) score **97.6%**, with a perfect 100% on the regular subset. The verdict

label, per the frozen bands: **MECHANISM-TRANSFER — provisional, single seed.**



What this is *not*, stated plainly:



- **Single seed means exactly that: provisional until replicated.**

- **Subset scope:** the score covers the regular and 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.

- **This is a mechanism-transfer demonstration, not a general language model.** The model was trained only

  on synthetic box-tracking text; every other suite in the official evaluation is expected to sit at chance

  by design, and no claim is made there.

- **The natural-diet baselines are diet-confounded as an architecture comparison.** Their chance-level

  scores come from natural-text training; the matched-diet architecture attribution rests on the bind2_1

  campaign's seven matched controls (Stage 4) plus the dedicated matched-diet control reported just below.



Nothing from this stage is downloadable at this point: the weights, code, configuration, and the

corpus/generator tooling are all withheld at this stage. The frozen pre-registration, the decomposition,

and the per-item results are on record internally; the numbers reported here are complete.



**Update (2026-07-17) — three pre-registered follow-ups, all resolved in the mechanism's favour.** Each
froze its bands before its run; the results:

- **Seed replication (now n = 3).** The frozen procedure re-ran on two untouched seeds: pooled **94.12%**
  and **94.22%**, against the original **93.96%** — a spread of 0.26 points across three seeds, with the
  shortcut-unsolvable subsets and empty-box golds replicating in lockstep. The "provisional — single seed"
  qualifier is **removed**: the transfer replicates.
- **Matched-diet control (the diet-confound, closed).** A standard attention-only transformer trained on the
  *identical* box-tracking diet (same token budget, single seed) scores pooled **50.6%** — but on the
  pre-registered decisive subset, the items the shortcut cannot solve, it scores **23.2% ≈ chance (20%)**,
  versus this model's **92.6%** on the same subset. Its overall half-score is carried entirely by the
  empty-box prior and the last-touch shortcut; on the items that require state tracking it floors. A standard
  architecture on the same diet cannot do it — the advantage is architectural, not a diet artifact.
  (Single seed = provisional.)
- **Mixed-diet stress test.** Retrained on 35% box-tracking + 65% natural text (same 30M-token budget), the mechanism holds: pooled **94.54%** — if
  anything *above* the boxes-only run (report-only, single seed), all decomposition gates passed by wide
  margins. On this mixed diet the model also produces real (non-chance) general-language scores; its BLiMP
  came in at **59.92 against a pre-registered no-tax gate of 60.0** — 0.08 below the line, recorded under
  the frozen label **TAX-OR-BUDGET**: attribution left open between an architecture cost and the 5×-smaller
  token budget of this run versus the 150M-token baselines, neither claimed. A separate zero-training probe
  for transfer to *natural-language* reference tracking returned the pre-registered **NULL** on both primary
  subjects — expected, since the mechanism's readout is inert on those inputs; recorded as no evidence, not
  as a mechanism failure.

### Stage 6 — where the line is now

Current focus: consolidating the replicated transfer result and its controls.

## The dual-verdict structure, stated explicitly

Two verdicts exist for bind2_1 and they answer **different questions**. They are never merged:



1. **The original pre-registered verdict is NULL, permanently.** Criteria and judgment were frozen before

   data; criterion C failed; the AND-gate returns NULL. Re-labeling it PASS after seeing the data would be

   criterion-shopping, and the entire point of pre-registration is to make that impossible. The autopsy

   that found criterion C ceiling-confounded is *recorded alongside* the verdict; it does not modify it.

2. **The corrected-criterion confirmatory run is a separate, second question.** C′ (per-rung margin above

   the pre-registered minimum effect AND deepest-rung accuracy far above the audited shortcut ceiling) was

   frozen on 2026-07-15 **before** the five backup seeds were touched; those seeds played no role in

   designing C′. Its result (2026-07-15): **CONFIRMATORY PASS** — per-rung margins 73.3/74.6/66.9/59.4

   points on the fresh seeds, deepest rung 72.7 vs the 31.0 shortcut bar, causal battery replicated on all

   five unseen-seed checkpoints. It answers "does the corrected criterion hold on fresh seeds?" (yes) — it

   does not, and cannot, retroactively change verdict #1.



## Numbers



### Mechanism axis (bind2_1 campaign; frozen ladder, synthetic diagnostic exam, teacher-forced readout; accuracy %, mean±SD, n=5 seeds per arm)

The seven learned control/ablation arms are anonymized here (A–G);
their internal identities and configurations are part of the withheld tooling. The negative-control,
upper-bound, chance, and ceiling rows are as frozen.

| arm | R3 | R4 | R5 | R6 | pooled deep (R3–R6) |
|---|---|---|---|---|---|
| the full system | 96.00±0.00 | 92.90±0.06 | 81.58±0.14 | 72.82±0.21 | **85.83±0.05** |
| learned control A | 21.38±0.32 | 17.28±1.22 | 15.10±1.40 | 13.75±1.17 | 16.88±0.14 |
| learned control B | 22.30±2.06 | 18.85±1.52 | 14.53±1.48 | 12.53±0.54 | 17.05±0.72 |
| learned control C | 21.93±2.01 | 16.48±0.99 | 15.68±1.41 | 12.03±1.14 | 16.53±1.11 |
| learned control D | 22.05±0.69 | 18.32±1.38 | 14.35±0.90 | 12.17±0.26 | 16.73±0.72 |
| learned control E | 23.43±1.37 | 19.37±1.86 | 14.67±1.25 | 12.35±1.13 | 17.46±0.57 |
| learned control F | 22.65±1.46 | 18.40±1.22 | 15.07±0.75 | 12.47±1.11 | 17.15±0.50 |
| learned control G | 21.68±0.89 | 18.60±1.78 | 14.40±0.78 | 12.90±1.38 | 16.89±0.67 |
| scrambled negative control | 16.05±0.07 | 14.45±0.07 | 11.25±0.15 | 8.95±0.14 | 12.67±0.05 |
| oracle upper bound | 99.95±0.07 | 100.00±0.00 | 99.90±0.10 | 99.85±0.14 | 99.92±0.04 |
| *chance* | 22.50 | 18.33 | 14.58 | 12.50 | 16.98 |
| *audited zero-state shortcut ceiling* | ≤25.38 | ≤20.50 | ≤17.38 | ≤15.50 | ≤19.69 |

Notes (from the frozen source): pooled deep = micro-average over R3–R6, the frozen primary endpoint;
pre-registered minimum effect (SESOI) = 5.0 pp on pooled deep; shallow rungs R0–R2 are shortcut-reachable
by design (claim-ineligible) and intentionally not tabled.

Causal results (frozen gate, criterion B): state-lesion kill = 99.3% of deep-rung advantage with non-query
perplexity flat; interchange flip = 96.4%; residual specificity = 1.0; all 5 seeds pass.

Depth margins vs learned control F, the pre-registered reference control (criterion C, failed):
R3 73.3 / R4 74.5 / R5 66.5 / R6 60.3.

### Official axis (BabyLM 2026 evaluation; "pending" = not yet measured)

| model | params | scoring | BLiMP | BLiMP-supp | EWoK | Entity | COMPS | GlobalPIQA | GLUE | Reading | AoA | Overall |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| mono (paper baseline) | 27.4M | pre-filter Entity standard [c] | 64.35 | 58.55 | pending | 27.82 | 51.00 | pending | pending | pending | pending | pending |
| mono | 23.9M | local zero-shot, single seed [b] | 65.35 | 58.17 | 51.32 | 21.16 | 51.55 | pending | pending | pending | pending | pending |
| mono | 27.4M | local zero-shot, single seed [b] | 64.35 | 58.55 | 50.70 | 19.24 | 51.00 | pending | pending | pending | pending | pending |
| bind1 (leaderboard, server-scored) | 24.0M | official server [a] | 65.83 | 54.18 | 51.08 | 18.92 | 51.19 | 34.21 | 61.22 | 9.59 | 0.00 | 38.12 |
| bind1 | 23.9M | local zero-shot, single seed [b] | 65.50 | 58.35 | 51.57 | 19.22 | 51.11 | pending | pending | pending | pending | pending |
| bind1 | 27M | local zero-shot, single seed [b] | 66.68 | 60.90 | 51.90 | 20.00 | 51.36 | pending | pending | pending | pending | pending |
| bind2_0 | 23.9M | local zero-shot, single seed [b] | 66.11 | 58.11 | 51.95 | 19.02 | 51.49 | pending | pending | pending | pending | pending |

| bind2_0 | 27M | local zero-shot, single seed [b] | 65.14 | 60.81 | 51.16 | 20.53 | 50.89 | pending | pending | pending | pending | pending |
| bind2_1 | — | official eval pending | pending | pending | pending | pending | pending | pending | pending | pending | pending | pending |

| **bind2_1e** | 27.8M | boxes-diet transfer probe, single seed [d] | n/a [d] | n/a [d] | n/a [d] | **93.96** [d] | n/a [d] | n/a [d] | n/a [d] | n/a [d] | n/a [d] | n/a [d] |

| **bind2_1e-mixed (35/65)** | 27.8M | mixed diet, 30M, single seed [e] | 59.92 [e] | 57.96 | 50.24 | **69.95** [e] | 50.01 | pending | pending | pending | pending | pending |

| mono (matched-diet control) | 27.4M | boxes diet, single seed [f] | n/a | n/a | n/a | 50.57 [f] | n/a | n/a | n/a | n/a | n/a | n/a |



- [a] Server-verified 2026-07-10; the leaderboard "Reading" aggregate is 6.46 (self-paced 3.34 /

  eye-tracking 9.59); the 9.59 cell above is the eye-tracking component as tabled in the source.

- [b] Local run of the official strict-small zero-shot pipeline, single seed, **not** submitted to the

  leaderboard; tasks the local pipeline cannot produce remain pending.

- [c] The paper-baseline Entity 27.82 uses the **old pre-filter** entity standard; the same checkpoint

  re-scored under the post-filter standard measures 19.24 — the two Entity columns are not directly

  comparable across scoring standards. (This scoring change is exactly the bind1 refutation story in

  Stage 1.)

- Reference point: GPT-2 strict-small baseline BLiMP = 65.08.

- [d] bind2_1e is a **mechanism-transfer probe, not a general LM entry**: trained solely on a synthetic
  distribution-matched box-tracking corpus (30M tokens, single seed, free routing at test). Its Entity cell
  covers the regular+move_contents subsets only (6,259/9,483 items; the ambiguous-reference subset is

  excluded by the frozen pre-registration — outside mechanism scope); other suites are expected ≈ chance

  **by design** and are not measured or claimed ("n/a"). Pre-registered decomposition: shortcut-unsolvable

  subset 92.39, empty-box gold 97.55, learned-routing gap 0.000 (the decomposition and per-item results are

  on record internally; withheld at this stage). Same-scorer baseline reruns (same subsets, same script)

  will be tabled as they land.

- [e] bind2_1e-mixed = the same architecture retrained on 35% box-tracking + 65% natural text (30M tokens,
  single seed), so its BLiMP/supp/EWoK/COMPS cells are real measurements. BLiMP 59.92 sits 0.08 below the
  frozen no-tax gate of 60.0 → recorded **TAX-OR-BUDGET**, attribution open (the natural-text baselines
  above trained on 150M tokens — 5× this run's budget); neither "tax" nor "no tax" is claimed. The Entity
  cell 94.54 is the **mechanism-caliber** one-shot score (regular+move_contents subsets, per-item records);

  under the **official-pipeline caliber** — the official 2026-07-12 exam, all three subsets including

  ambiguous-reference — the same run measures **69.95** on the official exam, versus the natural-text

  baseline's **19.24** (≈ chance). The two calibers are materially different and are never interchangeable. Decomposition:

  shortcut-unsolvable 92.55/89.92 (two frozen conventions), empty-box gold 98.94, routing gap 0.000.

- [f] mono matched-diet control = a standard attention-only transformer trained on the **identical**

  box-tracking diet (same budget, single seed). Pooled 50.57, but on the shortcut-unsolvable subset it

  scores **23.22 ≈ chance (20)** versus bind2_1e's 92.55 on the same subset; its overall half-score is
  carried by the empty-box prior (nothing-gold 99.89) and the last-touch shortcut. This closes the
  architecture-vs-diet confound: a standard architecture on the same diet floors on the mechanism-requiring
  items. Single seed = provisional.
- Under the current (filtered) Entity standard, entity tracking is ~chance for all natural-diet grid models
  — the spread across architectures on the comparable local-zero-shot rows is within single-seed noise; no
  architecture wins the official exam from natural-text training alone, which is exactly the Stage-3
  "no tax, no win" point. The bind2_1e rows are a different kind of entry — a diet-matched transfer probe

  (see [d]/[e] and Stage 5) — and do not overturn that point.



## Repo map



| repo | what it is |

|---|---|

| [`SecludedCorner/bind1-babylm2026-strict-small`](https://huggingface.co/SecludedCorner/bind1-babylm2026-strict-small) | bind1 entry weights + growth-checkpoint branches (cited by our workshop paper; unchanged). Dual scoring (pre/post-filter) published on its card |

| [`SecludedCorner/bind1-babylm2026-strict-small-r2`](https://huggingface.co/SecludedCorner/bind1-babylm2026-strict-small-r2) | bind1 retrain/resubmission line, re-collated under the revised entity-tracking standard |

| [`SecludedCorner/bind1-babylm2026-ablations`](https://huggingface.co/SecludedCorner/bind1-babylm2026-ablations) | bind1 ablation checkpoints |

| [`SecludedCorner/bind1-babylm2026-eval-artifacts`](https://huggingface.co/datasets/SecludedCorner/bind1-babylm2026-eval-artifacts) (dataset) | bind1 evaluation artifacts, incl. the falsification-analysis data |

| [`SecludedCorner/bind2_0`](https://huggingface.co/SecludedCorner/bind2_0) | bind2_0 weights + code; main = 23.9M, branch `27m` = 27M |
| `SecludedCorner/bind-evolution` (this repo) | the narrative hub; navigation and story only |

Not released: bind2_1 weights/code/config (see Stage 4 for why); bind2_1e weights, code, configuration, and
its synthetic training corpus and generator (withheld at this stage — see Stage 5); the synthetic diagnostic
corpora and their generators; and the probe harness. The numbers above are complete as reported; what is
withheld is tooling and weights, not results.

## How to cite

Cite **member repos, not this hub**, and always pin to a 40-character commit SHA
(`revision="<full-sha>"` in `from_pretrained`, or the `/tree/<sha>` URL form). The authoritative SHA for
each published artifact is stamped on that repo's card as a dated addendum at publish time.
This hub's prose may be updated as research progresses (updates are dated); member-repo cards are frozen at
publish, which is why they — at a pinned SHA — are the citation targets.