README.md

---
pretty_name: "bind2_1 — binding-architecture research model (results-only card)"
language:
- en
tags:
- babylm
- state-tracking
- compositional-generalization
- causal-analysis
- research-log
- negative-results
---

# bind2_1 — a binding-architecture research model (results-only card)

**Results-only card. No downloadable artifact.** No weights, no code, no configuration, no training
corpus, and no generator are published for `bind2_1`. This card reports what the model is, what was
tested, and what the results mean. The numbers below are complete as reported; what is withheld is
tooling and weights, not results. See [Why no weights](#why-there-are-no-weights) for the reason.

`bind2_1` is one member of the **bind-evolution** line — a small-model architecture program for
**compositional state tracking** at BabyLM 2026 Strict-Small scale. For the full falsification
timeline and how the members relate, see the narrative hub
[`SecludedCorner/bind-evolution`](https://huggingface.co/SecludedCorner/bind-evolution).

---

## Value at a glance

`bind2_1` was put through a **pre-registered campaign** — thresholds and the judgment script frozen
before any data existed — to separate two questions that are usually conflated: *is the mechanism
causally real at depth?* and *does its advantage grow with depth?* The campaign returned a rare and
useful combination:

| what was asked | frozen result |
|---|---|
| Does the system discriminate on the hard, state-requiring items? | **Yes.** Pooled deep accuracy **85.83** vs every learned control at chance (~17); a **~69-point** margin. |
| Is that behavior *caused* by the mechanism? | **Yes.** Lesioning the state pathway removes **99.3%** of the advantage; interchange patching flips **96.4%** of answers to the donor's holder. |
| Does the advantage hold or grow with depth (the pre-registered criterion)? | **No — frozen verdict NULL.** The margin shrinks across depth; the NULL stands permanently. |
| What did the NULL actually reveal? | **Limited effective depth** — a concrete, measurable architectural target, not a rhetorical one. |
| Does more scale dissolve the effect? | **No.** Across a ~6× parameter ladder, the standard controls never leave their floor — the form change is principle-grade, not a small-model artifact. |

The value of this member is not a leaderboard number. It is a **cleanly separated causal result and a
frozen NULL**, published at the same resolution, so that a reader can trust exactly what was and was
not established.

---

## What `bind2_1` is

A from-scratch binding architecture for compositional state tracking (tracking "who holds what" as a
narrative proceeds), built at **BabyLM 2026 Strict-Small scale (~24M parameters)**. It is the
successor mechanism to `bind2_0`
([`SecludedCorner/bind2_0`](https://huggingface.co/SecludedCorner/bind2_0),
weights + code published there). This card describes results only; the mechanism's internals are not
disclosed here.

Unlike a general language-model entry, `bind2_1` was not evaluated as a single trained checkpoint.
It was evaluated as a **campaign**: a frozen synthetic diagnostic exam (a narrative ladder of
increasing binding depth), a teacher-forced readout, and a fixed roster of learned control and
ablation arms trained under identical conditions so that any advantage can be attributed to the
architecture rather than to the data or the training budget.

---

## What was tested

A **10-arm campaign, 5 seeds per arm**, with the success thresholds and the entire judgment script
**frozen before any data existed**. The frozen gate was an AND over five criteria. The exam is a
synthetic diagnostic ladder whose deep rungs (labelled R3–R6 below, corresponding to operation depth
3 through 6) require genuine state tracking; the shallow rungs are solvable by shortcut and are
excluded from the claim by design.

Pre-registered decision parameters (frozen):

- **Primary endpoint** = pooled deep accuracy, micro-averaged over depth rungs R3–R6.
- **Minimum effect of interest (SESOI)** = 5.0 percentage points on the primary endpoint.
- **Anchors** = a scrambled negative control (destroys the input structure), an oracle upper bound
  (has perfect state access), chance, and an *audited shortcut ceiling* (the best score any
  state-blind strategy could reach).

---

## Results

### 1. Discrimination — PASS

The full system scored **85.83** pooled deep accuracy. Every one of the seven learned control arms
sat at chance (≈16.5–17.5; chance = 16.98) — a **~69-point** margin over each control, on all 5 seeds,
against the pre-registered 5-point minimum effect. The scrambled negative control scored *below*
chance (12.67); the oracle upper bound scored 99.92. The audited shortcut ceiling was ≤19.69 pooled;
the system exceeds it by **~66 points**. The advantage is not reachable by any state-blind shortcut.

### 2. Causality — PASS

The discrimination is *caused* by the mechanism, established two independent ways:

- **State-lesion (necessity):** removing the mechanism's state pathway removes **99.3%** of the
  deep-rung advantage, while non-query language-modeling perplexity stays flat (the lesion is
  targeted, not a general degradation).
- **Interchange intervention (sufficiency):** patching the state from a donor context flips **96.4%**
  of answers to the donor context's holder, with residual specificity **1.0**.

Both replicate on all 5 seeds. A bypassed or spurious module cannot carry 99.3% of an effect; the
deep-rung behavior is carried by the mechanism, necessarily and sufficiently.

### 3. The pre-registered depth criterion — FAILED → frozen verdict NULL

One pre-registered criterion required the depth-interaction margin to **hold or grow with depth**.
The observed per-rung margins instead *shrink* across the four deep rungs:

> R3 **73.3** → R4 **74.5** → R5 **66.5** → R6 **60.3** points.

The pre-registration had labelled a shrinking margin a "bypass" signal, so the AND-gate returns
**NULL**. **This frozen verdict stands permanently** and is never re-labeled PASS. Publishing it at
full resolution — alongside the passing causal results — is the point.

### 4. The autopsy (recorded, not used to overturn the NULL)

Adversarial post-hoc analysis found the failed criterion was **ceiling-confounded**: because chance
itself falls as depth increases, only a near-lossless system (like the oracle, which passes it) could
possibly satisfy it; a mechanism already starting near 96% at shallow depth has no room to *grow* its
margin. This is a **design-time specification error**, recorded honestly as exactly that — it is not
used to modify the frozen verdict. Separately, the "bypass" *interpretation* of the NULL is refuted
by the causal results: the deepest rung (72.8) sits ~57 points above the audited shortcut ceiling
(≤15.5), which a bypassed module could not produce.

### 5. The real finding — limited effective depth

The mechanism's engagement **decays gracefully with depth** (≈95% → 69%) while the oracle stays
≈100%. In plain terms: depth-robust binding *is* achievable on this task, and the learned mechanism
does not fully achieve it. That gap is a **concrete, measurable architectural target** — the most
useful thing the NULL bought.

---

## The dual-verdict structure (stated explicitly)

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

1. **The original pre-registered verdict is NULL, permanently.** Criteria and judgment were frozen
   before data; the depth criterion failed; the AND-gate returns NULL. Re-labeling it PASS after
   seeing the data would be criterion-shopping, which pre-registration exists to make impossible.
   The autopsy above is recorded *alongside* this verdict; it does not modify it.

2. **A corrected-criterion confirmatory run is a separate, second question.** A corrected criterion
   (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 touching five held-back seeds** —
   those seeds played no role in designing it. 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). This answers "does the corrected
   criterion hold on fresh seeds?" (yes). It **cannot** retroactively change verdict #1.

One procedural note, disclosed in full: the judgment script as originally frozen expected control
arms the confirmatory design never scheduled and exited without scoring; that output is preserved
untouched, and the confirmatory verdict comes from a plumbing-fixed variant whose criteria are
byte-identical to the frozen ones, adversarially reviewed before unblinding.

---

## Does parameter scale alone dissolve the gap? (No)

The most compute-starved test in the program asked whether more parameters simply wash the form
change out. The standard control architectures were re-trained across a **parameter ladder spanning
roughly 6×** (from ~24M up to a matched ~145M configuration). On **every** claim-bearing deep rung,
at **every** scale, the controls stayed on their floor — none crossed the discrimination bar, even
when given up to **10×** the campaign's training budget at the smallest and largest scales (3.3× at
the two intermediate scales).

The controls neither climb toward the threshold nor decay toward chance as they grow; they sit on a
**fixed, scale-invariant plateau**. Reading: the form change observed at small scale is
**principle-grade** — it is not a small-model sample-efficiency artifact that more parameters would
dissolve. 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; the span is
stated as ~6×).

---

## Numbers

### Mechanism axis (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. Depth rungs R3–R6 are the claim-eligible deep tiers; shallow rungs R0–R2
are shortcut-reachable by design and intentionally not tabled.

| 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 |

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

**Depth-interaction margins (the failed pre-registered criterion):** R3 73.3 / R4 74.5 / R5 66.5 /
R6 60.3 points.

---

## Why there are no weights

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**. The frozen pre-registration, the full decomposition, and the per-item results are on record;
the numbers reported here are complete. What is withheld is tooling and weights, not results.

---

## What this is — and is not

- **A causal-mechanism result plus a frozen NULL**, both pre-registered and published at the same
  resolution. It is *not* a general language-model entry and *not* a natural-language transfer claim.
- **The frozen NULL is permanent.** The confirmatory PASS is a separate, second question and does
  not — and cannot — overturn it.
- **Diagnostic, teacher-forced, synthetic exam.** The 85.83 is a depth-diagnostic capability score,
  not an official-benchmark score.
- **Architecture attribution is clean by design** — the advantage rests on seven learned controls
  trained under identical conditions, plus the ~6× parameter-scale scan above; it is not a data or
  budget artifact.
- **Single-hardware program.** Every result was produced on one 8 GB laptop-class GPU; single seeds
  were used where five were wanted, and budgets were smaller than a lab's. This is disclosed as a
  constraint on iteration rate, not on the frozen verdicts.

The causally-verified mechanism was subsequently carried to the official entity-tracking question
form as the extension `bind2_1e`; that separate result, its scope, and its caveats are documented in
the [bind-evolution hub](https://huggingface.co/SecludedCorner/bind-evolution) — not here.

This is part of a research program that we hope to develop toward a serious entry in a future
BabyLM cycle; we **aim to place** in 2027. No ranking or superiority claim is made here.

---

## How to cite

Cite this member card, pinned to the commit SHA of the revision you are citing (the authoritative SHA
is stamped on this card as a dated addendum at publish time). Do not cite the bind-evolution hub as a
result source — it is a narrative and navigation hub. Naming: the member id is **`bind2_1`**; a bare
`bind2` is never a repository or artifact name — the family is referred to in prose only.

**Related:** [`SecludedCorner/bind-evolution`](https://huggingface.co/SecludedCorner/bind-evolution)
(the falsification timeline) ·
[`SecludedCorner/bind2_0`](https://huggingface.co/SecludedCorner/bind2_0)
(the predecessor, weights + code).