bind-evolution narrative hub README (falsification timeline, frozen 2026-07-15)

README.md

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+---
+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-babylm2026-strict-small`](https://huggingface.co/SecludedCorner/bind2_0-babylm2026-strict-small)
+(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-babylm2026-strict-small`](https://huggingface.co/SecludedCorner/bind2_0-babylm2026-strict-small) | 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.