| ---
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| pretty_name: "bind evolution — a falsification timeline"
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| language:
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| - en
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| tags:
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| - babylm
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| - state-tracking
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| - linear-attention
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| - research-log
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| - negative-results
|
| ---
|
|
|
| # bind evolution — a falsification timeline
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|
|
| **Research in progress. This repo is a narrative and navigation hub — not a leaderboard showcase, and not a
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| citation target for any single result.** To cite a result, cite the member repo that carries it, pinned to a
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| commit SHA (see [How to cite](#how-to-cite)).
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|
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| This is the research log of a small-model architecture line ("bind") for **compositional state tracking**
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| at BabyLM 2026 Strict-Small scale (~24M params, 10M-word training corpus), told the only way we trust: as a
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| sequence of **pre-registered questions, frozen verdicts, and honest refutations — including refutations of
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| our own published results**. Every generation below either falsified its predecessor's headline claim or
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| was itself falsified by a frozen criterion. The value of the line is not any single score; it is that each
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| verdict — positive or null — forced a sharper next question.
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|
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| Ground rules that hold throughout:
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|
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| - **Frozen verdicts stand.** A pre-registered NULL is never re-labeled PASS, even when a post-hoc autopsy
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| finds the criterion itself was flawed.
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| - **We audit our own wins adversarially.** The strongest refutation in this log was performed by us, on our
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| own already-public result.
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| - **Negative results are published at the same resolution as positive ones.**
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|
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| ## What each test asked, why it was run, and what it buys — on one 8 GB laptop GPU
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|
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| Every experiment in this program ran on a **single RTX 4060 Laptop GPU (8 GB)**. That constraint
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| shaped everything: single seeds where five were wanted, 30M-token budgets where the baselines got
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| 150M, a serial queue where a lab would fan out overnight, and one scale scan that costs ~48
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| wall-clock hours here versus a fraction of that on a modern training node. Here is what each test
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| asked, why, and what its answer unlocks.
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|
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| **1. Does the headline result replicate?** A single-seed result is an anecdote. The frozen
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| procedure was re-run on two untouched seeds: **93.96 / 94.12 / 94.22** — a 0.26-point spread
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| across three seeds. The transfer replicates; "provisional" was removed. *Unlocks:* the result is
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| load-bearing enough to build on.
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|
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| **2. Is the advantage the architecture's — or just the training data's?** Any model can look good
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| on the diet built for it, so a standard attention-only transformer was trained on the *identical*
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| synthetic diet and scored on the same items. On the subset where state tracking is actually
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| required it **floors at chance (23.2%)** while the binding model scores **92.6%** there. *Unlocks:*
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| the advantage is an architectural asset, worth carrying to scale — not a data artifact.
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|
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| **3. Does the mechanism survive real language in the diet?** A mechanism that only works on a pure
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| synthetic diet is a lab curiosity. Diluted to 35% of a mixed diet (65% natural text, same total
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| budget), the mechanism **held — 94.54%, slightly above the pure-diet run** — and the model's
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| general grammar score landed 0.08 points below a frozen "no-tax" line while training on **5× less
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| data than the baselines**. *Unlocks:* full-budget mixed training is the single most obvious next
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| run, and it is purely compute-bound.
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|
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| **4. Does it already transfer to natural-language reference tracking?** The endgame is real
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| language, so the gap was measured instead of assumed: pre-registered **NULL**, and precisely
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| diagnostic — the mechanism's readout literally never fires on natural text. The gap is a **missing
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| interface, not a disproven mechanism**. *Unlocks:* the bridge to natural text is now a concrete, bounded workstream rather than a hope.
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|
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| **5. Can the training scaffold be removed — or replaced by signals raw text can provide?**
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| *(answered 2026-07-17 — in halves.)* The recipe leans on training supervision the real world doesn't
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| hand out, so two pre-registered arms asked whether each supervision channel can be dropped; frozen
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| bands, NULL as the default prediction — and neither arm landed on the default. **One channel:
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| retained.** Dropping that supervision entirely holds the result —
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| pooled **94.12\%** against a frozen retention line of 88.96, statistically indistinguishable from
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| the original (93.96), with the shortcut-unsolvable subset at 92.6\%, empty-container items at
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| 97.4\%, and a routing gap of 0.000. **The other channel: partial.** Removing it costs measurably —
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| pooled **81.50\%**, well above the frozen collapse line (59.4, the audited shortcut ceiling) but
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| below retention; the mechanism's gain survives removal (77.6\% on the shortcut-unsolvable items),
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| routing stays intact, and it degrades gracefully rather than collapsing the result. *Unlocks:* the supervision question splits cleanly — one channel is already
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| corpus-derivable, and the other is now a measured cost curve instead of an unknown.
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|
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| **6. Will the mechanism emerge on its own when the data demands it?** *(answered 2026-07-17.)* The
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| deepest question in the line: the mechanism was forced by construction — would training pressure
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| alone produce it? A standard architecture was trained on a corpus engineered to strip the shortcut
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| reward (so the only way to lower the loss is to grow the real parse), and on a matched corpus that
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| leaves the shortcut in. **Frozen verdict: neither learned it** — both floor on the
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| mechanism-requiring subset (~0.14, below chance) while the forced mechanism scores 0.92 on the
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| identical test. Removing the shortcut-solvable pressure, under a generous budget, does **not** rescue
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| learning: pressure is refuted as a *sufficient* cause — the mechanism can be forced by
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| construction, but training pressure alone does not induce it. This is a publishable negative and a
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| precise one: it bounds where emergence does and does not happen under a generous budget, rather
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| than leaving "emergence" an open hope.
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|
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| **7. Does parameter scale alone dissolve the gap?** *(resolved 2026-07-21 — the most
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| compute-starved experiment here.)* Re-trained across a ~6× parameter ladder (24M → 145M), the
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| standard control architectures stayed on their floor on **every** deep rung at **every** level:
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| none of the claim-bearing deep cells crossed the bar (up to 10× the campaign training budget at
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| the smallest and largest levels, 3.3× at the two middle levels).
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| Scale alone does not buy this capability — the form change is principle-grade, not small-model
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| sample efficiency. The controls neither climb toward the threshold nor decay toward chance as they
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| grow; they sit on a fixed, scale-invariant plateau. The claim stays bounded to the tested
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| budget × scale box, with no extrapolation to unlimited parameters. (The largest level was measured
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| at a matched 145M configuration, disclosed; the span is stated as ~6×.)
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|
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| ### The ask
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|
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| The methodology is the guarantee: criteria freeze before data exists, NULLs are published at the
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| same resolution as wins, and the strongest refutation in this log was performed by us on our own
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| already-public result. Compute given to this program is not spent chasing a leaderboard number —
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| it is spent buying **frozen answers**, at a far higher iteration rate than this hardware allows.
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| H100/H200-class hardware would turn multi-day scans like this one into afternoons, single seeds into
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| five-seed ladders everywhere, the 30M-token stress tests into full-budget runs, and the bridge
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| program (question 4) plus the emergence program (question 6) into a serious entry for
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| higher-budget tracks and the 2027 cycle, where we aim to place.
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| To say it plainly: everything in this log — every frozen verdict, every replication, every
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| control — was asked and answered on **one RTX 4060 laptop GPU**. We are proud of what that card
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| has managed to answer, and we sincerely hope that one day the right connection brings better
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| equipment within reach, so that more of these questions — and harder ones — can be attempted
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| properly. If this log reads to you like a program worth equipping, we would be glad to hear
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| from you.
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|
|
| ## The timeline
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|
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| ### Stage 1 — bind1: the win we refuted ourselves
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|
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| Our official BabyLM 2026 strict-small entry (~24M params, an iterative role-binding loop) showed what
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| looked like the paper's key result: **entity tracking 39.55 vs 27.82** for the matched monolith (+11.7
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| points, single seed), alongside BLiMP 65.5 vs the GPT-2 baseline 65.1. We flagged it at submission time as
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| "the key result to replicate, not a settled fact" — and then we replicated adversarially instead of
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| celebrating.
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| The audit finding: **the entity-tracking lead was not tracking.** In the benchmark's item pool, "nothing."
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| never appears as a distractor (0 of 9,483 items) — whenever it is among the options it is the answer — and
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| completion scoring structurally favors it. Counterfactual probes showed that *no* model in an 11-model,
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| multi-seed grid could actually distinguish empty from non-empty containers (discrimination ≈ coin-flip,
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| AUC 0.47–0.58); on non-"nothing" items every model, every seed, sat at chance. The entire ablation gradient
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| that looked like a mechanism story was the gradient of a state-blind "nothing." completion prior. Under the
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| leaderboard's corrected scoring standard (nothing-gold items removed), the lead disappears.
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| Both scorings are published side-by-side on the public bind1 model card (repo map below). This
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| refutation-of-our-own-result is the credibility opener of the whole line: it is why you can trust the
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| verdicts that follow.
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|
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| ### Stage 2 — the curriculum duel: a null that indicted the exam, not the architecture
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| A pre-registered curriculum duel asked whether the binding architecture out-learns a matched monolith on an
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| in-context binding curriculum. Frozen verdict: **NULL** — but of a specific, diagnostic kind: *both* arms
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| stayed flat near floor across all 12 checkpoints through 50M tokens (endpoint 0.1875 vs 0.1842). Under the
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| frozen decision grid this lands in the "exam/scale problem" cell: an exam neither arm can learn
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| discriminates nothing about architecture. We recorded it as an **exam-design artifact** and drew the
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| obvious lesson — before asking *who learns faster*, first build an exam that is demonstrably learnable.
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| That lesson directly shaped the next two generations.
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|
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| ### Stage 3 — bind2_0: the mechanism works; the transfer doesn't ("no tax, no win")
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|
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| bind2_0 combines delta-rule fast-weight memory with a **forced bottleneck**: attention is chunk-local, so
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| cross-chunk information can only flow through a recurrent state. Three results, all kept:
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|
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| 1. **Direct-task training works.** On a purpose-built synthetic swap-tracking task (n=800 per eval, 5-way,
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| chance 0.20), bind2_0 reaches **0.9988 accuracy** while its matched controls (monolith, bind1-style
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| loop, no-binding control) sit at **0.2125 / 0.1938 / 0.1938** — with a sharp grokking transition between
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| 5M and 10M training tokens (0.179 → 0.969 → 0.996). Learnable exam: achieved.
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| 2. **It does not emerge for free.** Trained as a plain LM on the real BabyLM strict-small corpus, the
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| architecture showed **no emergent zero-shot state-tracking advantage** (on a 60-probe test with chance
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| 0.50, no model — ours or baseline — beat chance).
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| 3. **No general-language tax.** On the official zero-shot evaluation it is **statistically tied with the
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| matched baselines**, slightly above the GPT-2 baseline on BLiMP (66.11 at 23.9M params vs 65.08).
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| Honest summary: **"no tax, no win."** Mechanism capability and benchmark transfer are *separate questions*,
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| and conflating them is how fields fool themselves. What this stage forced next: split the confound — first
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| prove the mechanism is *causally real at depth* under a pre-registered gate, separately from transfer.
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| Weights and code for this stage:
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| [`SecludedCorner/bind2_0`](https://huggingface.co/SecludedCorner/bind2_0)
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| (main = 23.9M build; branch `27m` = 27M build).
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|
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| ### Stage 4 — bind2_1: a NULL that stands, a causal result that survives, and a new finding
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| The successor mechanism (weights/code not released — see below) was tested the hard way: a **10-arm
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| campaign, 5 seeds per arm, with thresholds and the judgment script frozen before any data existed**. The
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| frozen gate was an AND over five criteria. What happened is the most instructive verdict in this log:
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| - **Discrimination passed.** The full system scored **85.83** (pooled deep-rung accuracy) against **every**
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| control arm sitting at chance (≈16.5–17.5, chance 16.98) — a ~69-point margin over each of seven learned
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| controls, on all 5 seeds, against a pre-registered minimum effect of 5 points. A scrambled negative
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| control scored *below* chance (12.67), and an oracle upper bound scored 99.92. An audited shortcut
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| ceiling (the best any state-blind strategy could reach) was ≤19.7 pooled; the system exceeds it by ~66
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| points.
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| - **Causality passed.** Lesioning the mechanism's state pathway removes **99.3%** of the deep-rung
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| advantage while leaving non-query language modeling flat; interchange patching flips **96.4%** of answers
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| to the donor context's holder, with residual specificity 1.0. The deep-rung behavior is carried by the
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| mechanism — necessarily and sufficiently.
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| - **Criterion C failed → verdict NULL.** C required the depth-interaction margin to hold or grow with
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| depth; the observed margins shrink (73.3 → 74.5 → 66.5 → 60.3 across the four deep rungs), which the
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| pre-registration had labeled a "bypass" signal. **The frozen verdict is NULL and it stands permanently.**
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| - **The autopsy — recorded, not used to overturn.** Adversarial post-hoc analysis showed criterion C was
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| **ceiling-confounded**: because chance itself falls with depth, only a near-lossless mechanism (like the
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| oracle, which passes C) *could* pass; a system starting at 96% has nowhere to grow its shallow margin.
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| This is a design-time specification error and we record it as exactly that. The "bypass" *interpretation*
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| of the NULL is, separately, refuted by the causal results: a bypassed module cannot carry 99.3% of the
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| effect, and the deepest rung (72.8) sits ~57 points above the audited shortcut ceiling (≤15.5).
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| - **The real finding: limited effective depth.** The mechanism's engagement decays gracefully with depth
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| (≈95% → 69%) while the oracle stays ≈100% — so depth-robust binding is achievable on this task and the
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| learned mechanism does not fully achieve it. That gap is a concrete architectural target, not a
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| rhetorical one.
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| **Why there is no bind2_1 repo:** the headline number is a teacher-forced readout on a synthetic diagnostic
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| corpus (not BabyLM data), and a standard free-running export does not reproduce it. Publishing weights that
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| cannot reproduce their own headline would be misleading, so this stage ships as numbers-and-narrative only.
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| What is withheld is tooling and weights, not results — the numbers reported here are complete.
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|
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| Because the corrected depth criterion (C′) was formulated after seeing the data, it cannot be scored on
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| that data as anything but exploratory. So C′ was **frozen on 2026-07-15, before touching the five held-back
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| seeds**, and the confirmatory rerun on those untouched seeds landed the same day: **CONFIRMATORY PASS.**
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| Per-rung margins on the fresh seeds — 73.3 / 74.6 / 66.9 / 59.4 points — replicate the original seeds
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| almost exactly; the deepest rung scores 72.7 against a 31.0 shortcut bar; and the causal lesion battery
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| replicates on all five unseen-seed checkpoints (99.30% of the deep advantage removed by the state lesion).
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| One procedural note, disclosed in full: the judgment script as originally frozen demanded control arms the
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| confirmatory design never scheduled and exited without scoring; that output is preserved untouched, and the
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| verdict above comes from a plumbing-fixed variant whose criteria are byte-identical to the frozen ones,
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| adversarially reviewed before unblinding, with the full evidence chain on record internally. The
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| discrimination criterion was inherited from the original seeds, not re-measured. The seeds-0-4 NULL stands
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| unchanged.
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|
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| ### Stage 5 — bind2_1e: the mechanism passes the official exam (single seed, provisional)
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| The transfer question bind2_0 failed ("no tax, no win") could now be asked properly: take the
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| causally-verified binding mechanism, train it on a synthetic box-tracking corpus whose answer statistics
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| 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.
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| Everything was pre-registered and frozen before the run: the corpus ruling, the success/null bands, the
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| shortcut-decomposition gates, the abort rules. Default prediction: NULL.
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| Result (2026-07-15), one shot, first read final: **93.96% pooled** on the two in-scope official subsets
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| (6,259 items; chance 20%; every earlier model in this program — and the published baselines — sits at
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| ≈19–21% on this benchmark). The regular subset scores **99.05% with zero decay across operation depth**
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| (99.4% at the deepest tier); the contents-move subset scores **88.80%** with graceful depth decay. The
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| routing fear died completely: the learned router matches oracle-hinted routing to the third decimal
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| (transfer gap 0.000).
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|
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| The credibility core is the pre-registered decomposition. The benchmark's strongest audited shortcut ("the
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| last-touched box is the answer") can solve ~59% of items; on the **2,550 items that shortcut cannot solve,
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| the model scores 92.4%** — and 96/92/92/92/81% across operation depths 1–5. Empty-box golds (the classic
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| prior-abuse trap from Stage 1) score **97.6%**, with a perfect 100% on the regular subset. The verdict
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| label, per the frozen bands: **MECHANISM-TRANSFER — provisional, single seed.**
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| What this is *not*, stated plainly:
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|
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| - **Single seed means exactly that: provisional until replicated.**
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| - **Subset scope:** the score covers the regular and contents-move subsets only — 6,259 of the benchmark's
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| 9,483 items; the ambiguous-reference subset was excluded by the frozen pre-registration as outside
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| mechanism scope.
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| - **This is a mechanism-transfer demonstration, not a general language model.** The model was trained only
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| on synthetic box-tracking text; every other suite in the official evaluation is expected to sit at chance
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| by design, and no claim is made there.
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| - **The natural-diet baselines are diet-confounded as an architecture comparison.** Their chance-level
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| scores come from natural-text training; the matched-diet architecture attribution rests on the bind2_1
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| campaign's seven matched controls (Stage 4) plus the dedicated matched-diet control reported just below.
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| Nothing from this stage is downloadable at this point: the weights, code, configuration, and the
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| corpus/generator tooling are all withheld at this stage. The frozen pre-registration, the decomposition,
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| and the per-item results are on record internally; the numbers reported here are complete.
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| **Update (2026-07-17) — three pre-registered follow-ups, all resolved in the mechanism's favour.** Each
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| froze its bands before its run; the results:
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|
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| - **Seed replication (now n = 3).** The frozen procedure re-ran on two untouched seeds: pooled **94.12%**
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| and **94.22%**, against the original **93.96%** — a spread of 0.26 points across three seeds, with the
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| shortcut-unsolvable subsets and empty-box golds replicating in lockstep. The "provisional — single seed"
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| qualifier is **removed**: the transfer replicates.
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| - **Matched-diet control (the diet-confound, closed).** A standard attention-only transformer trained on the
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| *identical* box-tracking diet (same token budget, single seed) scores pooled **50.6%** — but on the
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| pre-registered decisive subset, the items the shortcut cannot solve, it scores **23.2% ≈ chance (20%)**,
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| versus this model's **92.6%** on the same subset. Its overall half-score is carried entirely by the
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| empty-box prior and the last-touch shortcut; on the items that require state tracking it floors. A standard
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| architecture on the same diet cannot do it — the advantage is architectural, not a diet artifact.
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| (Single seed = provisional.)
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| - **Mixed-diet stress test.** Retrained on 35% box-tracking + 65% natural text (same 30M-token budget), the mechanism holds: pooled **94.54%** — if
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| anything *above* the boxes-only run (report-only, single seed), all decomposition gates passed by wide
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| margins. On this mixed diet the model also produces real (non-chance) general-language scores; its BLiMP
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| came in at **59.92 against a pre-registered no-tax gate of 60.0** — 0.08 below the line, recorded under
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| the frozen label **TAX-OR-BUDGET**: attribution left open between an architecture cost and the 5×-smaller
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| token budget of this run versus the 150M-token baselines, neither claimed. A separate zero-training probe
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| for transfer to *natural-language* reference tracking returned the pre-registered **NULL** on both primary
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| subjects — expected, since the mechanism's readout is inert on those inputs; recorded as no evidence, not
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| as a mechanism failure.
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|
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| ### Stage 6 — where the line is now
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| Current focus: consolidating the replicated transfer result and its controls.
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|
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| ## The dual-verdict structure, stated explicitly
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|
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| Two verdicts exist for bind2_1 and they answer **different questions**. They are never merged:
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|
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| 1. **The original pre-registered verdict is NULL, permanently.** Criteria and judgment were frozen before
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| data; criterion C failed; the AND-gate returns NULL. Re-labeling it PASS after seeing the data would be
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| criterion-shopping, and the entire point of pre-registration is to make that impossible. The autopsy
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| that found criterion C ceiling-confounded is *recorded alongside* the verdict; it does not modify it.
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| 2. **The corrected-criterion confirmatory run is a separate, second question.** C′ (per-rung margin above
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| the pre-registered minimum effect AND deepest-rung accuracy far above the audited shortcut ceiling) was
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| frozen on 2026-07-15 **before** the five backup seeds were touched; those seeds played no role in
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| designing C′. Its result (2026-07-15): **CONFIRMATORY PASS** — per-rung margins 73.3/74.6/66.9/59.4
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| points on the fresh seeds, deepest rung 72.7 vs the 31.0 shortcut bar, causal battery replicated on all
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| five unseen-seed checkpoints. It answers "does the corrected criterion hold on fresh seeds?" (yes) — it
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| does not, and cannot, retroactively change verdict #1.
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|
|
| ## Numbers
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|
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| ### Mechanism axis (bind2_1 campaign; frozen ladder, synthetic diagnostic exam, teacher-forced readout; accuracy %, mean±SD, n=5 seeds per arm)
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|
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| The seven learned control/ablation arms are anonymized here (A–G);
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| their internal identities and configurations are part of the withheld tooling. The negative-control,
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| upper-bound, chance, and ceiling rows are as frozen.
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|
|
| | arm | R3 | R4 | R5 | R6 | pooled deep (R3–R6) |
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| |---|---|---|---|---|---|
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| | the full system | 96.00±0.00 | 92.90±0.06 | 81.58±0.14 | 72.82±0.21 | **85.83±0.05** |
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| | learned control A | 21.38±0.32 | 17.28±1.22 | 15.10±1.40 | 13.75±1.17 | 16.88±0.14 |
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| | learned control B | 22.30±2.06 | 18.85±1.52 | 14.53±1.48 | 12.53±0.54 | 17.05±0.72 |
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| | learned control C | 21.93±2.01 | 16.48±0.99 | 15.68±1.41 | 12.03±1.14 | 16.53±1.11 |
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| | learned control D | 22.05±0.69 | 18.32±1.38 | 14.35±0.90 | 12.17±0.26 | 16.73±0.72 |
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| | 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 |
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| | learned control G | 21.68±0.89 | 18.60±1.78 | 14.40±0.78 | 12.90±1.38 | 16.89±0.67 |
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| | scrambled negative control | 16.05±0.07 | 14.45±0.07 | 11.25±0.15 | 8.95±0.14 | 12.67±0.05 |
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| | oracle upper bound | 99.95±0.07 | 100.00±0.00 | 99.90±0.10 | 99.85±0.14 | 99.92±0.04 |
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| | *chance* | 22.50 | 18.33 | 14.58 | 12.50 | 16.98 |
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| | *audited zero-state shortcut ceiling* | ≤25.38 | ≤20.50 | ≤17.38 | ≤15.50 | ≤19.69 |
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|
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| Notes (from the frozen source): pooled deep = micro-average over R3–R6, the frozen primary endpoint;
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| pre-registered minimum effect (SESOI) = 5.0 pp on pooled deep; shallow rungs R0–R2 are shortcut-reachable
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| by design (claim-ineligible) and intentionally not tabled.
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|
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| Causal results (frozen gate, criterion B): state-lesion kill = 99.3% of deep-rung advantage with non-query
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| perplexity flat; interchange flip = 96.4%; residual specificity = 1.0; all 5 seeds pass.
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|
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| Depth margins vs learned control F, the pre-registered reference control (criterion C, failed):
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| R3 73.3 / R4 74.5 / R5 66.5 / R6 60.3.
|
|
|
| ### Official axis (BabyLM 2026 evaluation; "pending" = not yet measured)
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|
|
| | 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.
|
|
|
