| ---
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| license: cc-by-4.0
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| language: en
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| tags:
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| - babylm
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| - babylm-2026
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| - strict-small
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| - evaluation-artifacts
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| - reproducibility
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| pretty_name: bind1 (BabyLM 2026 Strict-Small) — full evaluation artifacts
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| ---
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|
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| # bind1 — BabyLM 2026 Strict-Small entry: full evaluation artifacts
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| Complete, per-item evaluation outputs for the
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| [`SecludedCorner/bind1-babylm2026-strict-small`](https://huggingface.co/SecludedCorner/bind1-babylm2026-strict-small)
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| entry and its ablation families, released so that **every number we report is downloadable and
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| recomputable** — including the negative results.
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|
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| All outputs were produced by the official `babylm-eval` pipeline (zero-shot backend `causal`;
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| GLUE via the official fine-tuning protocol). Reports are the pipeline's own
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| `best_temperature_report.txt`; predictions are per-item.
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|
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| ## Model families included (35 evaluation targets)
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| | Directory pattern | What it is | What it shows |
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| |---|---|---|
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| | `hf_loop2_full(_sN)` | the entry architecture, trained loop depth T=3, seeds 0–3 | main results |
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| | `hf_loop2_T1/T2/T4(_sN)` | same weights, forced inference depth T=1/2/4 | the T-sweep: iteration helps grammar in all seeds, erodes healthy-write entity tracking, and partially rebuilds the one collapsed write |
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| | `hf_loop2_sever(_sN)` | same weights, verdict-to-trust edge zeroed at inference | inference-inertness of the feedback edge (4/4 seeds) |
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| | `hf_loop2_novg(_sN)` (+`_ti1`) | retrained from scratch with the edge frozen | training-scaffold evidence (seed-conditional: 3/4) |
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| | `hf_baseline_full(_sN)` | size-matched plain-decoder monolith, seeds 0–3 | the paired control for every claim |
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| | `hf_loop_full` | the earlier bypassed variant (v=1.0, loop inert) | the audit-caught failure kept as a diagnostic |
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| Internal (logical) experiment ids map to these physical names as
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| `bind1_tt3_sN` = `hf_loop2_full(_sN)`, `bind1_tt3_sN_ti{K}` = `hf_loop2_T{K}(_sN)`,
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| `mono_sN` = `hf_baseline_full(_sN)`, `bind1_tt3_sN_novg` = `hf_loop2_novg(_sN)`.
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|
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| ## Layout
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|
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| ```
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| results/<model>/zero_shot/causal/<task>/<subset>/
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| best_temperature_report.txt # official aggregate report
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| predictions.json # per-item predictions
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| results/hf_loop2_full/finetune/<task>/results.txt # official GLUE protocol (entry)
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| results/hf_baseline_full/finetune/<task>/results.txt # official GLUE protocol (control)
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| scripts/oracle_best_of_T.py # best-of-T oracle + chance-null recovery analysis
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| scripts/make_t_variant.py # builds the forced-T inference variants (config-only clones)
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| patches/compute_results_per_item_is_correct.diff # additive patch to the official pipeline
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| ```
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| ## Per-item correctness (`is_correct`)
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| Entity-tracking `predictions.json` records include `pred_idx` / `gold_idx` / `is_correct`,
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| added by the **additive** patch in `patches/` (the official `id`/`pred` fields and all scoring
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| logic are untouched; patched reruns reproduce the official aggregates exactly, 12/12 checked).
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| This is what makes the chance-null analysis possible: for answers the single-pass model got
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| wrong and iteration *changed*, a memoryless re-roll lands on gold ~25% of the time (5 options).
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| Recovery at ~25% = re-rolled noise; recovery far above = real signal. Both verdicts occur in
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| this data — ~25% in the three healthy-write seeds, 37–45% in the collapsed-write seed —
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| which is the evidence behind the model card's "iteration as a backup pathway" note.
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| Recompute it yourself:
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|
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| ```bash
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| python scripts/oracle_best_of_T.py bind1_tt3_s0 # any of s0..s3
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| ```
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| (The script expects the experiment-archive layout; adapt the paths at the top, or read the
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| predictions files directly — the schema is one JSON object per subset with a `predictions` list.)
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|
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| ## Scope note
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| This dataset now covers **both** the entry generation's ablation families and the
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| **training-time-depth study** (`hf_bind1_tt1_s0..s9`: ten seeds trained single-pass — the
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| capability never forms; see the ablations model repo for the loadable checkpoints). Nothing in this dataset
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| was selected post-hoc: every ablation family we ran on the entry generation is present,
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| including the ones that falsified our own pre-registered predictions.
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|
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| ## Citation
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| Yulin Yang (ORCID 0009-0007-4827-8449). *A Microkernel Language Model: Reasoning as
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| Mutually-Supporting Aggregates, and Why Its Parts Must Be Judged Together.* BabyLM Challenge
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| 2026 (Strict-Small track) submission.
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|
