license: cc-by-4.0
language: en
tags:
- babylm
- babylm-2026
- strict-small
- evaluation-artifacts
- reproducibility
pretty_name: bind1 (BabyLM 2026 Strict-Small) — full evaluation artifacts
bind1 — BabyLM 2026 Strict-Small entry: full evaluation artifacts
Complete, per-item evaluation outputs for the
SecludedCorner/bind1-babylm2026-strict-small
entry and its ablation families, released so that every number we report is downloadable and
recomputable — including the negative results.
All outputs were produced by the official babylm-eval pipeline (zero-shot backend causal;
GLUE via the official fine-tuning protocol). Reports are the pipeline's own
best_temperature_report.txt; predictions are per-item.
Model families included (35 evaluation targets)
| Directory pattern | What it is | What it shows |
|---|---|---|
hf_loop2_full(_sN) |
the entry architecture, trained loop depth T=3, seeds 0–3 | main results |
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 |
hf_loop2_sever(_sN) |
same weights, verdict-to-trust edge zeroed at inference | inference-inertness of the feedback edge (4/4 seeds) |
hf_loop2_novg(_sN) (+_ti1) |
retrained from scratch with the edge frozen | training-scaffold evidence (seed-conditional: 3/4) |
hf_baseline_full(_sN) |
size-matched plain-decoder monolith, seeds 0–3 | the paired control for every claim |
hf_loop_full |
the earlier bypassed variant (v=1.0, loop inert) | the audit-caught failure kept as a diagnostic |
Internal (logical) experiment ids map to these physical names as
bind1_tt3_sN = hf_loop2_full(_sN), bind1_tt3_sN_ti{K} = hf_loop2_T{K}(_sN),
mono_sN = hf_baseline_full(_sN), bind1_tt3_sN_novg = hf_loop2_novg(_sN).
Layout
results/<model>/zero_shot/causal/<task>/<subset>/
best_temperature_report.txt # official aggregate report
predictions.json # per-item predictions
results/hf_loop2_full/finetune/<task>/results.txt # official GLUE protocol (entry)
results/hf_baseline_full/finetune/<task>/results.txt # official GLUE protocol (control)
scripts/oracle_best_of_T.py # best-of-T oracle + chance-null recovery analysis
scripts/make_t_variant.py # builds the forced-T inference variants (config-only clones)
patches/compute_results_per_item_is_correct.diff # additive patch to the official pipeline
Per-item correctness (is_correct)
Entity-tracking predictions.json records include pred_idx / gold_idx / is_correct,
added by the additive patch in patches/ (the official id/pred fields and all scoring
logic are untouched; patched reruns reproduce the official aggregates exactly, 12/12 checked).
This is what makes the chance-null analysis possible: for answers the single-pass model got
wrong and iteration changed, a memoryless re-roll lands on gold ~25% of the time (5 options).
Recovery at ~25% = re-rolled noise; recovery far above = signal. Both verdicts occur in
this data under the pre-revision item set — ~25% in the three healthy-write seeds, 37–45% in the
collapsed-write seed — which was the evidence behind the model card's original "iteration as a backup
pathway" note.
Post-revision update (2026-07-09): under the leaderboard's revised standard (drop "nothing"-gold items) the 37–45% cells fall to 23.4–24.7% ≈ null; the model card note has been updated accordingly and the backup-pathway reading retired. The per-item files here let you reproduce both readings.
Recompute it yourself:
python scripts/oracle_best_of_T.py bind1_tt3_s0 # any of s0..s3
(The script expects the experiment-archive layout; adapt the paths at the top, or read the
predictions files directly — the schema is one JSON object per subset with a predictions list.)
Scope note
This dataset now covers both the entry generation's ablation families and the
training-time-depth study (hf_bind1_tt1_s0..s9: ten seeds trained single-pass — the
capability never forms; see the ablations model repo for the loadable checkpoints). Nothing in this dataset
was selected post-hoc: every ablation family we ran on the entry generation is present,
including the ones that falsified our own pre-registered predictions.
Citation
Yulin Yang (ORCID 0009-0007-4827-8449). A Microkernel Language Model: Reasoning as Mutually-Supporting Aggregates, and Why Its Parts Must Be Judged Together. BabyLM Challenge 2026 (Strict-Small track) submission.