SecludedCorner/bind1-babylm2026-eval-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 = real signal. Both verdicts occur in this data — ~25% in the three healthy-write seeds, 37–45% in the collapsed-write seed — which is the evidence behind the model card's "iteration as a backup pathway" note.

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.