--- 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`](https://huggingface.co/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//zero_shot/causal/// best_temperature_report.txt # official aggregate report predictions.json # per-item predictions results/hf_loop2_full/finetune//results.txt # official GLUE protocol (entry) results/hf_baseline_full/finetune//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: ```bash 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.