Datasets:

SecludedCorner
/

bind1-babylm2026-eval-artifacts

@@ -1,84 +1,84 @@
----
-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/<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:
-```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 (honest boundary)
-These are the artifacts of the **2026 entry generation** (bind1). Artifacts of the
-training-time-depth study that motivates the 2027 generation are withheld until a companion
-paper clears double-blind review, and will be added here afterwards. 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.

+---
+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/<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:
+```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.