section float64 5.1 5.4 | check stringlengths 35 108 | passed int64 1 1 | detail stringlengths 5 111 ⌀ |
|---|---|---|---|
5.1 | router_stability = 1.0 after the planted freeze | 1 | min post-freeze stability=1.0000 |
5.1 | crystallisation_step recovers the planted freeze (2000) | 1 | recovered=2200.0 |
5.1 | two INDEPENDENT routers look ~72% stable on raw Hamming but ~0 chance-corrected | 1 | raw=0.718 chance=0.722 adj=-0.013 |
5.1 | a router that never settles returns None (NEGATIVE CONTROL) | 1 | got None |
5.1 | planted segmentation-BEFORE-capability -> 'SEGMENTATION FIRST' verdict | 1 | SEGMENTATION FIRST -- fixed substrate; route |
5.1 | planted capability-BEFORE-segmentation -> 'CAPABILITY FIRST' (NEGATIVE CONTROL) | 1 | CAPABILITY FIRST -- circuits precede stabl |
5.1 | asynchrony_test detects planted late low-resource crystallisation | 1 | rho=0.88 p=0.0269 |
5.1 | tau1_window brackets the planted gap (1600, 4000) | 1 | window=(1800.0, 4200.0) |
5.1 | SYNCHRONOUS languages -> no asynchrony and NO principled tau1 (NEGATIVE CONTROL) | 1 | rho=0.00 p=1.000 window_valid=False |
5.1 | capability_inflection recovers a planted sigmoid inflection (3000) | 1 | got 3400.0 |
5.2 | pool_chunks(mean) is the chunk mean | 1 | null |
5.2 | span-restricted pooling selects only overlapping chunks | 1 | null |
5.2 | planted SHARED geometry -> high whitened alignment gain | 1 | gain=1.000 |
5.2 | planted UNRELATED languages -> ~zero gain (NEGATIVE CONTROL) | 1 | gain=-0.001 |
5.2 | retrieval P@1 after alignment: shared high, unrelated at null | 1 | shared=1.00 unrelated=0.03 |
5.2 | CKA/RSA (rotation-free) separate shared from unrelated | 1 | cka 1.00 vs 0.10, rsa 1.00 |
5.2 | injected anisotropy lowers IsoScore and inflates off-diagonal cosine | 1 | iso 0.061 vs 0.902; offcos 0.16 vs -0.00 |
5.2 | anisotropy_sensitive flag FIRES on anisotropic data | 1 | delta_gain=+0.762 |
5.2 | anisotropy_sensitive flag is QUIET on isotropic data (NEGATIVE CONTROL) | 1 | delta_gain=-0.000 |
5.2 | whitening recovers the true shared geometry under anisotropy | 1 | gain_white=1.000 |
5.2 | pairwise_table reports probe accuracy and a full anisotropy audit | 1 | probe_acc_white=0.30 chance=0.33 |
5.2 | width_sweep recovers a planted 'wider = more shared' trend | 1 | rho(N, sharing)=+1.00 |
5.2 | width_sweep also recovers the accompanying drop in separability | 1 | rho(N, separability)=-1.00 |
5.2 | sweep_verdict states the recovered trade-off in words | 1 | rho(N, separability) = -1.00; rho(N, sharing) = +1.00; wider chunks are MORE shared and LESS language-separated |
5.2 | compare_sources ranks planted sharing correctly (hnet > bpe > byte) | 1 | hnet:0.94 > bpe:0.42 > byte:-0.00 |
5.3 | chunk_index_at / aligned_sites / misaligned_offsets place patches correctly | 1 | null |
5.3 | patch_effect is signed so that POSITIVE = improved prediction | 1 | null |
5.3 | region restriction strips the trivial within-span self-reconstruction credit | 1 | whole-sequence=0.2774 vs after-patch-only=0.1585 |
5.3 | planted transferable content -> aligned patch improves prediction | 1 | effect=0.2342 CI=(0.19806601373115348, 0.27255066410129847) |
5.3 | content-MISMATCHED donor null is centred on ~zero | 1 | null=-0.0122 CI=(-0.029104634476045707, 0.004660881617882052) |
5.3 | aligned effect separates from the null (large d, p < 0.01) | 1 | d=2.13 p=0.0000 |
5.3 | null success rate sits at its 0.05 construction value | 1 | 0.050 |
5.3 | ALIGNED (boundary) patching beats MID-CHUNK patching | 1 | delta=0.0699 p=0.0290 d=0.40 |
5.3 | no-transfer model -> no significant effect (NEGATIVE CONTROL) | 1 | effect=0.00e+00 p=1.000 |
5.3 | compare_checkpoints detects planted B > A transferability | 1 | B-A=0.1966 d=1.52 p=0.0000 |
5.3 | identical checkpoints -> hypothesis NOT supported (NEGATIVE CONTROL) | 1 | B-A=0.00e+00 p=1.000 |
5.4 | UD treebanks present on disk (>= 20 languages) | 1 | 27 treebanks: am_att, ar_padt, cy_ccg, de_gsd, en_ewt, es_gsd... |
5.4 | load_ud reconstructs the surface string exactly (byte offsets are valid) | 1 | mean=0.9995, worst=fi_tdt 0.986 |
5.4 | SIGMORPHON 2022 loader keeps only SURFACE segmentations (offsets exist) | 1 | 40107 surface items, kept_frac=0.699 |
5.4 | MorphyNet inflectional loader yields valid morpheme offsets | 1 | 5000 items, surface_frac=0.718 |
5.4 | planted 85%-recall router scores high F1 above a rate-matched random baseline | 1 | F1=0.846 random=0.071 above_chance=0.834 |
5.4 | a RANDOM router at the same rate scores ~0 above chance (NEGATIVE CONTROL) | 1 | F1=0.051 above_chance=-0.027 |
5.4 | AUROC of a signal-carrying router score is high; a random score is ~0.5 | 1 | signal=0.907 random=0.556 |
5.4 | EMA acting only on the low-confidence band is distinguishable from a entropy-matched generic regulariser | 1 | KS_lowconf ema=0.283 generic=0.190 |
5.4 | a 'smoothing module' that is really just tempering is NOT distinguishable (NEGATIVE CONTROL) | 1 | KS_lowconf ema=0.190 generic=0.190 |
5.4 | level_typing calls the dense level morph-like and the sparse level word-like | 1 | L1=morph-like (m=-0.35), L2=word-like (m=+0.35) |
5.4 | unit_length_stats reports both byte and character widths (continuous-script languages need chars, not bytes) | 1 | {'mean_bytes': 12.64, 'median_bytes': 12.0, 'n_units': 42, 'mean_chars': 4.31, 'median_chars': 4.0} |
5.4 | alignment_predicts_downstream reports a weak/no relation as such (the MorphScore-70 caveat) | 1 | R2=0.072 p=0.253 |
5.4 | ...and still detects a genuinely strong relation (NEGATIVE CONTROL) | 1 | R2=0.931 p=0.0000 |
H-Net dynamic-chunking: experiment results
Every result table behind the study, including the ones that failed. 21 experiments; each
directory has a RESULTS.md alongside its machine-readable CSV/JSON.
Headline findings
| experiment | question | verdict |
|---|---|---|
exp21_tier1_pilot |
does a parity objective equalise chunk allocation? | each beta variant equalises the denominator it targets (A→B +62.1% cps; Apc→Bpc +51.9% cpc), attributable — EMA and generic-regularisation controls do not reproduce it |
exp26_crystallisation |
do boundaries stabilise before capability? | no — capability first, 36/36 language-seed pairs; 20/36 never crystallise by step 6000 |
exp27_chunk_content |
are learned chunks linguistic? | no — morpheme F1 at or below rate-matched chance; gold features add ≤0.001 AUROC over computational features in 9/9 languages |
exp25_chunk_patching |
are chunks functional computational units? | no — aligned-minus-misaligned at the construction rate (0/28 cells >2 SD), while killing the chunk pathway costs +0.34 nats/byte |
exp28_difficulty_gate |
does chunking track difficulty? | partial — boundary rate yes (+0.181 ± 0.057, 12/12 languages); chunk length no (wrong sign) |
exp28_compute_knob |
is there an inference-time compute knob? | no — one-sided; BPB is minimised at the training threshold and worse in both directions |
exp28_mainnet_ablation |
how much is the chunk network worth? | ~0.07 BPB, not the +0.51 a naive off-distribution ablation reports |
exp24_neural_chunkers |
how do released chunkers segment? | Bolmo-1B matches its distillation teacher at boundary F1 0.987 after 39.3B tokens |
Reporting discipline
Pre-registered and applied throughout: every effect carries a null (shuffled, circular-shift,
Gaussian-donor, or content-mismatched donor as appropriate); spread is reported across
seeds, never pooled over items; boundary F1 always carries its rate-matched random
baseline; FLOP claims state the counting rule (block(T,d) = 24 d²T + 2dT², causal-halved)
and use measured chunk counts; both directions of every result were treated as publishable.
exp23_interp_validation validates the estimators against synthetic ground truth with a
negative control for each positive check (49/49 passing).
Limitations
16.2M/22.5M params, 6000 steps, 3 seeds, one hierarchy stage. Only the final training step
was retained, so all causal analysis is at step 6000 — which exp26 shows is before
convergence for the low-resource half. Entropy is a byte 5-gram proxy; the model's own
predictive distribution was never logged.
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