RiverRider/srt-depth-probe-artifacts

SRT depth-probe artifacts

Where a frozen multimodal backbone carries cross-modal meaning depends on what you use to look. A raw-cosine probe and a fitted linear probe rank the layers of the same model differently, on the same states, in the same run.

Every number here is measured on frozen google/gemma-4-31B-it. Nothing in this repository is a trained product. These are measurement artifacts and the scripts that produced them, published so the ranking can be checked instead of taken on trust.

The result

Nine layers, 5,000 COCO val2017 images, 4,000 fitted and 1,000 held out. Chance is R@1 0.001 and median rank 500. "Cosine" is the head-free read-out. "Fitted" is the shipped head recipe, two 1024-d towers under InfoNCE, fitted per layer on the training split only.

layer	cosine R@1	cosine median	fitted R@1	fitted median
L4	0.013	269	0.080	21
L12	0.047	116	0.175	8
L20	0.100	76	0.293	4
L28	0.015	329	0.172	9
L36	0.068	127	0.240	5
L44	0.137	24	0.254	5
L47	0.167	20	0.230	5
L54	0.160	8	0.193	7
L60	0.003	393	0.108	16

Cosine ranks L47 first. Fitted ranks it fourth, behind L20, L44 and L36. The two probes disagree about the shape of the model.

Two things follow. A depth profile is a joint property of the backbone and the read-out, so "layer k is the alignment peak" is only meaningful with the probe named. And L60, which looks like total collapse under cosine (R@1 0.003, median 393), still supports 108x chance under a fitted head. The information survives, and it is the cosine read-out that loses reach.

Scaling, and where it stops replicating

The L20 advantage grows with fitting data. On val2017, five seeds, the same 1,000 images held out:

train images	250	500	1,000	2,000	4,000
L20 − L47 R@1	+0.009	+0.013	+0.030	+0.032	+0.061

Replicated on a different image distribution, COCO train2017, three seeds, 2,000 held out:

train images	L20	L47	gap
2,000	0.145 ±0.004	0.130 ±0.005	+0.015
4,000	0.192 ±0.009	0.180 ±0.011	+0.012
8,000	0.245 ±0.005	0.219 ±0.003	+0.026
16,000	0.290 ±0.004	0.260 ±0.009	+0.030
28,000	0.332 ±0.004	0.289 ±0.001	+0.043

Scope note. The direction replicates across both image distributions. The magnitude does not. At 4,000 training images the gap is +0.061 on val2017 and +0.012 on train2017, a five-fold difference. Treat the sign as the finding and the size as distribution-dependent.

Further scope: one backbone, one architecture family, COCO images, English captions, retrieval as the task. L47 remains the shipped tap for the SRT-Sunstone artifacts, which were fitted and validated at that layer. Nothing here has been run at deployment scale, and none of it has been tested on a second backbone.

SugarCrepe under a fitted probe

Fitted per layer on the 3,440 val2017 images SugarCrepe does not use, then applied to its 7,511 triples. The replace_obj split, n=1652:

layer	cosine	fitted
L4	0.514	0.557
L12	0.528	0.611
L20	0.571	0.696
L28	0.533	0.574
L36	0.561	0.626
L44	0.617	0.669
L47	0.614	0.661
L54	0.666	0.676
L60	0.541	0.562

Object identity reaches its ceiling at L20 and stays flat, so identity is not a late-stack property under this probe. Arrangement peaks mid-stack instead: fitted swap_obj is 0.596 at L28. The macro ordering also inverts between probes, with cosine best at L54 (0.569) and fitted best at L36 (0.598).

Read the swap splits with care

sugarcrepe_symmetry_audit.json counts, for every split, how many pairs have a positive and negative that are the same multiset of words:

split	n	bag-of-words identical
swap_obj	245	164 (66.9%)
swap_att	666	408 (61.3%)
replace_att	788	0
replace_obj	1652	0
replace_rel	1406	0
add_att	692	0
add_obj	2062	0

For a bag-identical pair, any model whose text representation ignores word order scores both captions the same and lands at chance by construction. Both swap splits are majority such pairs, so a swap score cannot separate content quality across that whole class of models. This is a property of the data and not a defect, since the swap splits exist to probe word order. It does change how the number reads. Reproduce with scripts/sugarcrepe_symmetry_audit.py, which runs on the benchmark JSONs alone and evaluates no model.

Banked null

l60_fitted_readout.json is a failed experiment, published because it failed its own control. A ridge/PCA read-out on 936 images scored 0.5222 at L47 against a head-free 0.5655, so the instrument was weaker than no instrument. The regularisation was also chosen on the test split. The result is void and the later InfoNCE refit (l60_head_refit.json, depth_fitted_profile.json) supersedes it. It is here so the path to the working version is visible.

Other artifacts

• crosslingual_convergence.json. Opus-100 English pivot, frozen L47, 400 pairs per language, per-language centring. English–Mandarin 0.335 (134x chance), English–Arabic 0.297, English–Finnish 0.247. Confound stated in the file: one model was trained on all three languages, so this is not evidence about independently evolved systems.
• space_capacity.json. Retrieval against growing pools. Pool 1,000 gives median 1; 123,287 gives median 70. The pool grows 123x while rank grows 70x. Participation ratio 84.5 of 1024 dimensions, 99% of variance in 210.
• invariant_core.json. 13 multi-backbone claims across 8 hosts, 11 hold. Worth reading for what it concedes: 7 of the 11 describe how to measure, and only 4 describe what a model does.
• sugarcrepe_layer_sweep.json. The cosine-only sweep that the fitted re-test above supersedes.

Reproducing

scripts/ contains the exact code. l60_head_refit.py encodes and fits the depth profile, tap_layer_scaling.py runs the seeded scaling curves, tap_layer_full.py is the full-scale version, sugarcrepe_fitted_depth.py does the compositional re-test. Each writes the JSON it is named after.

Every fit uses a held-out split, and l60_head_refit.py enforces a control: a fitted head at L47 must beat head-free, or the instrument is rejected.

Anisotropy

These states are strongly anisotropic and raw cosine on them is not directly interpretable. Comparisons here are centred by the pool mean. Any reuse of these artifacts should do the same and should report a random floor.

Citation

@software{srt_depth_probe_2026,
  title  = {SRT depth-probe artifacts: probe-dependent layer ranking in a
            frozen multimodal backbone},
  author = {Lancaster, Burton},
  year   = {2026},
  url    = {https://github.com/space-bacon/SRT}
}

Apache-2.0. Source: github.com/space-bacon/SRT.