RiverRider/srt-browser-head-118k

SRT Browser Read-Out Head (118K)

A 2.1 MB linear head that lets a 0.6B-parameter quantized language model, running entirely in a web browser, search 123,287 photographs that a 27B-parameter model encoded offline.

Try it: 0.6B reads 27B. Static files, no inference server, works offline after the first load.

The large model is never downloaded and never runs. It encoded the gallery once and left behind a file. The small model meets it in that space.

The head reads a hidden state from the same frozen weights that write the reply: one model doing two jobs, with no second encoder, no extra parameters, and no server round-trip. It is a second read of your message rather than a reuse of the generation pass. We tried to merge them and measured that it does not work. The head needs your bare message; handing it the chat-formatted prompt instead moves the correct photograph from median rank 187 to 41,014 of 123,287 on the probe used for that test, and the two encodings share a cosine of 0.10. Two passes are forced, and each is one matrix multiply on top of a hidden state the model was computing anyway.

What it does

Text-to-image retrieval, 123,287-image gallery	R@1 0.1108, R@5 0.2510, R@10 0.3373, median rank 33
Image-to-text, 5,000-image val pool	R@1 0.5348, R@5 0.8124, R@10 0.8952
Text-to-image, 5,000-image val pool	R@1 0.3985, R@5 0.6856, R@10 0.7887
Shuffled-pair control	0.0002

Median rank 33 out of 123,287 puts the correct image in the top 0.027% of the gallery, which is the number we would ask you to look at. R@1 on a 123K pool is a harsh summary: at that scale many photographs are equally good answers to a caption, and the metric scores a better match than the gold image as a miss.

Always read a retrieval number with its pool size, and with the head it was measured on. The v2 head and its captions score R@1 0.4959 against 1,000 images and 0.0628 against 123,287, which is the cost of pool size on one head, not a figure for the head that ships. Numbers quoted without a pool size are uninterpretable, and numbers quoted without a checkpoint are worse; both mistakes are ours and both were caught in public review.

Files

file	size	what it is
head_v3.safetensors	2.1 MB	the head that ships. Text side only, fp16
browser_head_v2_best40.pt	25.2 MB	full v3 training checkpoint, both sides, fp32
gallery_123k_v3.srtidx	130 MB	123,287 COCO images projected by the v3 head, int8
anchor_candle_q4_text.bin	4 KB	runtime recalibration for candle/Q4_0 — read §Anchor
browser_head_v3_report.json	—	v3 evaluation, val pool and full gallery
browser_head_v2_arms.json	—	six-arm ablation with predictions registered before running
browser_head_118k.pt	25.2 MB	previous head (v2), superseded
gallery_123k_v2.srtidx	130 MB	previous gallery, superseded

Only the first, third and fourth files reach a visitor. The image head never ships: it runs offline, and the browser receives its output, not its weights.

The anchor is not optional

A head fitted against PyTorch/fp16 states does not transfer unchanged to candle/Q4_0, and the failure is silent — you get confidently ranked results with plausible scores that are simply the wrong images.

Measured on the head and gallery that ship, mean-pooled as the browser pools, 5,001 val captions against all 123,287 images:

runtime	t2i R@1	R@5	R@10	median rank
PyTorch fp16 (reference)	0.1092	0.2442	0.3307	36
candle Q4_0, head as-is	0.0000	0.0000	0.0002	44,578
candle Q4_0, + 4 KB anchor	0.0350	0.1062	0.1518	176

Without the anchor the read-out is at zero by recall: not degraded, gone. The sentence that used to follow this one said the unanchored arm was at chance by median too. That was wrong, and Dipankar Sarkar caught it by reading our own control arms back at us.

Analytic chance over this gallery is 61,644. We now have 17 measured null arms at n>=200, from the verbalizer's mean-record and wrong-record controls scored against this same 123,287-image gallery. They run from 56,954 to 64,085 with a mean of 60,994, so the analytic figure is well calibrated. The unanchored arm sits at 44,578, which is 12,376 ranks below the lowest null we have ever measured. Dead by recall, yes. At chance by median, no. Something survives the runtime change that R@1 cannot see.

That control has now been run, and the residual is signal. Each of the 5,001 captions was scored against a different photograph, same queries and same gallery, with only the pairing broken. That is the empirical floor for this query distribution, which analytic chance cannot supply once the queries are collapsed:

arm	R@1	median	floor, pairing broken
candle Q4_0, head as-is	0.0000	44,578	64,481
candle Q4_0, + 4 KB anchor	0.0350	176	59,071

Both floors are clean nulls at R@1, R@5 and R@10. The unanchored arm sits 19,903 ranks above its own floor while scoring exactly zero recall. The empirical floor is worse than analytic chance, so the effect is larger than the analytic comparison implied rather than smaller.

One warning for anyone reproducing this. The obvious implementation, rolling the gallery by one position, is wrong here and we nearly published it. COCO gives five captions per image and the replay file is grouped by image, so caption i+1 owns the same photograph 80% of the time. That version reported a floor of 48,640 and looked like proof the residual was an artifact. It was the true pairing in disguise. browser_rung now jumps half the caption list, walks until the image genuinely differs, and prints the collision count so the control is checked rather than trusted. Artifact: browser_rung_mismatched_123k.json.

The 4,096-byte mean vector measured on 200 held-out sentences takes it to 32% of the fp16 reference at R@1. That is the number to plan a port around, and it is much less than the 85% this card previously advertised.

Read the median column alongside it, because R@1 alone is harsh on this arm. After recalibration the median correct image sits at 176 of 123,287, the top 0.14% of the gallery. The anchored Q4 read-out usually puts the right photo near the top; it is just rarely first. Whether that is usable depends entirely on the product: it is weak for "I feel lucky" and perfectly serviceable behind a grid of results or a reranker.

That 85% (0.2300 → 0.0154 → 0.1952) was real but was measured on an earlier 4,000-image head against a 1,000-image pool, and it was published here as though it described the deployment. It does not. Recovery degrades sharply with pool size, because the residual error the anchor cannot remove competes against 123,287 distractors instead of 999. Caught in public review by @dipankarsarkar, who traced the head identity through the artifact chain.

Two things follow for anyone porting this. The anchor is still the difference between a working system and a dead one, so it is not optional. And it is not sufficient: a read-out crossing runtimes should be measured end-to-end at the pool size it will actually serve, because both the collapse and the repair look completely different at 1,000 images than at 123,287.

This failure is invisible to agreement metrics, which is why we flag it loudly. Agreement applies the same transform to both sides of its comparison, so a consistent displacement reads as healthy. Only a task with external ground truth catches it. If you port this head to a new runtime, measure recall, not agreement.

Architecture

image (offline, datacenter)          text (in the visitor's browser)
  Qwen/Qwen3.8-27B, layer 52           Qwen3-0.6B Q4_0, layer 28 (final block)
  mean-pool over image tokens          mean-pool over positions
  center by image anchor               center by text anchor  <- 4 KB, per runtime
  linear -> 1024, L2-norm              linear -> 1024, L2-norm
        |                                        |
        +----------- dot product -----------------+
                  int8 index, 123,287 rows

Both sides are linear. This is deliberate and it is measured: a non-linear image head, which costs zero shipped bytes because the image side never ships, was the single worst thing we tried (−0.108 R@1). Free in bytes is not free in statistics.

Tap layer 28 is the last block of Qwen3-0.6B. Our layer scan was still climbing when it ran out of model (L22 0.293, L25 0.351, L28 0.401), so depth is an open boundary here, not a tuned choice.

How it was trained

• 118,287 COCO train2017 images, all 5 captions each, one sampled per epoch so that five captions of one image never sit in a batch as false negatives
• symmetric InfoNCE, 40 epochs
• both backbones frozen throughout; only the two projections train

The v3 recipe came from a six-arm ablation in which four of six registered predictions were wrong:

arm	i2t R@1	Δ
baseline (1 caption, last-token)	0.4236	—
mean pooling	0.4668	+0.043
5 captions	0.4552	+0.032
3-layer concat	0.4192	−0.004 (null, costs 4 MB)
40 epochs	0.4112	−0.012
non-linear image head	0.3154	−0.108
everything at once	0.4636	+0.040
mean pooling + 5 captions, 40 ep	0.5348	+0.111

The two winners are super-additive: +0.043 and +0.032 alone, +0.111 together. Note that the all arm scored below mean pooling alone, because it bundled the winners with three levers that hurt. An "everything" arm is not a substitute for testing the winners together.

More epochs hurt with one caption per image (−0.012) and helped with five (+0.010). Reporting "more epochs hurt" without that conditional would have been wrong.

Gallery index format

SRTIDX02: magic | dim u32 | count u32 | scales f32×count | int8 data | length-prefixed keys. Rows are unit-norm, so one symmetric scale per row is sufficient.

int8 storage is free on this task:

store	t2i R@1	123K resident
f32	0.2300	505 MB
f16	0.2300	252 MB
int8, per-row scale	0.2306	127 MB

Those three rows are the earlier 4,000-image head against a 1,000-image pool, which is fine for the question they answer, because all three arms share a head and a pool and only the storage changes. Read them as "quantizing the gallery costs nothing", not as a figure for the shipped head.

The reader is srt-geometry, a Rust crate with no model dependency that builds for both native and wasm32-unknown-unknown. Python writes the format, Rust reads it, and top-1 agreement between int8 and f16 is 1.0000 over the parity fixture.

Steering

A direction in head space shifts what the gallery returns: q' = normalize(q + α·axis). Axes are built from captions and applied to image queries, so a positive result is a claim about a shared space rather than about memorized neighbours.

Calibrate on retention — the share of the query's own unsteered top-k that survives — not on how strong the effect looks:

α	class purity	random control	retention
0	0.024 / 0.004 / 0.009	same	1.00
0.5	0.252 / 0.117 / 0.128	0.009–0.024	0.61–0.77
1.0	0.744 / 0.640 / 0.587	0.010–0.025	0.13–0.29
2.0	0.908 / 0.851 / 0.895	0.013–0.029	0.01–0.03

Purity climbing to 0.9 is not a success. At α = 2 retention is 0.01: the axis has replaced the query, and every input returns the same images. DEFAULT_ALPHA = 0.5 is set on retention. 32 matched-norm random axes stay flat at baseline throughout (z = 108–302 for the real axis at the operating point), so the direction carries meaning rather than degrading the query into a class prior.

This is query-side steering only. It does not touch generation.

Limitations

• One backbone pair, one domain: Qwen3-0.6B text against Qwen3.8-27B image, COCO only.
• The anchor is validated on one runtime pair. The silence argument is structural; we have measured it once.
• The text tower is the bottleneck and we did not test a larger one, because the deployment budget fixes the payload.
• Steering is validated on keyword-defined classes, chosen to be objective rather than subtle.
• Generation quality is not evaluated. A 0.6B model answers; we make no claim about how well.
• COCO licensing applies to the images; the gallery here contains projected vectors and keys, not photographs.

Running it on a phone

The browser tier runs on iOS Safari, but only because the model is parsed directly out of a Blob rather than staged in WebAssembly memory first.

Staging is the obvious implementation and it is fatal here. The 382 MB file exists twice while candle converts it into tensors, and wasm linear memory grows but never shrinks, so the doubled peak is the permanent footprint rather than a spike. iOS terminates the tab. Reading through a Blob with FileReaderSync costs the model only its tensors.

Smaller payloads do not substitute for this. Shrinking the gallery from 130 MB to 2 MB changed nothing, and candle cannot read llama.cpp's i-quants, so the smallest usable quantization is Q2_K at 296 MB, 22% under Q4_0. The copies were the problem, not the size.

Smaller gallery shards are published anyway for memory-constrained tiers: gallery_20k_v3.srtidx (21 MB) and gallery_2k_v3.srtidx (2.1 MB). Recall improves as the pool shrinks, so quote the pool size with any number from them.

Reproducing

Every number above is backed by a committed JSON artifact in space-bacon/SRT under artifacts/nla/q4/, with a claim-to-artifact mapping in arxiv_deploy/README.md. The raw pre-projection hidden states are published as RiverRider/srt-qwen38-coco-states, so a new head can be fitted without re-encoding 118K images on a 27B model.

The gallery encoder refuses to run until it reproduces vectors already in the shipped gallery to cosine > 0.99. A pooling or layer mismatch produces vectors that look entirely reasonable on their own and are quietly incomparable with what they are meant to extend.

Citation

@techreport{lancaster2026readeverywhere,
  title  = {Read Everywhere, Verify There: What It Takes to Put a
            Frozen-Model Read-Out on the Visitor's Hardware},
  author = {Lancaster, James Burton},
  year   = {2026}
}

@techreport{lancaster2026trainonce,
  title  = {Train Once, Read Everywhere: Substrate Invariance of the Linearly
            Readable Structure in Frozen Language Models},
  author = {Lancaster, James Burton},
  year   = {2026},
  type   = {SSRN Working Paper},
  number = {7264778},
  url    = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7264778}
}