d1 Browser Decision FP32

An audio-free derivative of LiquidAI/d1-omni-600M. It directly scores named options for choice, noul and ordinal score questions using text/JSON state and optional screenshots. It does not generate text or tokens. Do not call generate() or treat it as a causal chat model.

The reported quality data are authored synthetic browser pages, with AI-authored labels independently checked through AI DOM/pixel review, not human-expert annotations. Actual Chrome/WebGPU runtime parity was measured separately; that does not establish reliable completion detection on independently developed websites.

Immutable Candidate

The published bytes are the already validation-selected projector_head_text_lora epoch 06 from the balanced-V3 experiment. The lock was written before held-out model evaluation. Packaging performed no training, new selection, weight transfer, merge, quantization or ONNX re-export.

• Source revision: 02b55d7076f15129e59ab3f94783f32c4b088674.
• 380 clean FP32 tensors: text encoder, decision head, vision tower and projector; no audio or LM head.
• Prior adaptation updated 31 head tensors, four projector tensors and four encoder Q/V matrices through one rank-4, alpha-8 LoRA merge. The other 341 tensors, including all 197 vision-tower tensors, remain exact original-A values.
• Physical model.safetensors SHA-256: 75ab6d7d0ec2966c969a95c548b91f4015b07cba82fa839fcfb5c19b40e9f940 (1,899,912,876 bytes).
• Logical clean-tensor-state SHA-256: 76409dd958673e2028f1da23a909033876169f89603c16c7cbcdfbcc7404cdb5. This is not the file SHA-256.
• Selected delta SHA-256: 82b6844524adf1ff1f7add1c9ef57475af5fcfa07ada10b9edf70c4d24c1ba35.
• Selection-lock SHA-256: 4ab2441d9b3cf7742957a374988fc50fc400b29081b7c38b6920be88cd65bb19.

The three ONNX graphs and their external data total 1,900,356,386 bytes. They are full FP32, unquantized, and copied without binary changes. package-manifest.json and SHA256SUMS enumerate payload checksums; large files also have 4 MiB chunk checksums.

Config caveat: the unchanged legacy config.json says dtype: float16 and architectures: [NoAudioModel]. Actual checkpoint/graphs/feeds are float32, as explicitly required by the release manifest. Do not derive precision from the legacy label. This repository does not advertise an AutoModel.from_pretrained() loader or executable remote Python code.

Synthetic Held-Out Results

New final test: 72 screens, 144 questions (126 categorical choice/noul, 18 ordinal score). Old test: 60 screens, 120 questions (90 categorical, 30 score). All four historical stages are shown, not only the released one.

The released stage has one false completion among 48 non-complete/uncertain new-test cases: 0/24 known negatives and 1/24 uncertain cases. It has two missed new-test positives. All four stages miss all seven positive completion examples in the older test. No positives are predicted there, so old-test completion precision is undefined; zero false positives is not evidence of successful completion recognition.

The LoRA stage did not add new-test categorical accuracy over projector+head. It remains the release candidate because selection was validation-only; held-out results did not reselect it. The original unlabelled 58-request/70-question regression suite is not an accuracy benchmark: the released stage changes 9/46 choice/noul decisions versus current original A (maximum probability drift 0.734239; maximum expected-score drift 1.496044).

Limitations include synthetic layout/text/color regularities, finite family splits, overconfidence/train-versus-validation loss separation, residual Ready-identifier/completion association (0.622556 bits within sparse target groups), and joint family/option-position association (0.584963 bits). Zero conditional viewport MI in the specified QC groups is not proof of universal nuisance independence. No independently developed real-site dataset was collected for this release.

Measured Runtime Scope

The existing own-checkpoint FP32 source API was compared with desktop ONNX and actual Chrome 154 WebGPU on an NVIDIA RTX 5090, non-software adapter. Fixtures were 72 validation screenshots plus 20 neutral text requests: 92 requests/169 questions, not held-out quality examples. Each browser path had two warm-ups and ten hot repeats; all 145 categorical questions agreed on every hot repeat. Maximum absolute probability/expected-score errors stayed below the unchanged 0.001 gate.

Tokens, pixels, masks and shapes matched their references exactly. Position-interpolation FP32 order differences reached 0.000000774860; the trained intermediate media-prefix difference reached 0.0565567. There is no claim of bit-exact intermediate activations or a 0.001 intermediate gate.

Observed hot p50/p95 milliseconds on that machine: text decision 30.778/151.935; saved image-prefix decision 67.290/272.953; full image inference 219.943/751.239; PNG preprocessing plus inference 337.175/957.691. These finite request-balanced measurements are not throughput guarantees. Model/graph storage bytes are not VRAM consumption.

Separate three-fixture profiling observed 1,465 WebGPU nodes and 407 CPU/WASM nodes, including four floating mask/position construction nodes; this is not pure GPU execution. Separate bounded memory sampling observed adapter-total memory, not isolated model VRAM. Existing runner parity is not, by itself, proof of a newly integrated packed WebBrain extension.

Adapter Use

The package contains the dependency-injected adapter copied from the actual WebBrain extension module src/chrome/src/providers/d1-runtime.js. Its preprocessing helper preserves the old verified bytes except two explicit source-semantic corrections: present-null noul criteria (Object.hasOwn, matching Python dict.get) and small fractional Python-style JSON exponent formatting. The old baseline helper is untouched. JavaScript numbers cannot recover Python int-versus-integral-float lexical types: use preformatted state/criterion strings when exact original lexical representation matters. The adapter also restores the source 65,536 padded-token subbatch budget while returning named answers in original order. Executable JavaScript/WASM must be bundled locally for extension CSP; do not load remote executable code. Immutable-revision model graph/tokenizer downloads are data and must be checksum-verified before caching/creating sessions.

The exact API is createD1Runtime({ort, tokenizer, config, ratios, sessions, device, model}), then evaluate({state, images, questions, signal}). See runtime/README.md for session wiring. noul answers expose the source public yes-probability; score answers expose expected ordinal level, not argmax-class accuracy. Option insertion order, masks, media prefix, calibration and the image text limit of 896 tokens must remain unchanged.

import { createD1Runtime } from './runtime/d1-runtime.js';

// Supply verified package assets, the bundled ORT/tokenizer, and three FP32 sessions.
const judge = createD1Runtime({ ort, tokenizer, config, ratios, sessions, device, model: 'd1-browser-decision-fp32' });
const result = await judge.evaluate({
  state: { task: 'Check whether a visible receipt establishes completion.' },
  images: [inlinePngDataUrl],
  questions: {
    completion: { type: 'choice', instructions: 'Judge only visible evidence.', criteria: {
      completed: 'An explicit receipt confirms the named task.',
      not_completed: 'Visible evidence establishes failure or an unfinished task.',
      unknown: 'The screenshot does not establish the outcome.'
    } }
  }
});
// result.usage.output_tokens === 0; this is not text generation.

License And Notices

The model is governed by the exact upstream LFM Open License v1.0, not Apache/MIT. Its commercial-use provisions include annual-revenue threshold terms of US$10 million; determine eligibility and obtain any required separate license before commercial deployment. This card is not legal approval or an endorsement by Liquid AI.

NOTICE and modified-binary sidecars retain attribution and identify the historical derivative changes without changing verified binary bytes. The underlying LFM2.5-Encoder license reference is separately retained; that license-reference revision is not a claim about the historical base-weight revision used by d1. Bundled ONNX Runtime 1.31.0-dev.20260914-8d85527a0 is MIT-licensed; Transformers.js 4.3.1 is Apache-2.0-licensed. The actual WebBrain GPL-3.0-or-later project notice is retained for the copied runtime source at licenses/webbrain/LICENSE. Separate component notices do not replace the model license or constitute a legal compatibility/commercial-eligibility opinion.

Native WebGPU Runtime Dependency Correction

This revision retains the same checkpoint, six ONNX graph/data files, tokenizer, config, decision adapter and preprocessing helper. It adds the exact same-version ORT asyncify MJS/WASM pair and corrects the local loader example. The native WebGPU bundle calls webgpuInit; explicitly forcing the JSEP factory (jsepInit only) failed during an actual packed-extension initialization before neural inference. The earlier successful numerical browser runner used the same ORT distribution's directory-prefix loader, which selected the native asyncify pair. Existing JSEP files remain preserved but must not be selected for this native WebGPU bundle.

See runtime/README.md, runtime/vendor/vendor-manifest.json and the pinned package-manifest.json checksums. This is a dependency closure correction, not a new export, precision change or new quality result. It does not itself establish packed-extension inference success or real-site generalization. The existing synthetic evaluation, license restrictions and unverified real-site scope remain.