d3-mini-multi

d3-mini with a fourth question type. multi answers "which of these options apply?" with every option that applies, from zero to many, in one forward pass.

Type	Answers	Returns
`choice`	Which single option fits best?	One option
`score`	Where does this sit on an ordered scale?	One level
`noul`	Is this statement true?	One probability
`multi`	Which options apply?	Every option that applies, a calibrated score for each, and the cutoff

multi adds one option to the question, the cutoff. The model scores the cutoff in the same pass and the same softmax as the labels, and keeps every label that outscores it. Because the cutoff is scored from the input, the model sets a new threshold for every input. The default cutoff works without tuning. An offset is there when a task needs it.

Code and proxy: github.com/akta-pro/d3-multi
Paper: The Cutoff Is an Option

Results

Micro-F1 on held-out test data, with no threshold tuning on either side. "1 pass" means one forward pass per input.

Test set	Labels	Base: one `noul` per label (p > 0.5)	Base: `choice` top-1	d3-mini-multi (1 pass)
arXiv categories	155	0.29	0.65	0.67
Civil Comments (toxicity attributes)	7	0.59	0.41	0.78
DBpedia-14	14	0.90	0.97	0.98
ECtHR-A (articles violated)	10	0.66	0.69	0.75
ECtHR-B (articles alleged)	10	0.69	0.72	0.80
GoEmotions	28	0.40	0.56	0.56
UNFAIR-ToS (never seen in training)	8	0.60	0.19	0.66
TweetTopic (never seen in training)	19	0.63	0.68	0.68
Hate-speech targets (never seen in training)	8	0.69	0.76	0.72

multi beats per-label noul on all nine test sets, including the three domains that are not in the training data. Against choice top-1, it wins on six, ties on two, and trails on hate-speech targets, where most comments target a single group.

choice, score and noul keep their accuracy. We ran the base and fine-tuned models on the same 10,763 requests (29,053 questions) from 42 public benchmarks of the Decision Index. The public index is 55.10 for d3-mini-multi and 55.05 for the base model: a change of +0.01 points, with a standard error of 0.22.

The tuned-baseline comparison, cost and calibration are in the GitHub README.

Quickstart

Serve the model with vLLM, then put the multi proxy from the GitHub repository in front of it:

git clone https://github.com/akta-pro/d3-multi && cd d3-multi
pip install -r requirements.txt
vllm serve akta-pro/d3-mini-multi --served-model-name d3-mini-multi --port 8100 --no-enable-prefix-caching
python -m d3multi.serve --upstream http://localhost:8100 --port 8200

Ask a multi question:

curl -s localhost:8200/v1/systemone -H 'content-type: application/json' -d '{
  "model": "d3-mini-multi",
  "state": "The order arrived damaged yesterday. The customer was charged twice and wants a replacement today.",
  "questions": {
    "teams": {
      "type": "multi",
      "instructions": "Which teams need to act on this request?",
      "criteria": {
        "returns": "Refunds, replacements and damaged deliveries",
        "billing": "Payments, invoices and charges",
        "technical": "Product setup and faults",
        "shipping": "Delivery tracking and couriers"
      }
    }
  }
}'

The answer (scores rounded to four decimals):

{
  "teams": {
    "type": "multi",
    "selected": ["returns", "billing"],
    "scores": {
      "returns": 0.9867,
      "billing": 0.9108,
      "shipping": 0.2438,
      "technical": 0.0494
    },
    "cutoff": 0.0116
  }
}

choice, score and noul questions pass through the proxy unchanged, and they can share a request with multi questions. The request format, the offset setting and the client-side decoder are in the GitHub repository.

Limits

Unseen domains. On UNFAIR-ToS, untuned multi reaches recall 0.94 but precision 0.51: it tags too many sentences on a set where 89% of inputs have no label. On hate-speech targets, where most comments target one group, choice top-1 is ahead (0.76 against 0.72).
254 options per question. The readout scores 255 codes, and the cutoff takes one.
Near-zero margins. Options within a hair of the cutoff can flip with batch shape or numeric precision.

_{All test sets are English text. We did not test other languages, or multi questions about images or video.}

Files

The weights are BF16. The readout, tokenizer, configuration and d3 runtime files are copied unchanged from vllm-sr/d3-mini at revision 97dd054. Only the language-model weights differ. SHA256SUMS lists the hash of each weight file.

License

Citation

@misc{d3_multi_2026,
  title        = {The Cutoff Is an Option: Single-Pass Multi-Label Classification
                  with Adaptive Thresholding},
  author       = {Balot, Harsh and {akta.pro}},
  year         = {2026},
  howpublished = {\url{https://akta.pro/blog/the-cutoff-is-an-option-single-pass-multi-label-classification-with-adaptive-thresholding}},
  note         = {Code: \url{https://github.com/akta-pro/d3-multi}}
}