README.md

---
license: other
language:
  - en
pretty_name: BioManufacturingBench v1.0.0
size_categories:
  - 1K<n<10K
task_categories:
  - question-answering
  - visual-question-answering
  - text-generation
tags:
  - biomanufacturing
  - biotechnology
  - multimodal
  - benchmark
  - objective-scoring
  - temporal-holdout
configs:
  - config_name: default
    data_files:
      - split: test
        path: data.jsonl
---

<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/68ed488e386d9944bc2ea3a5/z6lWF8anMTDnO-E3zrFTU.png" alt="Capicú Technologies" />
</div>

# BioManufacturingBench v1.0.0

BioManufacturingBench v1.0.0 is a 2,000-item benchmark for evidence-grounded
biomanufacturing reasoning. It covers evidence extraction, mass-balance calculation,
process diagnosis, microscopy count-range estimation, strict output formatting, and
abstention. Every primary score is computed by a deterministic rule; no score uses an
LLM judge. Public records are deliberately answer-free so the benchmark remains useful
for future evaluation.

## Usage

```python
from datasets import load_dataset

bench = load_dataset("capicu-ai/BioManufacturingBench", split="test")
```

Records with `input_type="image_text"` are the 400 microscopy questions. Text-only
systems should be evaluated on the remaining 1,600 records and reported separately.
Assigned-task scores are not directly comparable when one model receives images and
another does not.

## Composition

| Task family | Items | Evidence source | Primary scorer |
|---|---:|---|---|
| Evidence extraction | 400 | Held-out 2026 articles | Semantic JSON, candidate selection, classification accuracy |
| Table and unit reasoning | 350 | Deterministic mass balances | Numerical tolerance |
| Process trajectory reasoning | 500 | Deterministic simulation | Closed-label exact match and semantic JSON |
| Microscopy reasoning | 400 | Reserved microscopy groups | Exact count-range label |
| Instruction following | 250 | Reserved source metadata | Exact JSON structure |
| Abstention | 100 | Balanced answerable and incomplete records | Exact match |

500 fresh held-out open-access articles from 2026 supply the text evidence. 3,927 reserved
microscopy images are indexed for the image items. Source groups used here are excluded
from the BioManufacturingCorpus and BioManufacturingInstruct training releases, so a model
trained on those releases has not seen this evidence through that path.

## Construction

Each family is built from a frozen deterministic recipe rather than free-form question
writing. Answers derive from exact evidence, closed-form calculations, deterministic
trajectory diagnostics, image-mask count ranges, or strict JSON rules. Process questions
use explicit answer vocabularies, microscopy uses broad count bins rather than unreasonable
exact high-count matching, and abstention records are balanced between answerable and
incomplete cases. Public prompts and private answers remain in separate governed locations.

## Evaluation protocol

Use deterministic decoding where the provider permits it. Record the model revision,
provider, prompt version, generation settings, failure count, and date. Treat failed
requests as missing observations and report them separately rather than counting
infrastructure failures as model errors.

Report at minimum: overall score, text-only score, per-task-family scores, multimodal
coverage and microscopy score, request completion rate, and uncertainty intervals for any
full study.

## Preliminary baseline

A stratified 100-item sample was run against 16 open-weight checkpoints. It contains
80 text items and 20 microscopy items. Text-only models received 80 requests and
multimodal models received 100, for 1,440 total requests. All 1,440 returned scored
answers.

The pilot is evidence of benchmark behavior, not a claim that 100 sampled items establish
a definitive model ranking. Scores below are deterministic and use each model's assigned
tasks. Microscopy is shown separately because text-only models did not receive those items.

| Model | Assigned-task score | Evidence | Table/unit | Process | Microscopy | Instruction | Abstention |
|---|---:|---:|---:|---:|---:|---:|---:|
| Kimi K3 | 96.0% | 95.0% | 100.0% | 100.0% | 85.0% | 100.0% | 100.0% |
| GPT-OSS 120B | 87.5% | 95.0% | 72.2% | 86.4% | — | 90.0% | 100.0% |
| Inkling | 82.0% | 90.0% | 55.6% | 90.9% | 75.0% | 90.0% | 100.0% |
| MiniMax M3 | 78.9% | 94.7% | 27.8% | 86.4% | 85.0% | 90.0% | 100.0% |
| DeepSeek V4 Pro | 59.8% | 79.4% | 5.6% | 77.3% | — | 70.0% | 70.0% |
| Qwen 3.5 397B | 59.8% | 78.8% | 0.0% | 86.4% | 40.0% | 70.0% | 100.0% |
| GPT-OSS 20B | 59.2% | 71.7% | 16.7% | 63.6% | — | 70.0% | 90.0% |
| Qwen 3.6 27B | 58.9% | 74.7% | 0.0% | 68.2% | 60.0% | 70.0% | 100.0% |
| DeepSeek V4 Flash | 57.3% | 79.1% | 5.6% | 63.6% | — | 80.0% | 70.0% |
| Qwen 3.6 35B-A3B | 56.6% | 77.8% | 0.0% | 72.7% | 55.0% | 70.0% | 70.0% |
| Qwen3 235B-A22B | 55.2% | 70.9% | 5.6% | 54.5% | — | 70.0% | 100.0% |
| GLM-5.2 | 55.0% | 40.0% | 5.6% | 81.8% | — | 70.0% | 100.0% |
| Llama 3.3 70B | 54.3% | 82.2% | 0.0% | 50.0% | — | 70.0% | 90.0% |
| Laguna-S-2.1 | 50.5% | 62.2% | 5.6% | 50.0% | — | 70.0% | 90.0% |
| Qwen 3.5 9B | 47.8% | 73.8% | 0.0% | 68.2% | 25.0% | 50.0% | 80.0% |
| Gemma 4 31B | 43.0% | 5.0% | 0.0% | 63.6% | 45.0% | 90.0% | 100.0% |

### Task-family separation

Performance differed sharply by capability. Kimi K3 led the assigned-task pilot at 96.0%
and was the only model to score 100% on evidence, table/unit, process, instruction, and
abstention simultaneously. Table/unit reasoning remained highly selective: six models
scored 0%, while Kimi K3 scored 100%, GPT-OSS 120B scored 72.2%, and Inkling scored 55.6%.
Process reasoning ranged from 50.0% to 100.0%. Microscopy count-range accuracy ranged from
25.0% to 85.0% across the eight multimodal models.

Instruction-following and abstention are reported as competency checks. They are useful for
detecting failures but are not intended to dominate model ranking.

## Fields

| Field | Description |
|---|---|
| `id` | Stable public question identifier |
| `task` | One of the six task families |
| `prompt` | Text shown to the model |
| `input_type` | `text` or `image_text` |
| `image` | Governed image URI for microscopy items; empty for text items |
| `scorer` | Frozen objective scoring rule |
| `source_id` | Source ancestry identifier |
| `source_url` | Public source location where applicable |
| `license` | Record-level source license where applicable |
| `evaluation_role` | `discrimination` or `competency` |
| `release_version` | Always `v1.0.0` |

The public file is `data.jsonl`. It contains no reference answers, internal build
identifiers, or nullable `scenario_id` field.

## Governance and license

This is a mixed-source benchmark, so the repository is released under `license: other`
rather than asserting one blanket license across every source. Source-derived records retain
their identifiers and URLs, and admission follows a fail-closed license policy. Users are
responsible for the terms of the referenced source datasets.

Benchmark prompts, metadata, and project-authored deterministic scenarios may be used for
research subject to the repository terms. The private answer key must not be redistributed
or used for training.

<!-- ## Reproducibility (internal build record)

| Property | Value |
|---|---|
| Public release | `v1.0.0` |
| Prompt JSONL SHA-256 | `1d825d44bd297151a2713b50401e2d0a4fd7dbda93a3f13e196eb8bd63590181` |
| Prompt Parquet SHA-256 | `e742f299711e4dc8dbfe673036a877ee4f0574adf7e17cd4bc9dfd737b2c7127` |
| Objective scoring coverage | 100% |
| LLM judge in primary scores | No | -->

## Citation

Cite the dataset repository with the exact benchmark version and access date. A methods
manuscript citation will replace this when released.

```bibtex
@misc{biomanufacturingbench2026,
  title  = {BioManufacturingBench v1.0.0},
  author = {S.A. Cruz Romero},
  year   = {2026},
  url    = {https://huggingface.co/datasets/capicu-ai/BioManufacturingBench},
  note   = {Version v1.0.0}
}
```