---
license: other
library_name: transformers
tags:
- multimodal
- vision-language
- reasoning
- science
- rl
- qwen3.5
---
# InnovatorVL-v2
**InnovatorVL-v2 is a dense 9B-parameter multimodal model that delivers
state-of-the-art scientific visual understanding at its scale, without giving up
general multimodal ability.** Built on Qwen3.5-9B-Base, it is trained through a
pipeline centred on systematic data governance — recursive self-improvement for
data scoring and filtering, rank-based mixture optimisation, failure-guided data
synthesis, and Visual Evidence-Calibrated Reinforcement Learning (VEC-RL).
Evaluated on 22 benchmarks with thinking disabled for every compared model,
InnovatorVL-v2 attains **the highest average in all three categories**:
| Category | **InnovatorVL-v2 (Ours)** | Margin over strongest baseline |
| --- | :---: | :---: |
| General (12 benchmarks) | **80.18** | +3.32 |
| Science (8 benchmarks) | **59.71** | **+12.46** |
| Spatial (2 benchmarks) | **61.56** | +1.17 |
Headline results:
- **First on 18 of the 22 benchmarks** among the models displayed, and
**beats Qwen3.5-9B on 21 of the 22** — the single exception is SimpleVQA.
- **Scientific understanding is the standout gain**: +17.70 points over
Qwen3.5-9B on the Science average.
- **MolParse 75.40** and **OpenRxn 56.50** — ahead of the best compared baseline
by **+56.35** and **+40.85** points respectively. Molecular structure parsing
and reaction understanding are near-solved relative to the field.
- **Best general score on 11 of the 12 general benchmarks**, including
DocVQA 96.76, V\*Bench 90.58, ChartQA 89.24, AI2D 88.34,
OCRBench 88.20 and InfographicVQA 87.69.
- **Highest Spatial average**, with EmbSpatial-Bench 77.36 and ERQA 45.75.
## Performance
**Performance overview across 22 benchmarks.** Blue bars indicate our model; the
remaining bars show the compared models available in each panel. Our model ranks
first on 18 of the 22 benchmarks among the models displayed and outperforms
Qwen3.5-9B on 21 of the 22. The signed value in the upper-right corner of each
panel is the score-point difference against Qwen3.5-9B. All results use our
local evaluation setting with thinking disabled. Higher scores are better;
vertical scales vary across panels.
**Category-level performance with thinking disabled.** Panels (a), (b) and (c)
report unweighted averages over 12 general, eight scientific and two spatial
benchmarks respectively. All seven models have complete coverage. Blue bars
denote InnovatorVL-v2 (Ours); the remaining bars are the six baselines, including
Qwen3.5-9B-Base. Models appear in the same order across panels, sorted by their
General scores. All panels use the same 0–100 scale.
### Full results
Local non-thinking evaluation across all 22 benchmarks. The
**InnovatorVL-v2 (Ours)** column is placed first and emphasised throughout;
bold marks the best score in each row, including ties.
| Benchmark |
InnovatorVL-v2 (Ours) |
Qwen3-VL-8B |
Gemma4-12B-IT |
InternVL3.5-8B |
Step3-VL-10B |
Qwen3.5-9B |
Qwen3.5-9B-Base |
| General |
| AI2D |
88.34 |
83.74 |
77.49 |
82.22 |
83.55 |
85.46 |
63.28 |
| ChartQA |
89.24 |
85.24 |
52.12 |
87.20 |
59.68 |
85.28 |
61.04 |
| InfographicVQA (val) |
87.69 |
83.72 |
53.86 |
74.23 |
71.27 |
83.34 |
76.70 |
| DocVQA (val) |
96.76 |
93.66 |
83.10 |
92.37 |
92.33 |
94.98 |
92.60 |
| MMBench (EN dev) |
87.97 |
83.67 |
81.62 |
83.51 |
85.47 |
85.48 |
86.34 |
| MME-RealWorld |
71.33 |
63.29 |
35.02 |
62.66 |
30.32 |
60.08 |
46.79 |
| MMStar |
73.15 |
59.94 |
67.11 |
66.38 |
63.24 |
70.52 |
71.92 |
| OCRBench |
88.20 |
87.00 |
77.80 |
83.20 |
85.30 |
83.10 |
86.30 |
| RealWorldQA |
76.73 |
68.76 |
65.23 |
63.53 |
63.66 |
74.51 |
72.81 |
| SEED-Bench-2-Plus |
72.38 |
71.28 |
68.25 |
70.58 |
69.35 |
70.84 |
64.08 |
| V*Bench |
90.58 |
84.82 |
52.36 |
70.68 |
67.02 |
84.29 |
82.20 |
| SimpleVQA |
39.80 |
39.31 |
40.27 |
35.47 |
46.24 |
44.40 |
45.73 |
| Science |
| EMVista |
49.62 |
44.27 |
39.30 |
46.81 |
43.03 |
43.35 |
37.84 |
| ScienceQA |
93.89 |
91.91 |
89.39 |
95.94 |
85.76 |
93.35 |
89.53 |
| MolParse |
75.40 |
4.35 |
4.95 |
19.05 |
4.25 |
7.00 |
5.20 |
| OpenRxn |
56.50 |
9.25 |
12.60 |
14.05 |
12.70 |
14.40 |
15.65 |
| RxnBench (EN) |
89.05 |
82.75 |
85.31 |
80.46 |
73.70 |
88.52 |
87.67 |
| SmolInstruct_v2 |
27.08 |
20.15 |
24.18 |
40.27 |
6.87 |
14.38 |
27.44 |
| ProteinLMBench |
61.97 |
59.32 |
56.67 |
61.76 |
56.25 |
54.77 |
65.89 |
| SFE (EN) |
24.13 |
28.98 |
22.41 |
19.63 |
14.44 |
20.24 |
20.12 |
| Spatial |
| EmbSpatial-Bench |
77.36 |
74.90 |
73.54 |
73.24 |
68.10 |
75.77 |
75.00 |
| ERQA |
45.75 |
38.80 |
42.25 |
41.25 |
24.00 |
45.00 |
45.75 |
## Usage
Verified with `transformers` 5.3.0. All 760 tensors in `model.safetensors` map
cleanly onto the model (no missing or unexpected keys).
```python
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
path = "InnovatorLab/InnovatorVL-v2"
processor = AutoProcessor.from_pretrained(path) # Qwen3VLProcessor
model = AutoModelForImageTextToText.from_pretrained( # Qwen3_5ForConditionalGeneration
path, dtype=torch.bfloat16, device_map="auto"
)
```
### Inference dependencies
The text stack uses hybrid attention with `linear_attention` layers. Without
[`flash-linear-attention`](https://github.com/fla-org/flash-linear-attention)
and [`causal-conv1d`](https://github.com/Dao-AILab/causal-conv1d) installed,
`transformers` logs *"The fast path is not available"* and falls back to a
slower pure-PyTorch implementation. Results are unaffected, but throughput is
lower — install both for production inference.
## License
The license field above is a placeholder. It must be set by the model owner
before relying on this model commercially.