---
license: apache-2.0
base_model: Qwen/Qwen3-VL-8B-Instruct
pipeline_tag: video-text-to-text
library_name: transformers
tags:
  - video-understanding
  - reinforcement-learning
  - adaptive-reasoning
  - qwen3-vl
---

# EVAThink-Qwen3

<!-- TODO(author): one-line expansion of the name, paper title / arXiv link, code repo link -->

**EVAThink-Qwen3** is a vision-language model fine-tuned from [Qwen3-VL-8B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) with reinforcement learning to **decide per question whether to think**. When the visual evidence plainly supports an answer, it replies directly. When the evidence is sparse, discontinuous, or scattered across frames, it writes a short, evidence-grounded reasoning trace first.

You don't need to tell it which mode to use. Give it the plain question and options, and it picks the mode itself.

## Highlights

- **Adaptive, not always-on, reasoning.** How often it thinks follows how hard the task is to reason about. On purely perceptual spatial questions (VSI-Bench) it thinks on 0% of questions. On knowledge-heavy lecture videos (Video-MMMU) it thinks on 86%. Within a benchmark, the rate rises with video length: on Video-MME it goes 14% → 41% → 48% from short to medium to long videos.
- **Short outputs.** Mean response length is 4 tokens on VSI-Bench, 14 on MVBench, 95 on LongVideoBench, 117 on Video-MME, 257 on MMVU, and 794 on Video-MMMU, where it thinks on 86% of questions.
- **Better than the base model or on par on all six benchmarks**, and ahead of VideoAuto-R1 (Qwen3-VL-8B) on five of six, all under the same frame budget.

## Results

All models were evaluated by us under **one shared protocol**:
- 64 frames sampled uniformly (first and last frame included)
- each frame resized to a fixed 200,704 pixels (factor 28)
- temperature=0.0, greedy decoding

Each model uses its intended prompt:
- **EVAThink**: the bare question and options, with no instruction.
- **Qwen3-VL-8B-Instruct**: each benchmark's official prompt.
- **VideoAuto-R1**: its own answer-twice prompt, with confidence-based early exit.

| Benchmark (split, #Q) | Qwen3-VL-8B-Instruct | VideoAuto-R1-Qwen3-VL-8B | **EVAThink-Qwen3** | EVAThink think-rate |
|---|---|---|---|---|
| Video-MME w/o subs (2,700) | 64.9 | 66.0 | **66.3** | 34.4% |
| MVBench (4,000) | 65.8 | **69.0** | 66.5 | 5.1% |
| MMVU, multiple-choice (625) | 70.4 | 69.6 | **72.8** | 59.4% |
| Video-MMMU (900) | 65.6 | 64.3 | **67.4** | 85.7% |
| LongVideoBench val (1,337) | 60.4 | 54.6 | **60.7** | 46.1% |
| VSI-Bench (5,130) | 58.3 | 54.6 | **59.9** | 0.0% |

Notes:
- These numbers come from our own fixed-budget protocol and are not directly comparable with numbers in other papers, which use different frame counts, resolutions, and prompts.
- MVBench uses the original segment boundaries for the tasks that define them.
- VSI-Bench scores numerical questions with mean relative accuracy (MRA) and multiple-choice questions with accuracy; the table reports the average over all questions.
- Video-MMMU includes its 21 open-ended questions, scored with the official MMMU rules.
- A response counts as "thinking" (think-rate) when it doesn't start directly with the answer.

<!-- TODO(author): add significance / paired-test notes if desired -->

## Citation

```bibtex
@misc{evathink2026,
  title  = {TODO},
  author = {TODO},
  year   = {2026},
  note   = {TODO: arXiv link}
}
```