---
language:
- en
pipeline_tag: image-text-to-text
tags:
- ekt-router
- embodied-question-answering
- lora
- coming-soon
---
# EKT-Router: Expert–Evidence Alignment for Embodied Vision–Language Models

**Model release placeholder. Coming soon. No model weights or adapters are available to download.**
This repository contains a model card only. It is not a loadable Transformers model.
No release date is promised, and no third-party weights have been uploaded.

## Model description

EKT-Router jointly selects one capability expert and optional knowledge/perception
under a bounded acquisition budget. The intended portfolio uses a shared
Qwen3-VL-8B backbone and three independent LoRA adapters: General (continued
pretraining), Spatial Reasoning, and Spatiotemporal. A frozen Qwen3-VL-2B descriptor
feeds the utility router. After one acquisition round, the Packet Verifier retains
valid evidence with positive predicted gain while keeping the expert fixed. The
expert generates once, using its original input if all evidence is rejected.

Knowin describes this project as its first internal Mixture-of-LoRA (MoL) solution;
this is an internal project milestone, not an academic priority claim.

## Availability and reproducibility

The General, Spatial Reasoning and Spatiotemporal adapters, trained router, verifier,
and capability signatures are not released. The supplied project materials do not
include original predictions, complete experiment logs, immutable split/scorer
manifests, or weights sufficient to reproduce the paper measurements.

61.4 (six embodied QA benchmarks), 74.7 (ten general VLM benchmarks), and +1.75
(nine-benchmark evidence-null control) are paper-reported measurements under
separate protocols. They are not results reproduced by this public release.
CPU mock demos and unit tests are software checks, not model evaluation.

## Intended use and limitations

Research on embodied vision-language question answering and expert/evidence routing.
Packet checks and fallback do not establish factual correctness, physical safety,
or suitability for autonomous robot deployment. Domain transfer, robustness,
and calibration require independent evaluation.

## Training and licensing

See the paper and source repository for the reported methodology. Missing training
artifacts must not be inferred from this card. Weight licensing will be documented
per artifact if and when weights become available; the code's MIT license does not
license unreleased weights or third-party models.

## Project and contact

- [Project](https://knowin-29.github.io/KnowinRouter.github.io/)
- [Code](https://github.com/Knowin-29/EKT-Router)
- [Data card](https://huggingface.co/datasets/Knowin-29/EKT-Router)
- Contact: rwlinno@gmail.com

Authors: Weilin Ruan, Yingjie Xu, Yangkai Wei, Bohan Zhou, Shuaibo Li,
Harold Haodong Chen, Kaiwen Zhou, Yinchuan Li, Ying-Cong Chen, James Cheng (corresponding author).