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README (1).md

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+---
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+language:
+- en
+license: apache-2.0
+metrics:
+- accuracy
+library_name: transformers
+pipeline_tag: image-text-to-text
+---
+
+<p align="center">
+  <a href="https://huggingface.co/Carol0110/UniRM/blob/main/README.md"><b>English</b></a> | <a href="https://huggingface.co/Carol0110/UniRM/blob/main/README_zh.md">中文</a>
+</p>
+
+# UniRM: Multi-Head Scalar Reward Model for Multimodal Moderation
+
+**UniRM** is a **multi-head scalar reward model** that provides **interpretable, attribute-level scoring** for multimodal moderation. It was introduced in the paper [From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation](https://huggingface.co/papers/2602.02536).
+
+- **Project Page:** [trustworthylab.github.io/UniMod/](https://trustworthylab.github.io/UniMod/)
+- **Repository:** [github.com/Carol-gutianle/UniMod](https://github.com/Carol-gutianle/UniMod)
+- **Paper:** [arXiv:2602.02536](https://huggingface.co/papers/2602.02536)
+
+UniRM is designed to support policy optimization for open-ended reasoning in **UniMod**, especially for the posterior response stage where deterministic labels are absent. It decouples reward attribution into multiple dimensions so the model can distinguish **stylistic quality** from **safety boundaries** (privacy, bias, toxicity, legality), enabling transparent diagnosis and stable optimization.
+
+---
+
+## Demo Video
+
+> UniRM demo video:
+
+<video controls preload="metadata" style="width:100%; max-width:900px; border-radius:12px;">
+  <source src="https://huggingface.co/Carol0110/UniRM/resolve/main/unirm.mp4" type="video/mp4">
+</video>
+
+---
+
+## Quick Start (Gradio)
+
+Below is a minimal Gradio demo that loads **UniRM** and returns **multi-head scores** for a *(prompt, response, optional image)* triple.
+
+```bash
+git clone https://github.com/TideDra/lmm-r1.git
+cd lmm-r1
+pip install -e .[vllm]
+pip install flash_attn --no-build-isolation
+python unirm.py --model_path {PATH_TO_UNIRM}
+```
+
+## Citation
+
+```bibtex
+@misc{gu2026sparsedecisionsdensereasoning,
+      title={From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation}, 
+      author={Tianle Gu and Kexin Huang and Lingyu Li and Ruilin Luo and Shiyang Huang and Zongqi Wang and Yujiu Yang and Yan Teng and Yingchun Wang},
+      year={2026},
+      eprint={2602.02536},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.02536}, 
+}
+```

README_zh.md

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+# UniRM：用于多模态内容审核的多头标量奖励模型
+
+**UniRM** 是一个用于多模态内容审核的**多头标量奖励模型**，能够提供**可解释的、属性级别的评分信号**。  
+该模型被设计用于支持 **UniMod** 中的开放式推理策略优化，尤其适用于**缺乏确定性标签的响应生成阶段**。
+
+UniRM 将奖励信号解耦为多个维度，使模型能够区分**表达质量**与**安全边界**（隐私、偏见、有害性、合法性），从而实现更透明的诊断与更稳定的训练。
+
+---
+
+## 演示视频
+
+> UniRM 演示视频：
+
+<video controls preload="metadata" style="width:100%; max-width:900px; border-radius:12px;">
+  <source src="static/videos/unirm.mp4" type="video/mp4">
+</video>
+
+---
+
+## 快速开始（Gradio）
+
+下面给出一个最小化的 Gradio 示例，用于加载 **UniRM**，并对 *(输入指令、模型回复、可选图像)* 进行**多头奖励评分**。
+
+```bash
+git clone https://github.com/TideDra/lmm-r1.git
+cd lmm-r1
+pip install -e .[vllm]
+pip install flash_attn --no-build-isolation
+python unirm.py --model_path {PATH_TO_UNIRM}