# Modus：纯解码器架构实现任意模态间任意映射

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-07-28 08:00
- AIHOT 分数：53
- AIHOT 链接：https://aihot.virxact.com/items/cms5uj9640uqnrobk6s8thfjd
- 原文链接：https://arxiv.org/abs/2607.25948

## AI 摘要

研究者提出 Modus，一种纯解码器架构的任意模态映射模型，无需特定模态的头部、损失函数或任务管线即可对称处理所有模态的输入与输出。Modus 支持通过中间模态进行链式生成，以及利用另一生成模态对自身输出进行交叉模态自验证。该模型在多项基准测试中与专用模型和多任务基线表现相当，所有材料已开源。

## 正文

Any-to-any models predict any modality from any combination of others within a single network, a formulation used in multimodal vision and vision-language models, and increasingly in scientific domains such as ecology and astronomy. Existing any-to-any models are typically trained from scratch using encoder-decoder or diffusion architectures, impacting their performance and preventing them from using strong pre-trained decoder-only models as a prior. In this work, we investigate decoder-only any-to-any multimodal modeling, which treats all modalities symmetrically and supports arbitrary modalities as inputs and outputs without modality-specific heads, losses, or task pipelines. Because every modality is both an input and an output of the same model, the resulting model, named Modus, can support a range of applications, such as chained generation through intermediate modalities or cross-modal self-verification by scoring the model's own outputs with another generated modality. Modus demonstrates strong out-of-the-box performance and is competitive with specialist and multitask baselines using a single model across various benchmarks. All materials are open-sourced at https://modus-multimodal.epfl.ch/.