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- Edit this `README.md` markdown file to author your organization card.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <div align="center">
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+ <img src='https://cdn-uploads.huggingface.co/production/uploads/647773a1168cb428e00e9a8f/N8lP93rB6lL3iqzML4SKZ.png' width=200px>
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+ <h1 align="center"><b>On Path to Multimodal Generalist: Levels and Benchmarks</b></h1>
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+ <p align="center">
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+ <a href="https://generalist.top/">[πŸ“– Project]</a>
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+ <a href="https://level.generalist.top">[πŸ† Leaderboard]</a>
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+ <a href="https://arxiv.org/abs/2510.10101">[πŸ“„ Paper]</a>
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+ <a href="https://huggingface.co/General-Level">[πŸ€— Dataset-HF]</a>
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+ <a href="https://github.com/path2generalist/GeneralBench">[πŸ“ Dataset-Github]</a>
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+ </p>
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+ [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/license/mit)
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+ ---
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+ </div>
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+ <h1 align="center" style="color:#F27E7E"><em>
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+ Does higher performance across tasks indicate a stronger capability of MLLM, and closer to AGI?
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+ <br>
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+ NO! <b style="color:red">Synergy</b> does.
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+ </em></h1>
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+ This project introduces:
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+ 1. **General-Level**, a 5-scale level evaluation system with a new norm for assessing the multimodal generalists (multimodal LLMs/agents). The core is the use of Synergy as the evaluative criterion, categorizing capabilities based on whether MLLMs preserve synergy across comprehension and generation, as well as across multimodal interactions.
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+ 2. **General-Bench**, a companion massive multimodal benchmark dataset, encompasses a broader spectrum of skills, modalities, formats, and capabilities, including over 700 tasks and 325K instances.
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