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JanusCoder model generates code from text and visuals

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced JanusCoder, a new model designed to bridge the gap between textual instructions and visual outputs in code generation. This model is trained on JanusCode-800K, the largest multimodal code corpus to date, which was created using a novel synthesis toolkit. JanusCoder and its variant JanusCoderV can generate code from text, visual inputs, or a combination of both, outperforming existing specialized models and rivaling commercial offerings. AI

IMPACT This multimodal approach to code generation could enable more intuitive and powerful tools for software development and content creation.

RANK_REASON The cluster contains a research paper detailing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

JanusCoder model generates code from text and visuals

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The cluster contains a research paper detailing a new model and dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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115 days old
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qiushi Sun, Jingyang Gong, Yang Liu, Qiaosheng Chen, Lei Li, Kai Chen, Qipeng Guo, Ben Kao, Fei Yuan ·

    JanusCoder: Towards a Foundational Visual-Programmatic Interface for Code Intelligence

    arXiv:2510.23538v3 Announce Type: replace Abstract: The scope of neural code intelligence is rapidly expanding beyond text-based source code to encompass the rich visual outputs that programs generate. This visual dimension is critical for advanced applications like flexible cont…

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