AI

microsoft/Mage-VL-Cowork · Hugging Face

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 31/10/2026, 19:47:26 UTC.

Briefings

A 4B parameter vision-language model named Mage-VL-Cowork achieved top performance on OSWorld-Verified benchmarks while consuming 13% fewer model calls and 25.4% fewer output tokens compared to baseline methods. This efficiency was enabled by integrating upstream teacher-harness evolution, workflow-first environment synthesis, and a five-stage specialize-then-unify curriculum. The study was submitted to r/LocalLLaMA community.

Key points

• Mage-VL-Cowork is a 4B parameter vision-language model that achieved the best performance on OSWorld-Verified benchmarks.
• The model reduced model calls by 13% and output tokens by 25.4% through the integration of teacher-harness evolution and workflow-first environment synthesis.

Context

This research represents a significant advancement in on-device intelligence for autonomous desktop agents, demonstrating that smaller models can match or exceed larger counterparts when optimized with specific curriculum and synthesis techniques.

Limitations

Performance data is limited to OSWorld-Verified benchmarks; specific results for other evaluation metrics are not provided. The study relies on community submission and comments, which may lack peer-reviewed validation or comprehensive testing across diverse hardware and software environments.

Generated by Bittrees-hosted qwen35-2b sha256:aaf42c8b7c3c on 31/10/2026, 21:20:47 UTC. Based on publisher-provided feed evidence; may not cover the full original article. Check the source for details.

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