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On October 30, 2026, Ant Group's inclusionAI open-sourced the ZwZ model family and the ZoomBench fine-grained perception benchmark as companion results to an ICML 2026 paper. According to the introduction of inclusionAI's new GitHub repository, the ZwZ model family includes three sizes—4B, 7B, and 8B—and achieves SOTA on multimodal perception benchmarks among open-source models, requiring only a single forward inference pass and no tool calls at inference time. Its method is Region-to-Image Distillation (R2I), which distills high-quality VQA supervision generated from enlarged local regions back into the full image, with 37K training samples. The simultaneously released ZoomBench contains 845 VQA items covering 6 fine-grained perception dimensions, and Qwen2.5-VL-7B achieves only 42.5% average accuracy. There are currently no further developments.
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inclusionAI open-sources the ZwZ model family and the ZoomBench benchmark
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Oct 30
Ant inclusionAI: New GitHub repositoryFirst-party
Ant inclusionAI Open-Sources the ZwZ Model Family and the ZoomBench Fine-Grained Perception Benchmark (ICML 2026)
Ant inclusionAI has released the ZwZ model family (4/7/8B) accompanying its ICML 2026 paper, achieving SOTA on multimodal perception benchmarks among open-source models, while requiring only a single forward inference pass and no tool calls at inference time. The method is Region-to-Image Distillation (R2I), which distills high-quality VQA supervision generated from enlarged local regions back into the full image, with 37K training samples. It also introduces ZoomBench, containing 845 VQA items covering 6 fine-grained perception dimensions, on which Qwen2.5-VL-7B achieves only 42.5% average accuracy.