Fovea-4B is a 4-billion-parameter vision-language model designed to make structured decisions from images and accompanying JSON data in a single forward pass. It handles yes/no, multiple-choice, and ordinal-score questions about photos, documents, receipts, charts, and screenshots, returning a probability for every requested answer. An internal “unknown” option can capture uncertainty when evidence is insufficient; answers cover exactly the supplied labels and sum to one, while unscorable questions receive a uniform distribution. The model uses a Qwen3.5-4B architecture with a vision encoder, and applies separate calibration temperatures to different question types. It is released in bfloat16 under the Apache-2.0 licence and can be served locally through a provided API setup.
The model is presented as outperforming ImaJev-4B, and its evaluation compares it with Imajev-4B, H2O-Lightning-4B image, and the larger Jev-Vision. All four models answer the same 2,360 held-out multiple-choice questions from ImageJevBench v0.3, with results organized by overall performance and categories including documents, charts, spatial reasoning, diagrams, multi-image tasks, and counting. Scores use an “Intelligence” measure that adjusts accuracy for chance performance. The supplied details describe the evaluation design but do not provide the score table, so they establish the comparison’s scope rather than the size of Fovea-4B’s reported lead.
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