Ai2: Molmo 2's open release enabled an MIT researcher's salmon-tracking model
Ai2 published an account of how Kai Van Brunt, an MIT '25 graduate working in MIT's BeeryLab, built Molmo2Fish on the open vision-language model Molmo 2 to track salmon in underwater sonar video and revise its tracking predictions from everyday language instructions. According to the post, Molmo 2 can process a whole clip and output tracks directly; after fine-tuning on salmon videos, Molmo2Fish performed roughly as well as a specialized fish-tracking system before receiving any corrections, with results varying across rivers. The experiments showed language-guided correction helped with clear instructions but struggled to improve the model's own strongest predictions, and Molmo2Fish remains an undeployed research project.
The case illustrates how an open release of model, code and training data lets researchers adapt general video-language capabilities to specialized fields such as ecological monitoring.
How Molmo 2’s open release helped an MIT researcher rethink fish tracking
October 13, 2026
Ai2
When we released Molmo 2, our open vision-language model for images and video, we made the model, code, and training data openly available so researchers could inspect how it was built and customize it for their own work. Molmo 2 can follow a particular figure or object as it moves through a video, identify something described in language – such as “the second car to cross the finish line” – and reason about events over time. Making the model and the resources behind it open gives researchers a starting point for applying those capabilities to new problems.
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