Agents that act on what a camera sees: the spatial output is the weak link

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spatial-output vision-language-models grounding-errors agent-actions video-db open-source evaluation

Summary

A developer at VideoDB highlights the problem of precise spatial output from vision models when used by agents, sharing that small grounding errors can lead to wrong actions, and announces an open-sourced evaluation harness for checking spatial accuracy on custom footage.

I work on the video side at VideoDB, and the thing that keeps biting us is precise spatial output from vision models. If an agent has to act on exact positions, small grounding errors turn into wrong actions. The easiest way I found to see it: give a VLM a chess position and ask for the FEN. It usually recognizes the pieces, then places them on the wrong squares. Harmless in a demo, not harmless when an agent triggers on it. We pulled this into a wider VLM eval study and open sourced the harness so you can check it on your own footage or image data. For those building agents on top of video or images, how are you handling the cases where the model is confidently a little bit wrong?
Original Article

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