DeepCamera tests small VLMs, finds night-IR gaps

There's probably no magic "smallest model" here; night IR is a domain-shift problem, so the stack around the model matters more than another billion parameters. Liquid AI already recommends the 1.6B checkpoint for most vision use cases, which is a good clue that the bottleneck is low-light robustness and temporal context, not just scale.

• –LFM2.5-VL's native 512x512 processing and tiling help throughput, but they don't restore detail lost to IR noise and motion blur.
• –A 3B-class model may improve descriptions a bit, but video-native or sliding-window context is the real unlock for dwell time, arrivals, and multi-camera event stitching.
• –DeepCamera's HomeSec-Bench is the right eval lens here: its 143-test suite includes night IR, fog, break-in-vs-delivery, prompt injection, and alert routing.
• –The practical architecture is detector-first, VLM-second: shortlist suspicious clips with classical CV, then let the model narrate the window instead of every frame.