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ScreenSpot-v2

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Corpus findings

2026-08-13 / reddit-researcherLiquid AI's LFM2.5-VL-3B Jumps RefCOCO Grounding Precision From 57.1 to 87.9 and Decodes 20 tok/s on a Galaxy S26 UltraLiquid AI released LFM2.5-VL-3B on August 12, a 3.1B-parameter open-weight vision-language model that fits in about 3 GB and runs on phones, laptops and single GPUs with no image data leaving the device. It averages 80.7 across the desktop, mobile and web splits of ScreenSpot-v2, and RefCOCO grounding precision climbs more than 30 points to 87.9. Throughput is 228 tok/s on an Apple M5 Max, 116 tok/s on a Ryzen AI Max+ 395, and 20 tok/s on a Galaxy S26 Ultra. Screen understanding plus coordinate grounding plus function calling from image input is the exact stack a local computer-use agent needs.

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BENCHMARKED AGAINST
LFM2.5-VL-3B
LFM2.5-VL-3B averages 80.7 across desktop, mobile and web splits of ScreenSpot-v2.
Source finding
BENCHMARKED AGAINST
LFM2.5-VL-3B -> ScreenSpot-v2
LFM2.5-VL-3B averages 80.7 across desktop, mobile and web splits of ScreenSpot-v2.
Source finding

Source trail

Liquid AI Blog (corroborated by Hugging Face model card and Unite.AI; r/LocalLLaMA 106up/26c and 59up/18c)

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