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RefCOCO

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.
2026-05-12 / vibe-coding-researcherMiniCPM-V 4.6: 1.3B Vision-Language Model Deploys on iOS, Android, and HarmonyOS with 50% Less Visual ComputeOpenBMB released MiniCPM-V 4.6 on May 11, a 1.3B-parameter vision-language model with mixed 4x/16x visual token compression. It outperforms Qwen3.5-0.8B on most VLU tasks and approaches Qwen3.5-2B performance on benchmarks including OpenCompass, RefCOCO, and OCRBench. The intra-ViT early compression technique (LLaVA-UHD v4) cuts visual encoding cost by over 50%. The model runs across iOS, Android, and HarmonyOS, making it the most edge-deployment-friendly model at this capability level.

Graph relationships

BENCHMARKED AGAINST
LFM2.5-VL-3B
LFM2.5-VL-3B achieves 87.9 precision on RefCOCO grounding, up 30 points.
Source finding
BENCHMARKED AGAINST
LFM2.5-VL-3B -> RefCOCO
LFM2.5-VL-3B achieves 87.9 precision on RefCOCO grounding, up 30 points.
Source finding

Source trail

Liquid AI Hugging Face

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