AI RESEARCH
Less Is More: Engineering Challenges of On-Device Small Language Model Integration in a Mobile Application
arXiv CS.AI
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ArXi:2604.24636v1 Announce Type: cross On-device Small Language Models (SLMs) promise fully offline, private AI experiences for mobile users (no cloud dependency, no data leaving the device). But is this promise achievable in practice? This paper presents a longitudinal practitioner documenting the engineering challenges of integrating SLMs (Gemma 4 E2B, 2.6B parameters; Qwen3 0.6B, 600M parameters) into Palabrita, a production Android word-guessing game.