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arXiv · 2604.19642

Micro Language Models Enable Instant Responses

Wen Cheng·Tuochao Chen·Karim Helwani·Sriram Srinivasan·Luke Zettlemoyer·Shyamnath Gollakota
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Abstract

Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute constraints, yet cloud inference introduces multi-second latencies that break the illusion of a responsive assistant. We introduce micro language models ($μ$LMs): ultra-compact models (8M-30M parameters) that instantly generate the first 4-8 words of a contextually grounded response on-device, while a cloud model completes it; thus, masking the cloud latency. We show that useful language generation survives at this extreme scale with our models matching several 70M-256M-class existing models. We design a collaborative generation framework that reframes the cloud model as a continuator rather than a respondent, achieving seamless mid-sentence handoffs and structured graceful recovery via three error correction methods when the local opener goes wrong. Empirical results show that $μ$LMs can initiate responses that larger models complete seamlessly, demonstrating that orders-of-magnitude asymmetric collaboration is achievable and unlocking responsive AI for extremely resource-constrained devices. The model checkpoint and demo are available at https://github.com/Sensente/micro_language_model_swen_project.

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Wen Cheng, Tuochao Chen, Karim Helwani, Sriram Srinivasan, Luke Zettlemoyer, Shyamnath Gollakota. 2026-04-21. Micro Language Models Enable Instant Responses. https://arxiv.org/abs/2604.19642

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