arXiv AI
An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices
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arXiv AI
Jun 2
Make a Video Call with LLM: A Measurement Campaign over Six Mainstream Apps
arXiv:2510. 00481v2 Announce Type: replace-cross Abstract: In 2025, Large Language Model (LLM) services have launched a new feature -- AI video chat -- allowing users to interact with AI agents via real-time video communication (RTC), just like chatting with real people.
arXiv Machine Learning
Jul 21
ChipChat: Low-Latency Cascaded Conversational Agent in MLX
arXiv:2509. 00078v2 Announce Type: replace-cross Abstract: The emergence of large language models (LLMs) has transformed spoken dialog systems, yet the optimal architecture for real-time on-device voice agents remains an open question.
arXiv AI
Jun 2
MiCU: End-to-End Smart Home Command Understanding with Large Language Model
arXiv:2606. 01099v1 Announce Type: cross Abstract: Command understanding systems in smart home ecosystems can automate device control and substantially improve user experience.
arXiv AI
Jun 2
MobiBench: Multi-Branch, Modular Benchmark for Mobile GUI Agents
arXiv:2512. 12634v4 Announce Type: replace Abstract: Mobile GUI Agents, AI agents capable of interacting with mobile applications on behalf of users, have the potential to transform human computer interaction.
arXiv Machine Learning
Sep 14
The Battery Price of edge AI: A study of the Environmental Impact of LLM Inference on Mobile Devices
The paper investigates the environmental impact of running large language models (LLMs) on mobile devices. It evaluates 18 different LLM configurations on two smartphones and a server, measuring energy per token, latency, accuracy, and battery-cycle consumption. Findings reveal that on-device inference is about three times less energy‑efficient than batched server inference, that energy consumption varies non‑monotonically with quantization bit‑width, and that most models are not on the Pareto front of accuracy and energy efficiency. The study concludes that local AI is not inherently more sustainable than cloud inference, with the majority of environmental impact stemming from device embodied carbon.
arXiv AI
Sep 4
X-Translator: A Real-Time Multilingual Speaker-Aware Speech-to-Speech Translation System
X-Translator is a low‑cost, modular real‑time speech‑to‑speech translation system that integrates streaming ASR, machine translation, and prompt‑conditioned TTS, managed by a session‑level runtime controller. It uses incremental segment commitment to stabilize ASR streams and an online speaker prompt manager to maintain speaker consistency across multi‑speaker conversations. The system is evaluated on translation quality, speech naturalness, latency, and speaker preservation using OpenSTBench, and its code and demo are publicly available on GitHub.