mlsanityData to 7 Oct 2026

Amico: An Event-Driven Modular Framework for Persistent and Embedded Autonomy

arXiv 2507.14513 ↗First posted 19 Jul 2025cs.AIcited by 1

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Abstract (arXiv)

Recent advances in large language models (LLMs) and autonomous agents have enabled systems capable of performing complex tasks across domains such as human-computer interaction, planning, and web navigation. However, many existing frameworks struggle in real-world or resource-constrained environments due to their reliance on cloud-based computation, limited robustness in dynamic contexts, and lack of persistent autonomy and environmental awareness. We present Amico, a modular, event-driven framework for building autonomous agents optimized for embedded systems. Written in Rust for safety and performance, Amico supports reactive, persistent agents that operate efficiently across embedded platforms and browser environments via WebAssembly. It provides clean abstractions for event handling, state management, behavior execution, and integration with reasoning modules. Amico delivers a unified infrastructure for constructing resilient, interactive agents suitable for deployment in settings with limited compute and intermittent connectivity.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 3 of its 8 references that are in the map. First posted on arXiv 19 Jul 2025.

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3 of 8 references.

References · 3

  1. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  2. 2016Modular Multitask Reinforcement Learning with Policy Sketchescited by 200
  3. 2023Violation of Expectation via Metacognitive Prompting Reduces Theory of Mind Prediction Error in Large Language Modelscited by 4

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