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Toward a Modular Architecture for Embedded AI Agent Systems at the Edge

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arXiv:2606. 02862v1 Announce Type: new Abstract: The rise of Large Language Models (LLMs) has enabled agentic AI capable of complex reasoning and tool use; however, deploying such autonomy in pervasive computing environments remains challenging due to the strict memory and energy constraints of embedded microcontrollers.

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Authors: Marcus R\"ub, Michael Gerhards
Released: 2026-06-03 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Toward a Modular Architecture for Embedded AI Agent Systems at the Edge overlaps with clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)", clean result "Training one persona to emit a [ZLT] marker without bystanders adopting it has a one-cell-wide LR x epochs window on Qwen2.5-7B-Instruct (LOW confidence)". Matching terms: class, source, control, trained, language, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2606.02862

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