arXiv:2606.25115v1 Announce Type: new Abstract: On-device language-model agents improve by accumulating experience in retrieved memory rather than by updating weights. This memory is hard-bounded and exposed: it consumes RAM and energy, reaches peers through a thin uplink, and becomes an attack surface because it is writable by what the agent reads. Existing systems each cover one part of this problem: agentic memories grow without a budget, on-device methods keep entries by success alone, and poisoning is studied mainly as an attack rather than as a memory-governance problem. We propose \sys{
The proliferation of on-device LLMs and agents necessitates solutions for efficient and secure memory management, as current approaches are unsustainable for widespread deployment.
This research addresses fundamental constraints (RAM, energy, security) that hinder the scaling and trustworthiness of on-device AI agents, crucial for their broader adoption.
The proposed 'budget-curated memory' system aims to enable more robust, efficient, and secure continual learning directly on devices, moving beyond server-dependent memory solutions.
- · Edge AI device manufacturers
- · On-device LLM developers
- · Cybersecurity for AI solutions
- · Personalized AI agent services
- · AI agent systems relying solely on cloud recall
- · Developers ignoring memory and energy constraints
- · Security-vulnerable on-device AI platforms
On-device AI agents become more practical, secure, and energy-efficient for continuous learning.
Increased deployment of truly autonomous and personalized AI agents that learn from real-world interactions without constant cloud reliance.
New forms of edge computing hardware and software emerge specifically tailored for budget-curated agentic memory and secure on-device AI.
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Read at arXiv cs.LG