Efficient On-Device Agents via Adaptive Context Management

• URL: http://arxiv.org/abs/2511.03728v1
• Date: Wed, 24 Sep 2025 19:46:50 GMT
• Title: Efficient On-Device Agents via Adaptive Context Management
• Authors: Sanidhya Vijayvargiya, Rahul Lokesh,
• Abstract summary: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is constrained by limited memory capacity.<n>We break this trade-off with a framework for context-efficient on-device agents, driven by three synergistic optimizations.<n>Our agent matches, or exceeds, the performance of a conventional baseline while dramatically compressing context.
• Score: 1.1172382217477128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memory capacity, which restricts usable context. This reduced practical context window creates a trade-off between supporting rich, stateful interactions with complex tool capabilities and maintaining on-device feasibility. We break this trade-off with a framework for context-efficient on-device agents, driven by three synergistic optimizations (1) a dynamic memory system using specialized LoRA adapters to distill conversational history into a compressed, and structured Context State Object; (2) a minimalist serialization format for tool schemas to minimize token overhead per tool; and (3) a just-in-time schema-passing mechanism that loads full tool definitions only upon tool selection. We instantiate this framework by adapting a 3B parameter SLM to context-efficient trajectories and rigorously evaluate it against a conventional baseline on complex user tasks. Our agent matches, or exceeds, the performance of a conventional baseline while dramatically compressing context, achieving more than a 6-fold reduction in initial system prompt context and a 10- to 25-fold reduction in context growth rate based on the interaction verbosity, demonstrating that strategic context management is key to unlocking capable and persistent on-device AI.

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