AI Insight
This paper presents a novel approach to running AI agents on smartphones and other resource-constrained devices by developing adaptive context management techniques. The researchers introduce two key mechanisms: a learned memory architecture that compresses conversation history into essential "Context State Objects" while maintaining performance, and a just-in-time tool schema system that only loads full tool definitions when needed. Testing with 3-billion parameter models on smartphone hardware showed the system reduces initial tool context by over 6 times and ongoing interaction context growth by 10-25 times compared to standard approaches, while maintaining task performance at or above full-history execution levels.
Why it matters
This research addresses a critical bottleneck in deploying sophisticated AI assistants directly on personal devices rather than cloud servers. By dramatically reducing memory requirements without sacrificing capability, it could enable more private, faster, and offline-capable AI assistants on smartphones and other edge devices.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memory capacity. Context in agentic settings worsens this problem due to large static tool schemas and a growing interaction history that continually expands the persistent KV cache. To maintain on-device feasibility, agents must operate near the minimum task-sufficient context, while preserving task performance. We introduce two complementary mechanisms: (1) a learned intra-session memory architecture that distills trajectories into an append-only Context State Object (CSO), preserving current state and relevant details from previous steps that may have future utility, while still supporting KV-cache reuse, and (2) a just-in-time schema-passing mechanism that loads full tool definitions only upon tool selection. We instantiate this framework by adapting 3B-parameter SLMs and profiling them on smartphone hardware. In our evaluations, CSO-based memory outperforms KV-cache compression and trained summarization while retaining performance near or even above full-history execution. Deployed on-device, our framework reduces initial tool context by over $6times$ and interaction-context growth by 10-25 times, substantially reducing initial
Source: Efficient On-Device Agents via Adaptive Context Management