Efficient On-Device Agents via Adaptive Context Management

תקציר מקורי באנגליתarXiv:2511.03728v2 Announce Type: replace 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-ca