Efficient On-Device Agents via Adaptive Context Management
Quick summary
arXiv: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
Key takeaways
- arXiv: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.
Why it matters
“Efficient On-Device Agents via Adaptive Context Management” is a product decision that may change how people work with AI. Its value depends on task completion, correction effort and data handling—not simply the presence of a new feature.
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