arXiv:2602.06025v2 Announce Type: replace-cross Abstract: Memory is increasingly central to Large Language Model (LLM) agents operating beyond a single context window, yet most existing systems rely on offline, query-agnostic memory construction that can be inefficient and may discard query-critical information. Although runtime memory utilization is a natural alternative, prior work often incurs substantial overhead and offers limited explicit control over the performance-cost trade-off. In this work, we present \textbf{BudgetMem}, a runtime agent memory framework for explicit, query-aware pe
The increasing complexity and practical deployment of LLM agents necessitate more efficient and adaptable memory management solutions to move beyond single-context window limitations.
Improved runtime memory for AI agents directly addresses a critical bottleneck in deploying more capable and persistent AI systems, which can significantly enhance their autonomy and performance.
The explicit, query-aware control over performance-cost trade-offs in agent memory, offered by BudgetMem, changes how developers optimize and deploy advanced AI agents.
- · AI agent developers
- · Cloud providers
- · Enterprise users of AI
- · Systems relying on inefficient, offline memory management
- · Generative AI models with high inference costs
Reduced operational costs and improved performance for complex AI agent deployments.
Acceleration in the development and adoption of sophisticated, autonomous AI agent workflows across various industries.
Enhanced AI agent capabilities could lead to new forms of automation, impacting white-collar job markets and potentially enabling multi-modal, persistent AI companions.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG