arXiv:2605.30842v1 Announce Type: new Abstract: Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this process typically incurs substantial decoding overhead for the extra summarization tokens, which significantly affect the end-to-end response latency at deployment. In this paper, we introduce CoMem, a novel framework that decouples memory management from the primary agent workflow, enabling these processes to execute in parallel. We propose a $k$-step-off asynchronous pipeline that overlaps the me
The increasing complexity and length of tasks handled by AI agents necessitate more efficient context management to overcome performance and latency bottlenecks.
This development allows AI agents to solve longer-horizon tasks more efficiently, reducing operational costs and improving real-time application responsiveness, which is critical for scaling agentic systems.
The ability to decouple and parallelize memory management significantly reduces the decoding overhead in AI agents, enabling them to process more information faster and handle more complex, multi-step operations.
- · AI Agent developers
- · Cloud computing providers
- · Enterprises adopting AI agents
- · Generative AI model providers
- · Inefficient AI agent architectures
- · High-latency application users
Reduced latency and improved performance of AI agents in complex, long-horizon tasks.
Accelerated deployment and adoption of sophisticated AI agent workflows across professional sectors.
Enhanced automation capabilities potentially leading to a broader displacement of white-collar tasks by more capable AI systems.
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Read at arXiv cs.LG