arXiv:2607.05708v1 Announce Type: new Abstract: Recent LLM-based agent systems continuously accumulate context across multi-turn interactions, tool invocations, and cross-session workflows. Replaying the full history for every request quickly becomes impractical: long contexts increase prefill cost, may exceed context limits, and often bury task-relevant evidence in irrelevant content, degrading both serving efficiency and output quality. We propose Akashic, a low-overhead memory system built around MemAttention, which organizes context into bounded chunks and models semantic relationships acr
The continuous accumulation of context in LLM-based agent systems is reaching practical limits, driving innovation in memory management to improve efficiency and performance.
Improving LLM inference efficiency directly impacts the scalability, cost, and output quality of AI agents, accelerating their deployment across various applications.
The proposed MemAttention system changes how LLMs manage long-term context, enabling more practical and effective agentic AI applications.
- · AI Agent developers
- · Cloud providers offering LLM services
- · Industries adopting agentic AI
- · Hardware manufacturers optimizing for LLM inference
- · LLM architectures inefficient with long contexts
- · Legacy inference serving solutions
- · Systems unable to integrate new memory management techniques
More complex and persistent AI agents become economically viable and perform better.
Increased adoption of AI agents could lead to further automation of white-collar tasks and workflow collapse.
The enhanced capability and efficiency of AI agents could accelerate the development of more autonomous and sophisticated AI systems, potentially impacting societal structures.
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Read at arXiv cs.AI