arXiv:2606.09916v1 Announce Type: new Abstract: Multi-turn LLM agents fan short queries into long trajectories of tool calls, search results, and intermediate reasoning. Both KV memory and KV read bandwidth grow by orders of magnitude across a single trajectory, making the key-value (KV) cache, not parameter compute, the dominant serving bottleneck for long-horizon agents. We introduce IntentKV, learned KV pruning that keeps the base LLM frozen. IntentKV maintains a session-level QueryMemory of cross-turn intent, scores live history tokens with a memory-attention rule, and adds a zero-initiali
The rapid development and deployment of multi-turn LLM agents are exposing critical performance bottlenecks related to memory and bandwidth, necessitating novel solutions for efficient operation.
This innovation directly addresses the primary computational bottleneck for long-horizon AI agents, enabling more complex applications and reducing operational costs for a key emerging technology.
By optimizing the KV cache for cross-turn intent, IntentKV allows LLM agents to handle longer, more sophisticated trajectories without commensurate spikes in memory and bandwidth, improving their scalability and practical utility.
- · AI Agent developers
- · Cloud providers
- · LLM operators
- · Enterprise software vendors
- · Legacy LLM architectures
- · Companies with inefficient AI infrastructure
AI agents can execute more complex, multi-step tasks efficiently, improving their utility in business and research.
Reduced operational costs for AI agent inference will accelerate their adoption across various industries, creating new market opportunities.
The enhanced capability and cost-effectiveness of AI agents could lead to a restructuring of white-collar workflows, centralizing more tasks within autonomous systems.
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Read at arXiv cs.LG