IntentKV: Cross-Turn Intent-Aware KV Cache Pruning for Agent Inference
📰 ArXiv cs.AI
Learn how IntentKV improves agent inference by pruning KV cache, reducing serving bottleneck for long-horizon agents
Action Steps
- Implement IntentKV to prune KV cache for agent inference
- Use QueryMemory to store session-level information
- Evaluate the impact of IntentKV on KV read bandwidth and memory usage
- Compare IntentKV with other cache pruning techniques
- Apply IntentKV to real-world applications with long-horizon agents
Who Needs to Know This
AI engineers and researchers working on multi-turn LLM agents can benefit from this technique to optimize agent performance
Key Insight
💡 IntentKV reduces serving bottleneck for long-horizon agents by pruning KV cache, making it a key technique for optimizing agent performance
Share This
🚀 Improve agent inference with IntentKV, a learned KV pruning technique for multi-turn LLM agents!
Key Takeaways
Learn how IntentKV improves agent inference by pruning KV cache, reducing serving bottleneck for long-horizon agents
Full Article
Title: IntentKV: Cross-Turn Intent-Aware KV Cache Pruning for Agent Inference
Abstract:
arXiv:2606.09916v1 Announce Type: cross 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 o
Abstract:
arXiv:2606.09916v1 Announce Type: cross 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 o
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