Learning to Share: Selective Memory for Efficient Parallel Agentic Systems

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• Learning to Share (LTS) significantly reduces overall runtime in parallel agentic systems compared to memory-free parallel baselines.result 90%
• LTS matches or improves task performance compared to memory-free parallel baselines.result 90%
• LTS uses a reinforcement learning controller with usage-aware credit assignment to identify globally useful information.method 85%
• LTS introduces a global memory bank accessible to all parallel agent teams for selective cross-team information reuse.technical 90%
• The reliability of LTS depends on the accuracy of the reinforcement learning model in predicting useful data for sharing.limitation 85%
• Incorrect predictions by the LTS controller could lead to performance drops.limitation 80%
• LTS controls context growth while enabling selective information reuse.technical 85%
• Parallel agent teams in current systems perform substantial overlapping computation when reasoning about similar sub-problems.technical 80%

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