INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration
Quick Answer
INFRAMIND introduces an infrastructure-aware multi-agent orchestration framework that optimizes model selection and scheduling based on real-time system load.
Quick Take
It achieves up to 7.6 percentage points higher accuracy and 7x lower latency compared to previous methods, while maintaining 99.9% SLO compliance under high load conditions.
Key Points
- INFRAMIND adapts model selection based on dynamic infrastructure signals like queue depths and latencies.
- The framework uses reinforcement learning to balance quality and latency effectively.
- It outperforms previous baselines with significantly improved accuracy and reduced latency.
- Achieves up to 99.9% SLO compliance under high load, where other methods fail.
- Optimizes pipelines by prioritizing urgent requests and simpler topologies during congestion.
Paper Resources
Source Excerpt
arXiv:2606. 11440v1 Announce Type: new Abstract: Existing orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features. However, these methods do not consider the runtime state of the serving infrastructure. On shared GPU clusters under concurrent load, this infrastructure blindness causes systematic resource underutilization: preferred models accumulate deep request queues while equally capable alternatives sit idle.
In multi-agent pipelines, where each query triggers multiple sequential model calls, these delays then compound across every downstream step. …
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