AINeutralarXiv – CS AI · Jun 236/10
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TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load
TIP-Search presents a systems-level scheduling framework for real-time market prediction that balances prediction accuracy with deadline satisfaction under computational constraints. Using constrained online optimization and a shielded expert selector (OCO-ACPO), the approach achieves 99.1% timely accuracy and 96.2% deadline satisfaction on financial order book prediction tasks, demonstrating that temporal guarantees matter as much as prediction quality in production trading systems.