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[2602.18109] TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs
Summary
TempoNet introduces a novel reinforcement learning scheduler that utilizes a transformer architecture for efficient real-time task dispatching, enhancing deadline adherence and optimization stability.
Why It Matters
As industries increasingly rely on real-time systems, effective scheduling mechanisms are crucial for meeting tight deadlines. TempoNet's innovative approach combines transformer models with reinforcement learning, offering a scalable solution that can adapt to complex task environments, which is vital for improving operational efficiency in critical applications.
Key Takeaways
- TempoNet employs a transformer-guided reinforcement learning approach for scheduling tasks.
- The model enhances deadline fulfillment and optimization stability compared to traditional methods.
- It features a unique Urgency Tokenizer for better value learning and deadline management.
- The architecture allows for efficient global reasoning over unordered task sets.
- Extensive evaluations demonstrate its effectiveness in real-world industrial settings.
Computer Science > Machine Learning arXiv:2602.18109 (cs) [Submitted on 20 Feb 2026] Title:TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs Authors:Rong Fu, Yibo Meng, Guangzhen Yao, Jiaxuan Lu, Zeyu Zhang, Zhaolu Kang, Ziming Guo, Jia Yee Tan, Xiaojing Du, Simon James Fong View a PDF of the paper titled TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs, by Rong Fu and 9 other authors View PDF HTML (experimental) Abstract:Real-time schedulers must reason about tight deadlines under strict compute budgets. We present TempoNet, a reinforcement learning scheduler that pairs a permutation-invariant Transformer with a deep Q-approximation. An Urgency Tokenizer discretizes temporal slack into learnable embeddings, stabilizing value learning and capturing deadline proximity. A latency-aware sparse attention stack with blockwise top-k selection and locality-sensitive chunking enables global reasoning over unordered task sets with near-linear scaling and sub-millisecond inference. A multicore mapping layer converts contextualized Q-scores into processor assignments through masked-greedy selection or differentiable matching. Extensive evaluations on industrial mixed-criticality traces and large multiprocessor settings show consistent gains in deadline fulfillment over analytic schedulers and neural baselines, together with improved optimization s...
