arXiv:2606.23993v1 Announce Type: cross Abstract: High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger
The increasing complexity and data rates of high-throughput scientific facilities necessitate more adaptive and efficient data processing methods, driving the exploration of AI for real-time decision-making.
This development highlights the growing application of AI, specifically reinforcement learning, in critical infrastructure and scientific research, optimizing data filtering under extreme constraints.
Real-time data triggering processes, traditionally static and hand-tuned, can now become dynamic and self-optimizing, improving efficiency and adaptability in high-throughput environments.
- · High-energy physics research
- · Generative AI platforms
- · AI-driven automation companies
- · Scientific instrument manufacturers
- · Manual data trigger specialists
- · Legacy data processing systems
Scientific facilities will gain more efficient and adaptive data acquisition, leading to improved research outcomes.
The demonstrated success could accelerate broader adoption of reinforcement learning for real-time control and optimization across various industrial and scientific domains.
This may contribute to a future where critical infrastructure globally relies heavily on autonomous AI agents for operational decision-making.
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Read at arXiv cs.AI