3 articles tagged with #stochastic-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers define 'Agentic Technical Debt' as governance liabilities arising from rapidly deployed AI agent systems that lack proper validation and standardization. The paper distinguishes this from traditional technical debt and introduces 'Stochastic Tax' as the ongoing operational cost of managing probabilistic agent behavior, proposing lightweight dashboards and controls to address these challenges.
Researchers introduce Stochastic Lifting, a machine learning technique that generates diverse trajectories of stochastic physical systems by attaching random labels to state transitions during training. The method enables single-network inference to produce multiple plausible outcomes without collapsing to average predictions, advancing physics-informed AI applications.
Researchers introduce resilient strategies for stochastic systems, focusing on decision-making that remains robust against disturbances that could flip agent decisions. The work presents fundamental problems for Markov decision processes with reachability and safety objectives, extending to stochastic games with various disturbance aggregation methods.
