5 articles tagged with #stochastic-systems. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers formalize the problem of synthesizing control policies for stochastic systems that maintain entropy-based objectives in Markov Decision Processes, proving the problem is computationally hard while developing a verification and synthesis method that combines convex duality and invariant synthesis techniques.
A new arXiv paper analyzes the sources of variability in agentic AI systems, distinguishing between token-sampling randomness intrinsic to foundation models and external factors like environmental changes and infrastructure effects. The research clarifies when AI agent outputs are genuinely stochastic versus reproducible, with implications for understanding AI reliability in production deployments.
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.
