arXiv:2606.25274v1 Announce Type: new Abstract: Time-series models are usually scored as forecasters, yet deployed systems often require delayed decisions under uncertainty and hard feasibility constraints. UC-Search is a model-agnostic test-time wrapper: a backbone emits forecasts or action scores, a feasibility automaton rolls candidate paths forward, and bounded search returns the first action of a risk-adjusted feasible trajectory. We instantiate UC-Beam and a UCT-style UC-MCTS diagnostic, using epistemic, aleatoric, and propagated uncertainty mainly as path-risk terms. A myopic-collapse/s
The increasing complexity and safety requirements of deployed AI systems necessitate advanced control mechanisms that account for uncertainty and constraints, pushing research towards risk-aware solutions.
This development represents a significant step towards more reliable and autonomous AI agents capable of operating in critical, real-world environments with probabilistic guarantees.
Traditional time-series models, typically scored as forecasters, can now be augmented with sophisticated test-time wrappers like UC-Search to make delayed, risk-adjusted, and feasible decisions.
- · AI Safety Researchers
- · Autonomous Systems Developers
- · High-Stakes AI Applications (e.g., defense, healthcare, industrial control)
- · AI Agent Developers
- · Developers of simplistic, non-risk-aware control systems
- · Traditional forecasting-only AI applications in critical domains
More robust and trustworthy AI-driven decision-making in systems with hard constraints and delayed outcomes.
Accelerated adoption of AI in domains previously hesitant due to safety and reliability concerns.
Enhanced AI agents capable of autonomous complex action sequences in dynamic and uncertain real-world environments.
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Read at arXiv cs.LG