arXiv AI By Yiyuan Zou, Wenying Lyu, Clark Borst

Solution space path planning for supporting en-route air traffic control

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arXiv:2607. 00064v1 Announce Type: new Abstract: As technology advances, many path-planning algorithms have been proposed for Air Traffic Management, yet their operational adoption in tactical control remains limited, revealing a misalignment between algorithmic design priorities and air traffic controllers' needs.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jul 24

Declarative Problem Solving in UAM Strategic Deconfliction

arXiv:2607. 21197v1 Announce Type: cross Abstract: The growing demand for Urban Air Mobility (UAM) introduces significant challenges in airspace management, particularly within densely populated metropolitan regions.

By Gioacchino Sterlicchio (DMMM, Polytechnic University of Bari, Bari, Italy), Angelo Oddi (ISTC-CNR, Rome, Italy), Riccardo Rasconi (ISTC-CNR, Rome, Italy), Francesca Alessandra Lisi (DIB,CILA, University of Bari Aldo Moro, Bari, Italy)
arXiv AI
Sep 24

Reachable Global Optimization in AI Systems: How Global Is Global?

The paper "Reachable Global Optimization in AI Systems: How Global Is Global?" argues that claims of AI systems optimizing various components (prompts, policies, architectures, etc.) are underspecified unless they define the region actually reachable by the system. It introduces Reachability-Induced Optimization (RIO), a framework where a generator, verifier, controller, memory, tools, and budget determine a reachable candidate region, and proves several theoretical results about reachable-optimality and related concepts. Extensive benchmarks (66,150 trials across 270 landscapes) demonstrate that control can alter reachability, and that optimization quality, reachability quality, and control reliability must be reported separately.

By Wesley Shu