[2603.17112] Cascade-Aware Multi-Agent Routing: Spatio-Temporal Sidecars and Geometry-Switching

Computer Science > Artificial Intelligence arXiv:2603.17112 (cs) [Submitted on 17 Mar 2026 (v1), last revised 24 Mar 2026 (this version, v2)] Title:Cascade-Aware Multi-Agent Routing: Spatio-Temporal Sidecars and Geometry-Switching Authors:Davide Di Gioia View a PDF of the paper titled Cascade-Aware Multi-Agent Routing: Spatio-Temporal Sidecars and Geometry-Switching, by Davide Di Gioia View PDF HTML (experimental) Abstract:Advanced AI reasoning systems route tasks through dynamic execution graphs of specialized agents. We identify a structural blind spot in this architecture: schedulers optimize load and fitness but lack a model of how failure propagates differently in tree-like versus cyclic graphs. In tree-like regimes, a single failure cascades exponentially; in dense cyclic regimes, it self-limits. A geometry-blind scheduler cannot distinguish these cases. We formalize this observability gap as an online geometry-control problem. We prove a cascade-sensitivity condition: failure spread is supercritical when per-edge propagation probability exceeds the inverse of the graph's branching factor (p > e^{-\gamma}, where \gamma is the BFS shell-growth exponent). We close this gap with a spatio-temporal sidecar that predicts which routing geometry fits the current topology. The sidecar comprises (i) a Euclidean propagation scorer for dense, cyclic subgraphs, (ii) a hyperbolic scorer capturing exponential risk in tree-like subgraphs, and (iii) a compact learned gate (133 parame...