Task-oriented Vehicle-to-Everything (V2X) networks have recently been proposed to scalably support the large-scale deployment of connected vehicles. In task-oriented V2X networks, vehicles select the content of the transmitted messages based on its relevance to the intended receivers. However, estimating relevance can be challenging, especially in highly dynamic and complex driving scenarios. Relevance estimation errors may cause a transmitting vehicle to share incomplete information, omitting relevant data that is critical for the intended receivers' situational awareness. This work numerically demonstrates that task-oriented V2X networks exhibit an inherent resilience to incomplete information sharing. We show that such resilience guarantees a consistent delivery of relevant information even under high relevance estimation error probability conditions. Furthermore, we show that the fundamental conditions underpinning such inherent resilience can also be encountered outside of the V2X domain - in particular, in other task-oriented networks where multiple transmitters select the content of their messages based on the task-related requirements of a common set of intended receivers.

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