This paper presents the outcomes of the PETS2025 challenge, held in conjunction with AVSS 2025 and sponsored by the EU-funded EURMARS project. The challenge introduces a novel maritime surveillance dataset comprising image sequences captured by diverse multi-altitude, multimodal sensors, reflecting the real-world multi-authority environment. The key tasks include: (1) object detection using various sensors across different platforms (ground-based and low-altitude aerial) and spectral ranges (visible, thermal, ultraviolet (UV), and short-wave infrared (SWIR)); (2) long-term tracking of targets in maritime environments spanning both sea and land; and (3) approximating target geolocations by using sensor imagery and telemetry data. Performance evaluations of results submitted by 12 international participants are discussed. The results show the effectiveness of these submissions and highlight ongoing challenges posed by heterogeneous sensors and complex environments. These challenges emphasise the need to further improve detection, tracking, and geolocation approximation for maritime and coastal surveillance.

The Automated Domain-Understanding and Collaborative Agency (ADUCA) system allows for the acquisition of conceptual models of both objects and actions. With exposure to a small number of training exemplars, the ADUCA system constructs a canonical model based on semantically meaningful attributes and relationships that are common to the training data. At test time, the ADUCA system is first tasked with describing an image or video in generic semantic terms which constitutes a bottom up description of the input. The ADUCA system then attempts to determine which if any of its canonical class models is most consistent with the bottom up description. When prompted, the ADUCA system can provide a grounded explanation for its decision as well as answer questions such as why a different conclusion was not drawn. When a mistake has been identified, the ADUCA system attempts to reason over why the mistake was made as well as propose how best to modify its canonical model so as to avoid making similar mistakes in the future. This allows for a form of continuous learning.

We present an architecture and system for understanding novel behaviors of an observed agent. Our approach uses analogy with past experiences to construct hypothetical rationales that attempt to explain the behavior of an observed agent. Moreover, we view analogies as partial; thus multiple past experiences can be blended to analogically explain an unforeseen event, leading to greater inferential flexibility. We argue that this approach results in more meaningful explanations of observed behavior than approaches based on surface-level comparisons. A key advantage of behavior explanation over classification is the ability to i) take appropriate responses based on reasoning and ii) make non-trivial predictions that allow for the verification of the hypothesized explanation. We provide a simple use case to demonstrate novel experience understanding through analogies in a gas station environment.