Symbolic-driven agentic reasoning for environmental and behavioral event detection

Analyzing complex environmental and behavioral activities requires intelligent systems that not only perceive visual scenes but also reason about underlying events and interactions. Conventional computer vision models often operate at the object-detection level, limiting their ability to generalize across diverse outdoor contexts or provide interpretable explanations for higher-level behaviors such as waterway activity, construction-site analysis, and waste management. This work introduces symbolic-driven agentic reasoning (SDAR), a multimodal framework that bridges perception and cognition for event-level analysis. SDAR employs a symbolic reasoning engine to guide agentic decision making, connecting low-level visual cues with structured symbolic representations of events. Grounding is performed with pre-trained open-vocabulary perception models, without task-specific fine-tuning, while the event logic memory is derived from unlabeled exploration samples rather than manually specified rules. This design enables interpretable reasoning chains that capture causal relationships, contextual dependencies, and event categories. Comprehensive evaluations across 34 real-world field-scene tasks show that SDAR improves average precision by over 10%, achieves 91.7% accuracy in zero-shot open-set detection, and enhances event-level reasoning performance by more than 20%.