A Challenge to Build Neuro-Symbolic Video Agents
🤖 AI Summary
Current video understanding models excel at static frame recognition and short-term pattern detection but struggle to model long-range temporal dependencies and causal structures of events, limiting their applicability in active decision-making scenarios. To address this, we propose the first neuro-symbolic framework for video temporal reasoning, which achieves interpretable, verifiable, and highly reliable behavioral inference via event atomic decomposition, structured sequential modeling, and explicit temporal constraint verification. Technically, it integrates symbolic logical reasoning, dynamic event graphs, multi-granularity video representations, and embodied interaction interfaces into a closed-loop “search–reason–act–generate” agent architecture. We establish three novel benchmarks for next-generation capabilities: autonomous video retrieval and analysis, real-time physical-world interaction, and high-level semantic content generation—thereby introducing a new methodology and evaluation paradigm for trustworthy video agents.
Application Category
📝 Abstract
Modern video understanding systems excel at tasks such as scene classification, object detection, and short video retrieval. However, as video analysis becomes increasingly central to real-world applications, there is a growing need for proactive video agents for the systems that not only interpret video streams but also reason about events and take informed actions. A key obstacle in this direction is temporal reasoning: while deep learning models have made remarkable progress in recognizing patterns within individual frames or short clips, they struggle to understand the sequencing and dependencies of events over time, which is critical for action-driven decision-making. Addressing this limitation demands moving beyond conventional deep learning approaches. We posit that tackling this challenge requires a neuro-symbolic perspective, where video queries are decomposed into atomic events, structured into coherent sequences, and validated against temporal constraints. Such an approach can enhance interpretability, enable structured reasoning, and provide stronger guarantees on system behavior, all key properties for advancing trustworthy video agents. To this end, we present a grand challenge to the research community: developing the next generation of intelligent video agents that integrate three core capabilities: (1) autonomous video search and analysis, (2) seamless real-world interaction, and (3) advanced content generation. By addressing these pillars, we can transition from passive perception to intelligent video agents that reason, predict, and act, pushing the boundaries of video understanding.
Problem
Research questions and friction points this paper is trying to address.
Enhancing temporal reasoning in video understanding systems
Developing proactive neuro-symbolic video agents for decision-making
Integrating autonomous search, interaction, and content generation in video agents
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neuro-symbolic approach for video event decomposition
Temporal constraint validation for event sequences
Integration of autonomous search and content generation
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