Do Language Models Understand Time?

Large language models (LLMs) have revolutionized video-based computer vision applications, including action recognition, anomaly detection, and video summarization. Videos inherently pose unique challenges, combining spatial complexity with temporal dynamics that are absent in static images or textual data. Current approaches to video understanding with LLMs often rely on pretrained video encoders to extract spatiotemporal features and text encoders to capture semantic meaning. These representations are integrated within LLM frameworks, enabling multimodal reasoning across diverse video tasks. However, the critical question persists: Can LLMs truly understand the concept of time, and how effectively can they reason about temporal relationships in videos? This work critically examines the role of LLMs in video processing, with a specific focus on their temporal reasoning capabilities. We identify key limitations in the interaction between LLMs and pretrained encoders, revealing gaps in their ability to model long-term dependencies and abstract temporal concepts such as causality and event progression.

Introduction. Large language models (LLMs) have brought transformative advancements to artificial intelligence (AI), excelling across a wide array of tasks in natural language processing and computer vision [11, 52, 185]. Their ability to understand and generate humanlike language has enabled groundbreaking applications, from machine translation to image and video captioning [82] (see Figure 1, frames from EPIC-KITCHENS-100 [38]). More recently, the integration of LLMs into video processing has sparked significant interest, leading to advances in tasks such as action recognition [176, 177], anomaly detection [159, 201, 212], and video summarization [73, 103, 192, 213]. However, videos pose unique challenges compared to other modalities due to their dual reliance on both spatial and temporal information [21]. Unlike static images, videos capture the dimension of time, embedding sequential dynamics that demand sophisticated reasoning [100, 148]. Similarly, unlike textual data, videos involve rich, complex visual elements that require intricate modeling [26, 169].

Discussion / Conclusion. Building on the preceding analysis and discussion, we outline below several promising future research directions for those interested in advancing video LLMs. Overcoming dataset challenges for LLMs. Datasets remain a critical bottleneck in advancing LLM-based video systems. Addressing their limitations requires both creative solutions and resource investments: concepts such as causality, event sequencing, and duration. Architectures like temporal transformers, recurrent neural networks, or hybrid systems that combine hierarchical and sequential processing should be further explored to handle both short-term dynamics and long-term dependencies in video data. Explicit supervision for abstract temporal concepts through enriched annotations is another critical step [43]. Annotated datasets with detailed temporal labels, covering relationships, transitions, and event causality, can significantly boost the temporal reasoning capacity of these systems.