Causality-Aware Temporal Projection for Video Understanding in Video-LLMs

2026-01-05

arXiv.org

AI Summary

This work addresses the challenge that existing video large language models often fail to preserve strict temporal order and causal consistency due to bidirectional temporal modeling, which disrupts chronological sequence. To resolve this, the authors propose the V-CORE framework, which incorporates Learnable Spatial Aggregation (LSA) to retain spatial interactions and introduces a Causal-Aware Temporal Projector (CATP) to enforce unidirectional temporal information flow, thereby ensuring causal consistency. The approach explicitly models temporal ordering constraints through block-wise causal attention and dynamic causal sink tokens. Furthermore, it adopts 4-bit QLoRA fine-tuning with a frozen LLM backbone for computational efficiency. Evaluated on NExT-QA, the method achieves 61.2% accuracy, with notable improvements of 3.5% and 5.2% on temporal and causal reasoning subsets, respectively, demonstrating the effectiveness of the proposed temporal constraint mechanism.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Recent Video Large Language Models (Video-LLMs) have shown strong multimodal reasoning capabilities, yet remain challenged by video understanding tasks that require consistent temporal ordering and causal coherence. Many parameter-efficient Video-LLMs rely on unconstrained bidirectional projectors to model inter-frame interactions, which can blur temporal ordering by allowing later frames to influence earlier representations, without explicit architectural mechanisms to respect the directional nature of video reasoning. To address this limitation, we propose V-CORE, a parameter-efficient framework that introduces explicit temporal ordering constraints for video understanding. V-CORE consists of two key components: (1) Learnable Spatial Aggregation (LSA), which adaptively selects salient spatial tokens to reduce redundancy, and (2) a Causality-Aware Temporal Projector (CATP), which enforces structured unidirectional information flow via block-causal attention and a terminal dynamic summary token acting as a causal sink. This design preserves intra-frame spatial interactions while ensuring that temporal information is aggregated in a strictly ordered manner. With 4-bit QLoRA and a frozen LLM backbone, V-CORE can be trained efficiently on a single consumer GPU. Experiments show that V-CORE achieves strong performance on the challenging NExT-QA benchmark, reaching 61.2% accuracy, and remains competitive across MSVD-QA, MSRVTT-QA, and TGIF-QA, with gains concentrated in temporal and causal reasoning subcategories (+3.5% and +5.2% respectively), directly validating the importance of explicit temporal ordering constraints.

Problem

• temporal ordering
• causal coherence
• video understanding
• Video-LLMs
• bidirectional projector

Innovation

• Causality-Aware Temporal Projection
• Block-Causal Attention
• Learnable Spatial Aggregation
• Video-LLMs
• Temporal Ordering Constraints