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