Existing　video-LLMs　excel　at　capturing　the　overall　description　of　a　video　but　lack　the　ability　to　demonstrate　an　understanding　of　temporal　dynamics　and　a　fine-grained　grasp　of　localized　content　within　the　video.　In　this　paper,　we　propose　a　Time-Perception　Enhanced　Video　Grounding　via　Boundary　Perception　and　Temporal　Reasoning　aimed　at　mitigating　LLMs＇　difficulties　in　understanding　the　discrepancies　between　video　and　text　temporality.　Specifically,　to　address　the　inherent　biases　in　current　datasets,　we　design　a　series　of　boundary-perception　tasks　to　enable　LLMs　to　capture　accurate　video　temporality.　To　tackle　LLMs＇　insufficient　understanding　of　temporal　information,　we　develop　specialized　tasks　for　boundary　perception　and　temporal　relationship　reasoning　to　deepen　LLMs＇　perception　of　video　temporality.　Our　experimental　results　show　significant　improvements　across　three　datasets:　ActivityNet,　Charades,　and　DiDeMo　(achieving　up　to　11.2%　improvement　on　R@0.3),　demonstrating　the　effectiveness　of　our　proposed　temporal　awareness-enhanced　data　construction　method.　©　2025　Association　for　Computational　Linguistics.