TAR: Temporal Anchor-Constrained Reasoning for Video Temporal Grounding

Video Temporal Grounding (VTG) aims to localize specificvideo segments corresponding to natural language queries. While recentLarge Vision-Language Models (LVLMs) employ Reinforcement Learn-ing to generate Chains-of-Thought (CoT), they typically rely solely onoutcome-based supervision. Consequently, this often leads to halluci-nations, where the reasoning process becomes disconnected from thevisual content and the final prediction. Existing attempts to mitigatethis by relying on external supervision from larger models or separatereward models are computationally expensive and prone to rigid pat-terns. To address these challenges, we propose TAR (Temporal Anchor-Constrained Reasoning), a framework that introduces the temporal an-chor (T-anchor) as a transparent and auditable checkpoint mechanism.T-anchor enforces progressive refinement within the CoT, compellingthe model to continuously ground its intermediate thoughts in visual evi-dence and iteratively calibrate temporal predictions, thereby significantlyenhancing the faithfulness and autonomy of the reasoning process and fi-nal accuracy. Furthermore, we introduce a bootstrapping paradigm thatautomatically harvests high-quality CoT data using only a standard 7Bmodel, eliminating the dependency on ultra-large models. Extensive ex-periments demonstrate that TAR achieves state-of-the-art performanceand generates faithful, autonomous, and progressively refined reasoningtraces.