How Video MLLMs Form Temporal Boundaries: Boundary Flow Tracing and Temporal Revisit

Generative video temporal grounding enables video multimodal large language models (MLLMs) to directly produce temporal boundaries, yet their formation remains unclear. We investigate start and end timestamp formation through route interventions and representation probing. Boundary Flow Tracing reveals endpoint- and depth-dependent pathways in TimeLens, with visual-to-prompt effects concentrated at earlier depths than prompt-to-boundary effects. Qwen3-VL and InternVL2.5 also exhibit prompt-to-boundary dependence, with model-specific depth profiles. Restricted readouts recover temporal cues from intermediate visual states, while MiMo-VL probes provide additional evidence of boundary information before generation. These observations motivate Temporal Revisit, a learned intervention that returns intermediate visual evidence to later visual states of a frozen backbone. Broad Temporal Revisit (BTR) tests whether this evidence can improve native boundary generation. Hypothesis-Guided Temporal Revisit (HTR) further examines whether prediction-conditioned reuse with preservation training can improve grounding while limiting prediction changes. BTR improves mIoU on TimeLens-SFT, Qwen3-VL, and MiMo-VL across the three evaluated benchmarks. On TimeLens-SFT, the gains are 3.92, 2.85, and 2.19 percentage points on QVHighlights-TimeLens, Charades-TimeLens, and ActivityNet-TimeLens, respectively. After GRPO, HTR improves native mIoU while modifying fewer predictions than BTR. Post-intervention tracing on TimeLens-SFT shows stronger direct visual-to-boundary dependence after BTR. These results support learned reuse of recoverable temporal evidence, with benefits that depend on the host and intervention design.