Video event prediction (VEP) requires models to infer unobserved future states from partial video evidence. Existing video MLLMs usually verbalize intermediate future reasoning in text space: once visual evidence is verbalized, fine-grained motion, geometry, and interaction cues can be lost, leading to plausible but visually ungrounded hallucinations. We introduce **Future-L1**, an **interleaved latent visual reasoning framework** that lets an MLLM alternate between language tokens and continuous latent visual spans during autoregressive decoding. To train this capability, we construct **Future-L1-50K** by selecting examples where future visual hints help prediction and aligning latent states to future-frame embeddings, then further optimizing sampled latent trajectories with **LA-DAPO**, a **latent-aware RL** objective with outcome-contrastive and temporal-diversity rewards. **Future-L1** achieves new **state-of-the-art** results on both benchmarks: on FutureBench, it improves Qwen3-VL-8B from 61.0 to **85.4** and exceeds the previous best Video-CoE by 10.4 points; on TwiFF-Bench, it improves the average score from 2.44 to **3.04**. These results suggest that future-oriented video reasoning benefits from preserving intermediate visual semantics in latent space rather than translating every reasoning step into text.