ViPLE: Visual Inference with Parallel Latent Encoding

Explicit visual reasoning generates intermediate text sequentially, increasing decoding cost. We investigate whether useful visual reasoning context can instead be constructed in parallel for a frozen multimodal backbone. We propose VIPLE (Visual Inference with Parallel Latent Encoding), which extends one-pass latent trajectory encoding with a final cross-attention read of fused image-question states. The latent slots attend to these states before conditioning the frozen decoder. Supervised alignment, on-policy distillation, and reinforcement learning train the interface while keeping the backbone frozen. Across six benchmarks, on Qwen3-VL-2B, 4B, and 8B, VIPLE improves macro accuracy by **+(5.12–7.15) pp** over the cheapest measured explicit-reasoning settings while decoding fewer tokens. On GLM-4.1V-9B and Qwen3-VL-32B, the gains are +2.31 pp and +2.50 pp, respectively, at matched decoding costs obtained by interpolation. The cheapest measured explicit-reasoning settings that reach VIPLE's accuracy require **(1.8–5.8)×** as many decoded tokens. Length-preserving replacement controls show the importance of learned latent content, while retraining without the final cross-attention layer reduces accuracy on mathematical tasks. These results demonstrate that a learned parallel interface can improve the accuracy-decode-cost trade-off of frozen multimodal models.