Vision-language-action (VLA) models for autonomous driving typically express intermediate reasoning entirely in text, which may discard decision-relevant visual information. We propose Latent Dreaming (LatDM), a latent–text reasoning framework that complements textual chain-of-thought with continuous latent states. LatDM uses latent blocks to preserve visually grounded spatial and temporal information, while text handles semantic abstraction and decision communication. A pretrained visual encoder provides latent supervision during training, and the model autonomously interleaves latent and textual reasoning at inference. Experiments on VLADBench, CODA-LM, and NuInstruct show consistent improvements over the underlying VLA backbone, while ablations confirm that the latent channel contributes beyond standard textual fine-tuning.

Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page works. We introduce WebRISE, which compiles task requirements into Interaction Contract Graphs (ICGs) of observable states, user-intent transitions, and DOM/visual assertions for implementation-agnostic browser execution. WebRISE spans 442 tasks across five input modalities (Text, Markdown, Sketch, Image, Video), with 5,495 transitions and 5,271 requirement checks that separate user-stated functions from implicit product-level constraints. Across 14 MLLMs, even the strongest model reaches only 65.6% transition validity and 66.3% requirement coverage, and visual quality is no proxy for behavior (Qwen3.6-35B-A3B on Markdown: V=80.8 yet T=15.5). Video gives the strongest interaction signal (+10.6pp implicit coverage over Text), while implicit constraints persist; defect injection shows ICG-based scoring detects state errors at 2–16× the rate of checkpoint-style evaluation.