DOES THE POLICY KNOW WHERE THE BOX IS?

Unified world-action models place a vision-language reasoner and a video-action diffusion generator in one network, on the assumption that the policy inherits the reasoner's ability to ground objects. We test this assumption without training on the released Cosmos3-Nano-Policy-DROID, a 16B Mixture-of-Transformers built on a Qwen3-VL reasoner. Its inference code shows why the assumption could fail: at act time the reasoner stream receives only the text prompt, while the camera canvas enters only the generator, as VAE latents. Paired pixel, text, and activation interventions on 500 post-training frames (200 for activations) drawn from 2,302, and 500 paired closed-loop episodes per condition, show three things. Served in VLM mode, the policy checkpoint's own reasoner localizes the target on 71–76% of single views but 46% of the policy's three-view canvas (its base VLM: 76–78% and 50%); the halved resolution costs 0.14 in hit rate and the three-view layout 0.12. The named target is exposed far more sharply to a linear probe in the reasoner's image tokens than in any of the generator's token groups (top-cell hit 0.55 against at most 0.39; position-only prior 0.24), although both towers carry it (token AUROC 0.85 against 0.81; equal probe mass on the mask), yet the generator's actions follow the object's pixels and re-target when it is moved. The action head reads the instruction's noun: deleting or swapping it costs 39–46 percentage points (pp) of closed-loop success. It does not act on location text: a location sentence shifts actions about as much as a location-free one (0.5 cm more, toward neither location), survives a matched causal knock-out, steers toward neither instance on 26 frames with a verified same-class distractor (75 candidates), and gives no closed-loop gain (−0.4 pp, 95% CI [−4.4, +3.4]). The tower that exposes the object is not the tower that acts, and location text does not bridge them.