Omni-modal LLMs aim to answer queries over heterogeneous inputs through a unified interface, but accepting these inputs does not guarantee using them. % The model’s confidence should change when an informative modality is removed. Existing autoregressive and diffusion multimodal LLMs typically optimize answer likelihood only under the full context, leaving multimodal shortcuts largely unconstrained. We study this problem through the lens of *intervention sensitive omni-modal prediction*, where confidence in the correct answer should decrease when protocol designated evidence is disrupted. We propose *OmniDenoise*, a diffusion MLLM framework built around the *Evidence Denoising Gap* (EDG), a diffusion native objective that compares gold token denoising losses under full and intervened contexts. EDG uses the token-by-timestep denoising trajectory as both a training signal and a diagnostic, without requiring token level evidence annotations. OmniDenoise further applies consistency distillation for efficient few step bidirectional denoising. Across tri-modal, clinical, and out-of-distribution omni-modal benchmarks, OmniDenoise substantially improves over diffusion baselines and remains competitive with strong autoregressive omni models. On OmniBench, the distilled model achieves 45.27% accuracy at 0.19 seconds per sample. On EHRXQA, EDG improves H-Acc from 36.9% to 43.9%, while the distilled student retains 42.6% with a 3.9× speedup.