OmniVAL: Validated Chain-of-Thought Distillation for True Native Omni Models

True native omni models promise a single architecture for understanding text, images, audio, and video, yet a critical gap remains: compact open-source omni models still fall short of proprietary frontier systems, and the gap is widest where it matters most - in *grounding* each answer in the right modality and the right evidence. We trace this gap to how such models are trained. Fine-tuning and distillation transfer final outputs or representations, but never the intermediate reasoning that grounding depends on: deciding which modality holds the evidence, localizing it, and expressing it in the proper coordinate space. We introduce **OmniVAL**, a validated chain-of-thought distillation framework that makes these decisions explicit and transfers them from a frontier teacher to a compact open-source student. A teacher generates modality-routed reasoning traces; **MA-VAL**, a modality-aware rule-based validator, filters unreliable traces and turns the student's remaining errors into corrective feedback in the units of each modality, whether pixels, milliseconds, cell indices, or modality-routing fixes. Perception tools serve only as training-time scaffolding: at inference the student is a pure omni model that answers and grounds in a single autoregressive pass. Across 16 benchmarks spanning documents, GUI agency, video, audio-visual reasoning, voice interaction, ASR, and speaker diarization, OmniVAL improves Nemotron 3 Nano Omni, an open 30B model with 3B active parameters, on every benchmark, raising DocVQA grounding mAP from 71.4 to 85.5 and cutting ASR word error rate by 18%; the resulting student surpasses GPT-4o and all open-source omni baselines and substantially narrows the gap to closed-source frontier systems. We release **OmniVAL-CoT**, 110K validator-verified multimodal reasoning traces, and show that half of this validated corpus outperforms the full unfiltered teacher pool. Implementation: https://anonymous.4open.science/r/OmniVal-216F