Diffusion-Based Immersive Visual Reasoning

Visual reasoning in immersive environments is critical forVR, AR, and robotics, yet is fundamentally challenged by partial ob-servability. Existing Vision-Language Models (VLMs) are ill-suited forthis task, as their rigid “look first, think later” paradigm decouples per-ception from reasoning. They either process distorted, low signal-to-noisepanoramas or reason over a fixed set of pre-determined views, preventingdynamic, task-driven exploration. We reframe immersive visual reason-ing as an iterative process of probabilistic belief updating. We introduceDiffusion-Based Immersive Visual Reasoning (DIVR), a novel frameworkthat models reasoning as a denoising diffusion process, progressively re-fining a latent reasoning hypothesis from a state of high uncertainty toa denoised low-uncertainty state. DIVR comprises two tightly coupledmodules: a Latent Hypothesis Refiner (LHR) that uses observed visualevidence to denoise the current hypothesis, and a Reasoning-Guided Ob-server (RGO) that uses the denoised hypothesis to actively select the nextmost informative viewport. This creates a closed-loop system where theevolving reasoning hypothesis continuously guides perception, distinctfrom the rigid, open-loop paradigm of prior work. Extensive experimentson multiple 360° VQA benchmarks demonstrate that DIVR establishesa new state-of-the-art, significantly outperforming strong baselines, par-ticularly on complex questions that require dynamic, multi-step evidencegathering.