Manifold-Guided Geometric Enhancement for Reliable Multimodal Interaction in Consumer Electronics

Large vision–language models are moving into consumer devices in recent years, where reliability matters as much as speed. A central obstacle is hallucination. We examine the geometry of hidden states and observe a depth-wise shift from near-linear structure in early layers to curved manifolds in deeper fusion layers. This points to a simple design rule for deployment: respect the local geometry when steering the model. Motivated by this finding, we introduce MAGE (<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Manifold-guided Geometric Enhancement</i>), a training-free plug-in intervention that follows this rule. MAGE aligns the guidance with geodesic directions on the unit sphere, filters it through a visual-evidence subspace, aggregates across demonstrations, and applies layer-adaptive residual injections. In CHAIR, the level of sentence hallucination drops by 10.2 points (48.0→37.8), and the level of instances drops by 2.7 points (13.9→11.2). In MME, the total score increases by 73.1 (600.2→676.3). In consumer electronics, the answers are clearer and better grounded, without additional retraining.