Research paper titled “From Disorder to Design: Physical Mechanisms Governing Generalization and Hallucination in Deep Learning for Imaging Through Scattering Media”, with Professor Puxiang LAI as the co-correspondence author, was recently published in Nature Communications (IF=18.1; ranked 8th out of 140 SCI journals in Multidisciplinary Sciences). This work leverages a physics-guided framework based on scattering media and developed a model system where controlled variations in light transmission matrices (T) isolates the long-standing challenges in the field, unravelling the mechanistic interplay between generalization limits and hallucination origins.
Xuyu Zhang, Tianting Zhong, Haofan Huang] Dawei Zhang, Songlin Zhuang, Shensheng Han, Puxiang Lai*, and Honglin Liu*, "From Disorder to Design: Physical Mechanisms Governing Generalization and Hallucination in Deep Learning for Imaging Through Scattering Media", Nature Communications 17: 5616 (2026). doi: 10.1038/s41467-026-72304-z
Abstract
Deep learning has revolutionized computational imaging, yet its real-world deployment remains constrained by two critical challenges: poor generalization under dynamic conditions and the emergence of hallucinatory artifacts. By leveraging a physics-guided framework based on scattering media, a model system where controlled variations in light transmission matrices (T) isolates these challenges, we unravel the mechanistic interplay between generalization limits and hallucination origins. We demonstrate that a network’s generalization capacity is fundamentally bounded by its ability to accommodate distinct inverse mappings (T -1), while hallucinations arise when this capacity is exceeded, resulting in unconstrained, non-physical predictions. We also identify residual ballistic light, if not negligible, as a stabilizing anchor, enabling robust predictions under scattering variability. Integrating experimental validation with wave-optics simulations, we establish a universal framework that links these phenomena, showing that strategic training on diverse physical mappings enhances generalization while suppressing hallucinations. This work bridges physics-driven interpretability with AI design, offering actionable strategies to develop reliable models for applications ranging from medical imaging through biological tissues to autonomous navigation in scattering environments.
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