arXiv:2610.30874v1 Announce Type: new Abstract: Auditory scene analysis (ASA) models extract information about individual sound sources from a complex auditory mixture. Recent object-based ASA models process the mixture through a sequence of modules and split it into source-wise features (i.e., object features) only at the last layer. However, substantial disparities in performance across sources limit overall performance. Our analysis using centered kernel alignment (CKA) reveals that CKA scores correlate strongly with per-source performance, with their disparities widening from intermediate to deeper layers. These findings motivate the early split and exchange of object features to address source-wise performance disparities before they widen in deeper layers. To this end, we propose PARASITE, a plug-in refinement architecture that splits object features at earlier layers and progressively refines them through sequential layers. Each layer integrates object features separated at different depths using Object-wise Gated Fusion (OGF) and then refines them using Windowed Cross-Object Attention (WCOA). We introduce the DeepASA++ model, which combines PARASITE with an ASA backbone and delivers state-of-the-art results on both ASA2 and STARSS23. DeepASA++ achieves a SELD score of 0.240 on STARSS23 and surpasses prior multi-stage inference architectures, including chain-of-inference.