Beyond the Embedding Bottleneck: Adaptive Retrieval-Augmented 3D CT Report Generation

Automated radiology report generation from 3D CT volumes often suffers from incomplete abnormality coverage. We provide empirical evidence that this limitation stems from a representational bottleneck: contrastive 3D CT embeddings encode discriminative abnormality signals, yet exhibit dimensional concentration, with CT-CLIP concentrating 90% of its variance in only 2 of 512 principal components. Corroborating this, scaling the language model yields no measurable improvement, suggesting that the bottleneck lies in the visual representation rather than the generator. This bottleneck limits both generation and retrieval. Image-based similarity often fails to isolate fine-grained abnormalities. We propose AdaRAG-CT, an adaptive augmentation framework that compensates for this visual bottleneck by introducing supplementary textual information through controlled retrieval and selectively integrating it during generation. On the CT-RATE benchmark, AdaRAG-CT reaches a Clinical F1 of 0.480. Ablations show that relevant retrieved context improves clinical efficacy, while adaptive triggering balances clinical and language quality. Code is available at https://anonymous.4open.science/r/AdaRAG-CT-anonymous-5756.