[2603.19676] ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.19676 (cs) [Submitted on 20 Mar 2026] Title:ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models Authors:Mohammad Shahab Sepehri, Asal Mehradfar, Berk Tinaz, Salman Avestimehr, Mahdi Soltanolkotabi View a PDF of the paper titled ATHENA: Adaptive Test-Time Steering for Improving Count Fidelity in Diffusion Models, by Mohammad Shahab Sepehri and 4 other authors View PDF Abstract:Text-to-image diffusion models achieve high visual fidelity but surprisingly exhibit systematic failures in numerical control when prompts specify explicit object counts. To address this limitation, we introduce ATHENA, a model-agnostic, test-time adaptive steering framework that improves object count fidelity without modifying model architectures or requiring retraining. ATHENA leverages intermediate representations during sampling to estimate object counts and applies count-aware noise corrections early in the denoising process, steering the generation trajectory before structural errors become difficult to revise. We present three progressively more advanced variants of ATHENA that trade additional computation for improved numerical accuracy, ranging from static prompt-based steering to dynamically adjusted count-aware control. Experiments on established benchmarks and a new visually and semantically complex dataset show that ATHENA consistently improves count fidelity, particularly at higher targ...