ACV-Gate: selective amortization of counterfactual reasoning for visual token communication
The paper introduces ACV-Gate, an adaptive candidate-evaluation framework that learns to approximate full-budget counterfactual evaluation for generative image communication and selectively applies exact evaluations only to the most informative candidates. On CIFAR-10 the primary configuration improves PSNR by 0.636 dB at 0.20 bpp while requiring on average 2.13 candidate evaluations per image (≈27.6% of Exact-Full expert calls); additional tests on STL-10 and higher-resolution transfers show consistent computation–quality trade-offs, especially at low bit rates.