Polytechnic University of Bari

Gaussian sampling no longer has to be computed by digital arithmetic: a probabilistic Ising machine produces it from physical stochasticity, and at matched sampling precision the FPGA sampler core uses about 4.0-fold less dynamic power and 4.4-fold fewer logic slices than the isolated Falcon Gaussian core, with diffusion image quality preserved.

Gaussian sampling no longer has to be computed by digital arithmetic: a probabilistic Ising machine produces it from physical stochasticity, and at matched sampling precision the FPGA sampler core uses about 4.0-fold less dynamic power and 4.4-fold fewer logic slices than the isolated Falcon Gaussian core, with diffusion image quality preserved.

Using sparse-autoencoder (SAE) features in Gemma 3 4B/12B IT and Qwen3-8B on clinical triage vignettes, this study finds that medical features fire on the shared clinical narrative under both formats but are inactive at the multiple-choice decision token; emergency-tier information is linearly decodable from vignette representations (ROC-AUC 0.95–1.00) with no significant format difference yet is attenuated at the decision token, and scaffold-peaking features account for over 91% of unsigned attribution in both Gemma models, placing the strongest correlates of the format effect at answer selection.

Using sparse-autoencoder (SAE) features in Gemma 3 4B/12B IT and Qwen3-8B on clinical triage vignettes, this study finds that medical features fire on the shared clinical narrative under both formats but are inactive at the multiple-choice decision token; emergency-tier information is linearly decodable from vignette representations (ROC-AUC 0.95–1.00) with no significant format difference yet is attenuated at the decision token, and scaffold-peaking features account for over 91% of unsigned attribution in both Gemma models, placing the strongest correlates of the format effect at answer selection.