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cs.LG2026
Learning Sampling Parameters for Diffusion Models
Arisrei Lim, Yossi Gandelsman
Text-to-image diffusion models expose many inference-time sampling parameters, including prompts, negative prompts, classifier-free guidance scales, and noise schedules. These para…
cs.LG2026
Neuron Populations Exhibit Divergent Selectivity with Scale
Amil Dravid, Yasaman Bahri, Alexei A. Efros +1
We investigate whether neuron populations within neural networks evolve predictably with scale, extending scaling laws beyond macroscopic observables such as loss. To probe this qu…
cs.LG2025
Interpreting the Repeated Token Phenomenon in Large Language Models
Itay Yona, Ilia Shumailov, Jamie Hayes +2
Large Language Models (LLMs), despite their impressive capabilities, often fail to accurately repeat a single word when prompted to, and instead output unrelated text. This unexpla…