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cs.LG2026
STAPLE: Static Sparse Upcycling of Pretrained Checkpoints via Embedding Modules
Ranajoy Sadhukhan, Euijin Hong, Sheng Cao +9
Sparse computation can scale the parametric capacity of LLMs without proportionally increasing active computation, but pretraining sparse architectures remains expensive and data-h…
cs.LG2026
STEM: Scaling Transformers with Embedding Modules
Ranajoy Sadhukhan, Sheng Cao, Harry Dong +5
Fine-grained sparsity promises higher parametric capacity without proportional per-token compute, but often suffers from training instability, load balancing, and communication ove…
econ.TH2026
Supracompetitive Pricing Under AI Monoculture
Shengyu Cao, Ming Hu
When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with performance-driven updates aggregating seller…