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cs.LG2025
CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding
Ziteng Sun, Adrian Benton, Samuel Kushnir +4
Post-training quantization is an effective method for reducing the serving cost of large language models, where the standard approach is to use a round-to-nearest quantization leve…
cs.LG2025
Composing Linear Layers from Irreducibles
Travis Pence, Daisuke Yamada, Vikas Singh
Contemporary large models often exhibit behaviors suggesting the presence of low-level primitives that compose into modules with richer functionality, but these fundamental buildin…
cs.LG2025
FoGE: Fock Space inspired encoding for graph prompting
Sotirios Panagiotis Chytas, Rudrasis Chakraborty, Vikas Singh
Recent results show that modern Large Language Models (LLM) are indeed capable of understanding and answering questions about structured data such as graphs. This new paradigm can…