3 citations · 5 across the 26 of their papers we have counts for
Showing 2026 · cs.CLShow all
2 papers · 2 filters
cs.CL2026
When the Canonical Completion Is Wrong: Formalizing and Measuring the Jump in Large Language Models
Dai Shi, Xiaoyu Li, José Miguel Hernández-Lobato
Whether large language models (LLMs) can perform the abductive leap from evidence to a new system of axioms, commonly referred to as a jump, has recently attracted considerable deb…
cs.CL2026
Recipes for Steering and Scaling LLMs via Sampling
Jiajun He, Zongyu Guo, José Miguel Hernández-Lobato +1
Large Language Models (LLMs) are probabilistic models, typically defined by an autoregressive factorization. While recent work has begun to study richer target distributions beyond…