Author

Andrew Lee

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#machine learning Preprint Sep 2026

Large Language Models Develop Belief State Geometry In-Context

Large language models trained on next-token prediction exhibit remarkable in-context learning (ICL) abilities, yet the representations that support ICL remain poorly understood, and representation-level evidence that ICL in open-source LLMs approximates optimal Bayesian prediction over a context-inferred generative mod...

Daniel Balcells, Andrew Lee, Chirag Rastogi et al. · 1 citation · ⚡1

#artificial intelligence Dec 2025

Better World Models Can Lead to Better Post-Training Performance

It is found that explicit world-modeling yields better representations in terms of higher probing accuracy and steerability of the model, and that better representations yield larger gains from GRPO, especially on harder cube states.

Prakhar Gupta, Henry Conklin, Sarah-Jane Leslie et al. · arXiv.org · 3 citations