3 papers
math.ST2026
On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective
Nestor R. Barraza, Gabriel Pena
Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency. However, their achievable performance is fundamentally constrained…
cs.CL2025
Instability in Downstream Task Performance During LLM Pretraining
Yuto Nishida, Masaru Isonuma, Yusuke Oda
When training large language models (LLMs), it is common practice to track downstream task performance throughout the training process and select the checkpoint with the highest va…
cs.CL2025
Do LLMs Need to Think in One Language? Correlation between Latent Language and Task Performance
Shintaro Ozaki, Tatsuya Hiraoka, Hiroto Otake +8
Large Language Models (LLMs) are known to process information using a proficient internal language consistently, referred to as latent language, which may differ from the input or…