3 papers
cs.CL2025
Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs
Preetum Nakkiran, Arwen Bradley, Adam Goliński +3
Large Language Models (LLMs) often lack meaningful confidence estimates for their outputs. While base LLMs are known to exhibit next-token calibration, it remains unclear whether t…
cs.LG2025
To Infinity and Beyond: Tool-Use Unlocks Length Generalization in State Space Models
Eran Malach, Omid Saremi, Sinead Williamson +5
State Space Models (SSMs) have become the leading alternative to Transformers for sequence modeling. Their primary advantage is efficiency in long-context and long-form generation,…
cs.AI2025
Trace Length is a Simple Uncertainty Signal in Reasoning Models
Siddartha Devic, Charlotte Peale, Arwen Bradley +3
Uncertainty quantification for LLMs is a key research direction towards addressing hallucination and other issues that limit their reliable deployment. In this work, we show that r…