3 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
How Much Does a Reasoning Summary Reveal? An Observability Ladder for Large Language Models
Andres Algaba, Francesca Carlon, Lynn Delcon +3
Large language models often show users a final response and a short reasoning summary while the full reasoning trace stays hidden. We introduce an observability ladder that holds e…
Probing the Trajectories of Reasoning Traces in Large Language Models
Marthe Ballon, Brecht Verbeken, Brecht F. Verbeken +2
Large language models (LLMs) increasingly solve difficult problems by producing "reasoning traces" before emitting a final response. However, it remains unclear how accuracy and de…
Estimating problem difficulty without ground truth using Large Language Model comparisons
Marthe Ballon, Andres Algaba, Brecht Verbeken +2
Recent advances in the finetuning of large language models (LLMs) have significantly improved their performance on established benchmarks, emphasizing the need for increasingly dif…
The relationship between reasoning and performance in large language models--o3 (mini) thinks harder, not longer
Marthe Ballon, Andres Algaba, Vincent Ginis
Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and reinforcement learning. However, many open questions remain r…