The field of large language models is shifting towards a more nuanced understanding of their capabilities and limitations. Researchers are moving beyond traditional evaluation metrics and are instead focusing on the ability of these models to reason about their own behavior, ask for information when necessary, and introspect about their internal states. This change in direction is driven by the recognition that true intelligence requires more than just solving well-defined problems, but also the ability to adapt, learn, and understand one's own limitations. Noteworthy papers in this area include:

• A study that proposes a new dataset to evaluate the ability of large reasoning models to ask for information, revealing their inability to do so and highlighting the potential of supervised fine-tuning.
• Research that introduces the Self-Execution Benchmark to measure a model's ability to anticipate properties of its output, showing that models generally perform poorly on this task.
• A paper that argues for a thicker definition of introspection in AI, demonstrating that models can appear to have introspection while failing to meaningfully do so.