Deception: Understanding the power and restrictions of modeling models with the difficulty of the problems
The latest generations of the former languages inform the largest consultation models (LRMS) that produces detailed thinking procedures before giving answers. While these models indicate advanced performance from the reformed benchmarks, their basic skills, formal buildings, and well-understood limits. The current study is primarily FO-COs in the established benchmarks and established codes, emphasizing the final accuracy. However, this test paradig is often suffering from data pollution and does not provide comprehension to the structure of definitions and quality. In this work, we are formally investigating these posts with the help of the puzzle facilities that allows the direct deception of the difficulties of integrating while maintaining unchangeable buildings. This setup gives the analysis only the final answers but also internal consultants, which provides information that LRMS “thinks”. With a wide exploration on all different puzzles, we show that the frontier LRMS faces a complete deterioration of more difficulty. In addition, they show limited measurement limit: Their maximum effort is increasing with difficulties of problems until the point, and they refuse even though they have a model budget. By comparing the LRMS and their ordinary llm partners under equity, we identify three units, (1) low-use functions in LRMs reflecting the profit, and (3) complex functions where both models receive complete fall. We have found that the LRMs have the limitations in direct complication: They fail to use clear algorithms and benefit from the puzzles. We also investigate following traces in addition to further, reading medical solutions and analyzing ethical models, enabling power, energy, and ultimately increasing important questions about their genuine thinking skills.
* An equal offering.
4 † work done during an internship in Apple.
