Original): Eivind kjosbakken
Originally published in the direction of artificial intelligence.
Discovering the 3 best borderline by comparison – ARC AGI 3
Over the past few weeks, we have seen the release of powerful LLM, such as Qwen 3 Mo, Kimi K2 and Grok 4. We will continue to see such a quick improvement in the foreseeable future and compare LLM with each other, we need reference points. In this article I discuss the newly issued ARC Agi 3 reference point and why the LLMS Frontier are fighting for all tasks in relation.
The article discusses the latest achievements of LLM technology and the release of Benchmark Arc Agi 3, emphasizing the challenges of the LLM Frontier in achieving human performance in comparative tasks, with many models achieving only 0%results. The author examines several factors contributing to these low results, including lack of information during tests, mismatch between training data and comparative tasks, and the concept of exemplary prosecution – where the model's performance is optimized for comparative tests, not authentic intelligence. The conclusion emphasizes hope for future LLM performance improvements in ARC AGI 3, combined with an emphasis on understanding intelligence without restrictions of comparative tests.
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Published via AI
