LeCun on the ARC-AGI Benchmark and François Chollet's Test for Intelligence

LeCun's response to François Chollet's ARC-AGI benchmark — a test designed to measure general fluid intelligence by requiring reasoning from very few examples (few-shot learning on novel visual patterns). LeCun agrees with Chollet that current LLMs do not solve ARC-AGI and that this is significant evidence that LLMs lack something important. Where they differ: Chollet believes the solution to ARC-AGI requires a more explicit, program-synthesis-style architecture (his 'arc prize' research direction). LeCun believes it requires world models and JEPA-style learning — the ability to learn abstract representations from sensory data and then apply them flexibly. Both agree that next-token prediction alone doesn't get there. The conversation between LeCun and Chollet (who debate publicly on Twitter/X and in papers) represents the best technical alternative to the 'just scale LLMs' position, because both have proposed specific architectural alternatives rather than just criticizing current approaches. The ARC discussion also connects to the broader question of what benchmarks measure: ARC-AGI intentionally minimizes knowledge and maximizes fluid reasoning, which makes it a good test for the world-model hypothesis. If JEPA-trained models eventually outperform LLMs on ARC-AGI, it would be strong empirical support for LeCun's framework.