 | A 150-million-parameter model just redrew the ARC-AGI-1 cost-efficiency frontier. Btw, they show 600B scale retaining the recurrent latent reasoning capabilities. The result was evaluated by Łukasz Kaiser (co-author of the original Transformer paper), Remek Kinas (AI Researcher and co-creator of Bielik LLM) and Richard Zhong (NYU researcher) focused on model evaluation and benchmark robustness. ARC-AGI-1 is a public reasoning benchmark that tests whether a system can infer an underlying rule from a small number of examples and apply it to a new input, a capability often associated with human-like intelligence. Standard LLMs, despite their massive parameter counts, have historically struggled with ARC-AGI because it requires novel reasoning on unfamiliar problems. Transformers typically improve through brute-force scaling, which demands increasingly large amounts of compute. What these researchers have shown is that a non-transformer architecture can climb the ARC-AGI benchmark at a fraction of the compute cost of traditional models. Instead of relying on a standard attention-based Transformer stack and visible CoT, it uses recurrent memory and latent reasoning to achieve these strong results with drastically less compute. New architectures that deliver strong reasoning at low compute costs could change the economics of AI itself. [link] [comments] |
Predictive technology: computational social science
