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LG's EXAONE Deep: Revolutionizing Maths, Science, and Coding with AI Brilliance

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In a groundbreaking move that could change the future of education, LG AI Research recently unveiled its latest innovation, EXAONE Deep. This powerful reasoning model is set to change the game in fields like mathematics, science, and coding. Are you ready to see the impact?

As LG aptly pointed out, there's a significant gap in developing advanced reasoning models, particularly due to the complexity involved. Few organizations have the foundational models needed to tackle this ambitious challenge. With EXAONE Deep, LG aims to stand shoulder to shoulder with the giants, exhibiting remarkable reasoning abilities.

At the core of EXAONE Deep’s design is the ambition to enhance reasoning capabilities across various domains. The model excels not just in depth but also in breadth, demonstrating a profound understanding of multiple subjects.

And let’s talk numbers. The benchmarks released by LG AI Research are nothing short of impressive:

  • Math Performance: The 32B variant of EXAONE Deep astonishingly outshone a competing model that was 20 times its size in a rigorous mathematics benchmark. Not just that, but the 7.8B and 2.4B variants clinched top spots across major mathematics benchmarks in their respective categories.
  • Science and Coding Brilliance: It didn’t stop there; the EXAONE Deep models (both 7.8B and 2.4B) dominated all critical benchmarks in science and coding, further establishing their prowess.
  • MMLU (Massive Multitask Language Understanding): Impressively, the 32B model scored 83.0 in the MMLU benchmark, laying claim to the top spot among all Korean models.

The international tech community is already buzzing about EXAONE Deep. Soon after its launch, the model was featured in the ‘Notable AI Models’ list by the US-based non-profit organization Epoch AI, positioning LG as a leading entity in the global AI landscape.

Mathematics Mastery

EXAONE Deep’s mathematical reasoning skills are remarkable. During evaluations based on the current academic standards, all three versions (32B, 7.8B, and 2.4B) significantly outperformed their global counterparts.

The 32B model scored an impressive 94.5 on a general math test and rocked a score of 90.0 in the American Invitational Mathematics Examination (AIME) 2024, which is a qualifying exam for the prestigious US Mathematical Olympiad. Even more astonishingly, in the AIME 2025, it squared off against the vast 671B model and matched its performance, showcasing the model's astute ability to learn and reason logically, even in complex scenarios.

Excellence in Science and Coding

Shifting focus to science and coding, EXAONE Deep stunned observers with its high-level capabilities. The 32B model achieved a score of 66.1 on the GPQA Diamond test, which evaluates advanced problem-solving amidst complex scientific disciplines. In the LiveCodeBench evaluation, its coding skills were rated with a score of 59.5 — a solid indicator of potential high-level applications.

Meanwhile, the smaller models did not lag behind either; both the 7.8B and 2.4B variants secured top rankings in their evaluation categories, further cementing EXAONE Deep’s legacy.

Improved General Knowledge

Going beyond specialized tasks, EXAONE Deep also displayed enhanced results in general knowledge assessments. The 32B model's 83.0 score on the MMLU benchmark positions it at the pinnacle of domestic AI models in this comprehensive evaluation — talk about a well-rounded performer!

LG AI Research sees these advancements in reasoning as not just milestones but as steps toward a future where AI can tackle multifaceted issues. This depth of understanding may become instrumental in enriching human lives, combining technology with meaningful applications.

Curious to dig deeper into what AI can achieve? Check out the ongoing AI & Big Data Expo, where industry leaders are discussing the latest in tech trends. This event promises thought-provoking narratives and insights on the future of artificial intelligence and its applications.

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