mlsanityData to 7 Oct 2026

EXAONE Deep: Reasoning Enhanced Language Models

arXiv 2503.12524 ↗First posted 16 Mar 2025cs.CL cs.AIcited by 20

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

We present EXAONE Deep series, which exhibits superior capabilities in various reasoning tasks, including math and coding benchmarks. We train our models mainly on the reasoning-specialized dataset that incorporates long streams of thought processes. Evaluation results show that our smaller models, EXAONE Deep 2.4B and 7.8B, outperform other models of comparable size, while the largest model, EXAONE Deep 32B, demonstrates competitive performance against leading open-weight models. All EXAONE Deep models are openly available for research purposes and can be downloaded from https://huggingface.co/LGAI-EXAONE.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 12 of its 12 references that are in the map. First posted on arXiv 16 Mar 2025.

Builds on

12 of 12 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters6 Aug

Other references · 11

  1. 2024DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Modelscited by 8,851
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  3. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  4. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  6. 2023Let's Verify Step by Stepcited by 3,598
  7. 2023GPQA: A Graduate-Level Google-Proof Q&A Benchmarkcited by 2,748
  8. 2024LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Codecited by 1,906
  9. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  10. 2024EXAONE 3.5: Series of Large Language Models for Real-world Use Casescited by 50
  11. 2025SimPER: A Minimalist Approach to Preference Alignment without Hyperparameterscited by 25

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 18; 2026: 2.

20 papers.

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  1. 2025Phi-4-reasoning Technical Reportcited by 146
  2. 2025100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Modelscited by 29
  3. 2025EXAONE 4.0: Unified Large Language Models Integrating Non-reasoning and Reasoning Modescited by 26
  4. 2025Reasoning Path Compression: Compressing Generation Trajectories for Efficient LLM Reasoningcited by 16
  5. 2025Don't Pass@k: A Bayesian Framework for Large Language Model Evaluationcited by 15
  6. 2025Pushing on Multilingual Reasoning Models with Language-Mixed Chain-of-Thoughtcited by 10
  7. 2025AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasonerscited by 6
  8. 2025From KMMLU-Redux to KMMLU-Pro: A Professional Korean Benchmark Suite for LLM Evaluationcited by 6
  9. 2025HyperCLOVA X THINK Technical Reportcited by 5
  10. 2025SafeRBench: Dissecting the Reasoning Safety of Large Language Modelscited by 4
  11. 2025TTT-Bench: A Benchmark for Evaluating Reasoning Ability with Simple and Novel Tic-Tac-Toe-style Gamescited by 3
  12. 2025CTTS: Collective Test-Time Scalingcited by 3
8 more
  1. 2025Best-of-∞ -- Asymptotic Performance of Test-Time LLM Ensemblingcited by 3
  2. 2025Safety-Aligned Weights Are Not Enough: Refusal-Teacher-Guided Finetuning Enhances Safety and Downstream Performance under Harmful Finetuning Attackscited by 2
  3. 2025Apriel-Nemotron-15B-Thinkercited by 1
  4. 2025Future Policy Approximation for Offline Reinforcement Learning in LLM Reasoningcited by 1
  5. 2025KoSimpleQA: A Korean Factuality Benchmark with an Analysis of Reasoning LLMscited by 1
  6. 2025A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancementcited by 0
  7. 2026Thunder-KoNUBench: A Corpus-Aligned Benchmark for Korean Negation Understandingcited by 0
  8. 2026Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training
  2. 2025Encode, Think, Decode: Scaling test-time reasoning with recursive latent thoughts
  3. 2026K-EXAONE Technical Report
  4. 2025Evaluating Mathematical Reasoning Across Large Language Models: A Fine-Grained Approach
  5. 2025EffiReason-Bench: A Unified Benchmark for Evaluating and Advancing Efficient Reasoning in Large Language Models
  6. 2023ARB: Advanced Reasoning Benchmark for Large Language Models
  7. 2025Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisons
  8. 2025Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models