Deep Reasoning LLM Benchmarking Task

A Deep Reasoning LLM Benchmarking Task is a specialized LLM inference evaluation task designed to assess the advanced reasoning capabilityes of large language models and AI systems through complex, multi-step problem-solving across various domains.

• AKA: Deep Reasoning Benchmarking Task, Advanced Reasoning LLM Evaluation, AI Deep Reasoning Benchmark.
• Context:
  • Task Input: Complex, multi-step reasoning prompts across various domains.
  • Optional Input: Additional context, tools, or prior dialogue history.
  • Task Output: Detailed reasoning process culminating in a final answer.
  • Task Performance Measure/Metrics: Accuracy, reasoning depth, alignment with human judgment.
  • It can take complex prompts or questions, optionally with additional context or tools, and generate detailed, step-by-step reasoning leading to an answer.
  • It can evaluate the output using performance measures such as accuracy, reasoning depth, and alignment with human judgment.
  • It can cover diverse domains including mathematics, science, logic, and real-world problem-solving.
  • It can challenge models to perform tasks requiring abstraction, generalization, and logical deduction beyond surface-level understanding.
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• Example(s):
  • DROP Benchmark, which evaluates DeepSeek-R1 to assess its numerical reasoning capabilities.
  • LongBench v2 Benchmark, which tests LLMs such as OpenAI o1) for their ability to handle long-context reasoning tasks.
  • CriticBench , which evalutes LLMs such as Claude 3.7 Sonnet to assesss their critique and correction reasoning skills.
  • LLM Reasoning Benchmark, which benchmarks a wide range of LLMs on advanced logical, commonsense, and symbolic reasoning tasks using unified prompting strategies and scoring across domains like math, logic, and strategy games.
  • DocPuzzle Benchmark, which assesses a model’s ability to extract, integrate, and reason over unstructured long-form documents, testing real-world document understanding and puzzle-like inference.
  • OlympicArena Becnhmark, which pits top LLMs like GPT-4, Claude, and Gemini against each other in reasoning-based challenges across multiple categories, including analogy, mathematics, and programming, using blind evaluation with expert raters.
  • DNA Bench, which benchmarks whether LLMs can avoid unnecessary reasoning and over-generation.
  • Advanced Reasoning Benchmark (ARB), which assesses higher-order reasoning in domains like law, science, and mathematics.
  • KUMO Benchmark, which generates diverse, unseen reasoning tasks to evaluate generalization capacity.
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• Counter-Example(s):
  • GLUE Benchmarking Task, which focuses on sentence-level classification rather than deep reasoning.
  • SQuAD Benchmarking Task, which evaluates extractive question answering without multi-step reasoning.
  • MT-Bench, which assesses multi-turn dialogue capabilities but not necessarily deep reasoning.
  • ...
• See: LLM Inference Evaluation Task, Deep Reasoning Model, Chain-of-Thought Prompting, Reinforcement Learning, Agentic Reasoning.

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References

2025a

• (Smith et al., 2025) ⇒ Smith, A., et al. (2025). "Optimizing Multimodal Reasoning with Large Language Models". In: _arXiv preprint arXiv:2502.17807_.
  • QUOTE: We introduce a framework for optimizing multimodal reasoning tasks using large language models (LLMs).

    Our approach integrates vision, language, and structured knowledge to address complex reasoning challenges.

    Experimental results demonstrate significant improvements in cross-modal alignment and task performance.

2024a

• (Anon et al., 2024a) ⇒ Anon, et al. (2024). "Advancing Scientific Discovery through AI Reasoning". In: _arXiv preprint arXiv:2412.15204_.
  • QUOTE: This paper explores how advanced AI reasoning systems can accelerate progress in fields such as biology, physics, and materials science.

    Results indicate that combining human insights with AI-driven models yields novel discoveries.

2024b

• (Anon et al., 2024b) ⇒ Anon, et al. (2024). "A Unified Framework for Evaluating AI Reasoning Benchmarks". In: _arXiv preprint arXiv:2406.12753_.
  • QUOTE: We propose a unified framework for evaluating benchmarks designed to test the reasoning capabilities of advanced AI systems.

    The framework includes metrics for assessing logical consistency, factual accuracy, and contextual understanding.

2024d

• (Anon et al., 2024c) ⇒ Anon, et al. (2024). "Improving Numerical Reasoning in Large Language Models". In: _arXiv preprint arXiv:2402.14809_.
  • QUOTE: This study focuses on enhancing the numerical reasoning capabilities of large language models through targeted fine-tuning.

    Results show improved performance on tasks requiring multi-step calculations and quantitative problem-solving.

2024e

• (Confident AI, 2024) ⇒ Confident AI. (2024). "DROP (Discrete Reasoning Over Paragraphs)". In: _Confident AI Documentation_.
  • QUOTE: The DROP benchmark evaluates advanced reasoning capabilities of AI systems through complex question-answering tasks.

    It features over 9500 challenges requiring numerical manipulation, multi-step reasoning, and interpretation of textual data.

2024d

• (Salonen, 2024) ⇒ Salonen, S. (2024). "LLM Reasoning Benchmark". In: _LLM Reasoning Benchmark Website_.
  • QUOTE: The LLM Reasoning Benchmark evaluates the cognitive capability of large language models (LLMs) in solving complex reasoning tasks.

    It includes diverse scenarios to test logical inference, numerical reasoning, and knowledge synthesis.

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