LLM Reasoning Task

An LLM Reasoning Task is a reasoning task that is an AI task that can be solved by large language models through token-based reasoning processes.

• AKA: Language Model Reasoning Task, AI Reasoning Task, Model-Based Reasoning Task, Neural Reasoning Task.
• Context:
  • It can typically require Chain-of-Thought Processing through sequential reasoning steps.
  • It can typically involve Context Integration via attention mechanisms.
  • It can typically utilize Pattern Recognition through learned representations.
  • It can typically support Multi-Step Inference via iterative processing.
  • It can typically enable Knowledge Application through pretrained knowledge.
  • ...
  • It can often employ Self-Consistency Verification for answer validation.
  • It can often implement Reasoning Path Exploration for solution discovery.
  • It can often leverage Prompt Engineering for task formulation.
  • It can often apply Few-Shot Learning for task adaptation.
  • ...
  • It can range from being a Simple LLM Reasoning Task to being a Complex LLM Reasoning Task, depending on its reasoning complexity.
  • It can range from being a Deductive LLM Reasoning Task to being an Inductive LLM Reasoning Task to being an Abductive LLM Reasoning Task, depending on its reasoning pattern.
  • It can range from being a Single-Turn LLM Reasoning Task to being a Multi-Turn LLM Reasoning Task, depending on its interaction structure.
  • It can range from being a Domain-Specific LLM Reasoning Task to being a General LLM Reasoning Task, depending on its application scope.
  • ...
  • It can integrate with GPT-5 Reasoning Models for advanced reasoning.
  • It can connect to Reasoning Effort Parameters for depth control.
  • It can interface with Complex Reasoning Tasks for problem decomposition.
  • It can utilize OpenAI Responses API for task execution.
  • It can leverage Deep Research APIs for comprehensive analysis.
  • ...
• Example(s):
  • Mathematical LLM Reasoning Tasks, such as:
    • Arithmetic LLM Reasoning Task for numerical computation.
    • Algebraic LLM Reasoning Task for equation solving.
    • Geometric LLM Reasoning Task for spatial reasoning.
  • Logical LLM Reasoning Tasks, such as:
    • Syllogistic LLM Reasoning Task for logical inference.
    • Propositional LLM Reasoning Task for truth evaluation.
    • Modal LLM Reasoning Task for possibility assessment.
  • Commonsense LLM Reasoning Tasks, such as:
    • Physical LLM Reasoning Task for world knowledge application.
    • Social LLM Reasoning Task for interpersonal understanding.
    • Temporal LLM Reasoning Task for time-based inference.
  • ...
• Counter-Example(s):
  • Symbolic Reasoning Task, which uses formal logic rather than neural processing.
  • Rule-Based Reasoning Task, which follows explicit rules rather than learned patterns.
  • Human Reasoning Task, which involves biological cognition rather than artificial processing.
• See: Reasoning Task, Complex Reasoning Task, GPT-5 Reasoning Model, Chain-of-Thought Reasoning, Reasoning Effort Parameter, Deep Research API, Problem Solving Task, Natural Language Processing, Transformer Architecture.

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